Table of Contents
Senior Investigators
- Cora Dvorkin
- Jim Halverson
- Taritree Wongjirad
- William Freeman
- Edo Berger
- Matt Schwartz
- Phiala Shanahan
- Philip Harris
- Demba Ba
- Lisa Barsotti
- Isaac Chuang
- William Detmold
- Lina Necib
- Todd Zickler
- Shuchin Aeron
- Pulkit Agrawal
- Carlos Argüelles-Delgado
- Daniel Eisenstein
- Doug Finkbeiner
- Alexander Rakhlin
- Fabian Ruehle
- Tracy Slatyer
- Tess Smidt
- Marin Soljacic
- Max Tegmark
- Ashley Villar
IAIFI Affiliates
- Aram Apyan
- Ning Bao
- George Barbastathis
- Pierre-Hugues Beauchemin
- Jonathan Blazek
- Michael Douglas
- Liang Fu
- Cecilia Garraffo
- Kaiming He
- An Huang
- Tommi Jaakkola
- Erik Katsavounidis
- Rahul Kulkarni
- Sudhir Malik
- Vidya Manian
- Tyler Maunu
- Brent D. Nelson
- Olga Goulko
- Cengiz Pehlevan
- Dan Roberts
- Artan Sheshmani
- Akira Sone
- Christopher Stubbs
- Hidenori Tanaka
- Abiy Tasissa
- Washington Taylor
- Mark Vogelsberger
- Susanne Yelin
Post-Docs and Research Scientists
- Steven Eulig
- Plamen Krastev
- Victor Samuel Pérez Díaz
- Cari Cesarotti
- Blaise Delaney
- Daniel Hackett
- Daichi Hiramatsu
- Samuel Homiller
- Matheus Hostert
- Harsh Kumar
- Yin Lin
- Rashmish Mishra
- Matthew Mould
- Nikhil Mukund
- Andrzej Novak
- Fernando Romero-Lopez
- Christina Reissel
- Matthew Rosenberg
- Carlos Sarasty
- Sokratis Trifinopoulos
- Julian Urban
- Georgios Valogiannis
Students
- Ryan Abbott
- Polina Abratenko
- Jacob Adamczyk
- Aizhan Akhmetzhanova
- Omar Alterkait
- Samuel Alipour-fard
- Oscar Barrera
- Sean Benevedes
- Kiara Carloni
- Chandrika Chandrashekar
- Zhuo Chen
- Ameya Shrikant Daigavane
- Rumen Dangovski
- Aurélien Dersy
- Gokhan Egri
- Atakan Hilmi Firat
- Andre Grossi Fonseca
- Katherine Fraser
- Rikab Gambhir
- Ali Ghorashi
- Mark Hamilton
- Dean Hazineh
- Duc Hoang
- Elyssa Hofgard
- Zhang-Wei Hong
- Maryam Hussaini
- Zev Imani
- Gurtej Kanwar
- Ouail Kitouni
- Samuel Kim
- Mit Kotak
- Jeffrey Krupa
- Zhaoyi Li
- Ziming Liu
- Charlotte Loh
- Andrew Ma
- Silviu-Marian Udrescu
- John Martyn
- Ethan Marx
- Trevor McCourt
- Eric Michaud
- Eric Moreno
- Nayantara Mudur
- Joydeep Naskar
- Aviv Netanyahu
- Tri Nguyen
- Noah Paladino
- Sneh Pandya
- Sangeon Park
- Yidi Qi
- Kate Richardson
- Simon Rothman
- Andrew Saydjari
- Atınç Çağan ŞENGÜL
- Christopher Shallue
- Michael Skuhersky
- Yitian Sun
- Emmanouil Theodosis
- Arthur Tsang
- Alex Wen
- Noah Wolfe
- Felix Yu
- Xiyu Zhai
- Gemma Zhang
- Pavel Zhelnin
- Alexander Zlokapa
- Oreoluwa Alao
- Julia Balla
- Donato Jimenez Beneto
- Elias Benghiat
- Anugrah Chemparathy
- Matthew Chen
- Fiona Daly
- Owen Dugan
- Nino Ephremidze
- Orion Foo
- Varun Hariprasad
- Adriano Hernandez
- Elizabeth Panner
- Marco Pretell
- Marshall Taylor
- Vinh Tran
- Nate Woodward
- Lana Xu
Former Junior Investigators
- Floor Broekgaarden
- Salvatore Cali
- Aidan Chambers
- Mehmet Demirtas
- Mohit Dighamber
- Akshunna S. Dogra
- Arkopal Dutt
- Harold Erbin
- Cristiano Fanelli
- Lena Funcke
- Jasmine Gill
- Sebastian Gomez
- Qi Guo
- Benjamin Harris
- Arthur Hennequin
- Griffin Hosseinzadeh
- Jared Hwang
- Daniel Johnson
- Manami Kanemura
- Gurtej Kanwar
- Patrick Komiske
- Serhii Kryhin
- Jeffrey Lazar
- Keiran Lewellen
- Alexander Lin
- Peter Y. Lu
- Anindita Maiti
- Katie Mason
- Patrick McCormack
- Eric Metodiev
- Joshua Mills
- Niklas Nolte
- Curtis Northcutt
- Bryan Ostdiek
- Dylan Rankin
- Gustavo Salinas
- Nikita Saxena
- Ralitsa Sharankova
- Jonathan P Shoemaker
- Keegan Stoner
- Andrew K. Tan
- Dor Verbin
- Constantin Weisser
- Chris Whittle
- Ray Wynne
- Jun Yin
- Felix Yu
- Mikaeel Yunus
Former MSRP Students
Management
Jesse Thaler
-
[23] Moment Unfolding
Krish Desai, Benjamin Nachman, Jesse Thaler
[ arXiv:2407.11284 ] -
[22] Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics
Jonas Spinner, Victor Bresó, Pim de Haan, Tilman Plehn, Jesse Thaler, Johann Brehmer
[ arXiv:2405.14806 ] -
[21] PAPERCLIP: Associating Astronomical Observations and Natural Language with Multi-Modal Models
Siddharth Mishra-Sharma, Yiding Song, Jesse Thaler
[ arXiv:2403.08851 ] -
[20] Moments of Clarity: Streamlining Latent Spaces in Machine Learning using Moment Pooling
Rikab Gambhir, Athis Osathapan, Jesse Thaler
[ arXiv:2403.08854 ] -
[19] Anomaly Detection in Collider Physics via Factorized Observables
Eric M. Metodiev, Jesse Thaler, Raymond Wynne
[ arXiv:2312.00119 ] -
[18] Safe but Incalculable: Energy-weighting is not all you need
Samuel Bright-Thonney, Benjamin Nachman, Jesse Thaler
[ arXiv:2311.07652 ] -
[17] A Spectral Metric for Collider Geometry
Andrew J. Larkoski, Jesse Thaler
Journal of High Energy Physics 2023, Volume 2023, article number 107 [ arXiv:2305.03751 ] -
[16] Pileup and Infrared Radiation Annihilation (PIRANHA): A Paradigm for Continuous Jet Grooming
Samuel Alipour-Fard, Patrick T. Komiske, Eric M. Metodiev, Jesse Thaler
Journal of High Energy Physics 2023, Volume 2023, Article number 157 [ arXiv:2305.00989 ] -
[15] SHAPER: Can You Hear the Shape of a Jet?
Demba Ba, Akshunna S. Dogra, Rikab Gambhir, Abiy Tasissa, Jesse Thaler
Journal of High Energy Physics, 2023, Volume 2023, Article 195 [ arXiv:2302.12266 ] -
[14] EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets
Erik Buhmann, Gregor Kasieczka, Jesse Thaler
SciPost Physics, 2023, Volume 15, Issue 4 [ arXiv:2301.08128 ] -
[13] Comparing Point Cloud Strategies for Collider Event Classification
Peter Onyisi, Delon Shen, Jesse Thaler
Physical Review D, 2023, Volume 108, Issue 1 [ arXiv:2212.10659 ] -
[12] Degeneracy Engineering for Classical and Quantum Annealing: A Case Study of Sparse Linear Regression in Collider Physics
Eric R. Anschuetz, Lena Funcke, Patrick T. Komiske, Serhii Kryhin, Jesse Thaler
Physical Review D, Volume 106, Article 056008 [ arXiv:2205.10375 ] -
[11] Power Counting Energy Flow Polynomials
Pedro Cal, Jesse Thaler, Wouter J. Waalewijn
Journal of High Energy Physics, 2022, Article 21 [ arXiv:2205.06818 ] -
[10] Bias and Priors in Machine Learning Calibrations for High Energy Physics
Rikab Gambhir, Benjamin Nachman, Jesse Thaler
Physical Review D, Volume 106, Article 036011 [ arXiv:2205.05084 ] -
[9] Disentangling Quarks and Gluons with CMS Open Data
Patrick T. Komiske, Serhii Kryhin, Jesse Thaler
Physical Review D, 2022, Volume 106 Article 094021 [ arXiv:2205.04459 ] -
[8] Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics
Rikab Gambhir, Benjamin Nachman, Jesse Thaler
Physical Review Letters, 2022, Volume 129, Article 082001 [ arXiv:2205.03413 ] -
[7] SymmetryGAN: Symmetry Discovery with Deep Learning
Krish Desai, Benjamin Nachman, Jesse Thaler
Physical. Rev. D, 2022, 105:096031 [ arXiv:2112.05722 ] -
[6] Presenting Unbinned Differential Cross Section Results
Miguel Arratia, Anja Butter, Mario Campanelli, Vincent Croft, Aishik Ghosh, Dag Gillberg, Kristin Lohwasser, Bogdan Malaescu, Vinicius Mikuni, Benjamin Nachman, Juan Rojo, Jesse Thaler, Ramon Winterhalder
Journal of Instrumentation, 2022, Volume 17 [ arXiv:2109.13243 ] -
[5] Neural Conditional Reweighting
Benjamin Nachman, Jesse Thaler
Physical Review D, Volume 105, Article 076015 [ arXiv:2107.08979 ] -
[4] Scaffolding Simulations with Deep Learning for High-dimensional Deconvolution
Anders Andreassen, Patrick T. Komiske, Eric M. Metodiev, Benjamin Nachman, Adi Suresh, and Jesse Thaler
Workshop paper at ICLR 2021 SimDL Workshop [ arXiv:2105.04448 ] -
[3] The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics
Gregor Kasieczka (ed), Benjamin Nachman (ed), David Shih (ed), Oz Amram, Anders Andreassen, Kees Benkendorfer, Blaz Bortolato, Gustaaf Brooijmans, Florencia Canelli, Jack H. Collins, Biwei Dai, Felipe F. De Freitas, Barry M. Dillon, Ioan-Mihail Dinu, Zhongtian Dong, Julien Donini, Javier Duarte, D. A. Faroughy, Julia Gonski, Philip Harris, Alan Kahn, Jernej F. Kamenik, Charanjit K. Khosa, Patrick Komiske, Luc Le Pottier, Pablo Martín-Ramiro, Andrej Matevc, Eric Metodiev, Vinicius Mikuni, Inês Ochoa, Sang Eon Park, Maurizio Pierini, Dylan Rankin, Veronica Sanz, Nilai Sarda, Urous Seljak, Aleks Smolkovic, George Stein, Cristina Mantilla Suarez, Manuel Szewc, Jesse Thaler, Steven Tsan, Silviu-Marian Udrescu, Louis Vaslin, Jean-Roch Vlimant, Daniel Williams, Mikaeel Yunus
Reports on Progress in Physics, 2021, Volume 84, Number 12 [ arXiv:2101.08320 ] -
[2] E Pluribus Unum Ex Machina: Learning from Many Collider Events at Once
Benjamin Nachman and Jesse Thaler
Physical Review D, 2021, Vol. 103, Issue 11, Article 116013 [ arXiv:2101.07263 | code ] -
[1] Mapping Machine-Learned Physics into a Human-Readable Space
Taylor Faucett, Jesse Thaler, Daniel Whiteson
Physics Review D, 2021, Volume 103, Iss. 3 [ arXiv:2010.11998 ]
Mike Williams
-
[9] From Neurons to Neutrons: A Case Study in Interpretability
Ouail Kitouni, Niklas Nolte, Víctor Samuel Pérez-Díaz, Sokratis Trifinopoulos, Mike Williams
[ arXiv:2405.17425 ] -
[8] Applications of Lipschitz neural networks to the Run 3 LHCb trigger system
Blaise Delaney, Nicole Schulte, Gregory Ciezarek, Niklas Nolte, Mike Williams, Johannes Albrecht
[ arXiv:2312.14265 ] -
[7] NuCLR, Nuclear Co-Learned Representations
Ouail Kitouni, Niklas Nolte, Sokratis Trifinopoulos, Subhash Kantamneni, Mike Williams
[ arXiv:2306.06099 ] -
[6] Development of the Topological Trigger for LHCb Run 3
Nicole Schulte, Blaise Raheem Delaney, Niklas Nolte, Gregory Max Ciezarek, Johannes Albrecht, Mike Williams
[ arXiv:2306.09873 ] -
[5] Expressive Monotonic Neural Networks
Niklas Nolte, Ouail Kitouni, Mike Williams
International Conference on Learning Representations 2023 [ ] -
[4] Finding NEEMo: Geometric Fitting using Neural Estimation of the Energy Mover’s Distance
Ouail Kitouni, Niklas Nolte, Mike Williams
[ arXiv:2209.15624 ] -
[3] Towards Understanding Grokking: An Effective Theory of Representation Learning
Ziming Liu, Ouail Kitouni, Niklas Nolte, Eric J. Michaud, Max Tegmark, Mike Williams
[ arXiv:2205.10343 ] -
[2] Robust and Provably Motonic Networks
Ouail Kitouni, Niklas Nolte, Mike Williams
Fourth Workshop on Machine Learning and the Physical Sciences (NeurIPS 2021) Proceedings, [ arXiv:2112.00038 ] -
[1] Enhancing searches for resonances with machine learning and moment decomposition
Ouail Kitouni, Benjamin Nachman, Constantin Weisser, and Mike Williams
Journal of High Energy Physics, 2021, Article 70 [ arXiv:2010.09745 | code ]
Marisa LaFleur
Thomas Bradford
Maria Figueiredo
Senior Investigators
Cora Dvorkin
-
[9] Substructure Detection in Realistic Strong Lensing Systems with Machine LearningSubstructure Detection in Realistic Strong Lensing Systems with Machine Learning
Arthur Tsang, Atınç Çağan Şengül, Cora Dvorkin
[ arXiv:2401.16624 ] -
[8] Precise Cosmological Constraints from BOSS Galaxy Clustering with a Simulation-Based Emulator of the Wavelet Scattering Transform
Georgios Valogiannis, Sihan Yuan, Cora Dvorkin
Physical Review D 2024, Volume 109, Issue 10 [ arXiv:2310.16116 ] -
[7] Subhalo effective density slope measurements from HST strong lensing data with neural likelihood-ratio estimation
Gemma Zhang, Atınç Çağan Şengül, Cora Dvorkin
Monthly Notices of the Royal Astronomical Society, 2024, Volume 527, Issue 2 [ arXiv:2308.09739 ] -
[6] Data Compression and Inference in Cosmology with Self-Supervised Machine Learning
Aizhan Akhmetzhanova, Siddharth Mishra-Sharma, Cora Dvorkin
Monthly Notices of the Royal Astronomical Society, 2023, Volume 527, Issue 3 [ arXiv:2308.09751 ] -
[5] Inferring subhalo effective density slopes from strong lensing observations with neural likelihood-ratio estimation
Gemma Zhang, Siddharth Mishra-Sharma, Cora Dvorkin
Monthly Notices of the Royal Astronomical Society, 2022, Volume 517, Issue 3 [ arXiv:2208.13796 ] -
[4] Going Beyond the Galaxy Power Spectrum: an Analysis of BOSS Data with Wavelet Scattering Transforms
Georgios Valogiannis, Cora Dvorkin
Physical Review D, 2022, Volume 106, Article 103509 [ arXiv:2204.13717 ] -
[3] Substructure Detection Reanalyzed: Dark Perturber shown to be a Line-of-Sight Halo
Atınç Çağan Şengül, Cora Dvorkin, Bryan Ostdiek, Arthur Tsang
Monthly Notices of the Royal Astronomical Society, 2022, Volume 515, Issue 3, Pages 4391–4401 [ arXiv:2112.00749 ] -
[2] New limits on the light dark matter: proton cross section from the cosmic large-scale structure
Keir K. Rogers, Cora Dvorkin, Hiranya V. Peiris
Physical Review Letters, 2022, Volume 128, Article 171301 [ arXiv:2111.10386 ] -
[1] Towards an Optimal Estimation of Cosmological Parameters with the Wavelet Scattering Transform
Georgios Valogiannis, Cora Dvorkin
Physical Review D, 2022, 105, 103534 [ arXiv:2108.07821 ]
Jim Halverson
-
[11] TASI Lectures on Physics for Machine Learning
Jim Halverson
[ arXiv:2408.00082 ] -
[10] KAN: Kolmogorov-Arnold Networks
Ziming Liu, Yixuan Wang, Sachin Vaidya, Fabian Ruehle, James Halverson, Marin Soljačić, Thomas Y. Hou, Max Tegmark
[ arXiv:2404.19756 ] -
[9] Rigor with Machine Learning from Field Theory to the Poincaré Conjecture
Sergei Gukov, James Halverson, Fabian Ruehle
Nature Reviews Physics 2024 [ arXiv:2402.13321 ] -
[8] Metric Flows with Neural Networks
James Halverson, Fabian Ruehle
[ arXiv:2310.19870 ] -
[7] Neural Network Field Theories: Non-Gaussianity, Actions, and Locality
Mehmet Demirtas, James Halverson, Anindita Maiti, Matthew D. Schwartz, Keegan Stoner
[ arXiv:2307.03223 ] -
[6] Searching for ribbons with machine learning
Sergei Gukov, James Halverson, Ciprian Manolescu, Fabian Ruehle
[ arXiv:2304.09304 ] -
[5] Electric-Magnetic Duality in a Class of G2-Compactifications of M-theory
James Halverson, Benjamin Sung, Jiahua Tian
Journal of High Energy Physics, 2023, Volume 2023, Article 89 [ arXiv:2210.08628 ] -
[4] Building Quantum Field Theories Out of Neurons
James Halverson
[ arXiv:2112.04527 ] -
[3] Infinite Neural Network Quantum States
Di Luo, James Halverson
Machine Learning: Science and Technology, 2023, Volume 4, Number 2 [ arXiv:2112.00723 ] -
[2] Symmetry-via-Duality: Invariant Neural Network Densities from Parameter-Space Correlators
Anindita Maiti, Keegan Stoner, James Halverson
[ arXiv:2106.00694 ] -
[1] Learning to Unknot
Sergei Gukov, James Halverson, Fabian Ruehle, and Piotr Sułkowski
Machine Learning - Science and Technology, 2021, Volume 2, Number 2, Article 025035 [ arXiv:2010.16263 ]
Taritree Wongjirad
-
[3] Towards Universal Unfolding of Detector Effects in High-Energy Physics using Denoising Diffusion Probabilistic Models
Camila Pazos, Shuchin Aeron, Pierre-Hugues Beauchemin, Vincent Croft, Martin Klassen, Taritree Wongjirad
[ arXiv:2406.01507 ] -
[2] Score-based Diffusion Models for Generating Liquid Argon Time Projection Chamber Images
Zeviel Imani, Shuchin Aeron, Taritree Wongjirad
[ arXiv:2307.13687 ] -
[1] Towards Designing and Exploiting Generative Networks for Neutrino Physics Experiments using Liquid Argon Time Projection Chambers
Paul Lutkus, Taritree Wongjirad, Schuchin Aeron
Conference paper at ICLR 2021 [ | code ]
William Freeman
- [1] QuanTaichi: A Compiler for Quantized Simulations
Yuanming Hu, Jiafeng Liu, Xuanda Yang, Mingkuan Xu, Ye Kuang, Weiwei Xu, Qiang Dai, William Freeman, Fredo Durand
ACM Transactions on Graphics, Volume 4, Article 182 [ ]
Edo Berger
-
[15] Superphot+: Realtime Fitting and Classification of Supernova Light Curves
Kaylee M. de Soto (1), Ashley Villar (1), Edo Berger (1 and 2), Sebastian Gomez (3), Griffin Hosseinzadeh (4), Doug Branton (5), Sandro Campos (6), Melissa DeLucchi (6), Jeremy Kubica (6), Olivia Lynn (6), Konstantin Malanchev (6), Alex I. Malz (6) ((1) Center for Astrophysics | Harvard & Smithsonian, (2) The NSF AI Institute for Artificial Intelligence and Fundamental Interactions, (3) Space Telescope Science Institute, (4) Steward Observatory | University of Arizona, (5) DiRAC Institute and the Department of Astronomy | University of Washington, (6) McWilliams Center for Cosmology | Department of Physics at Carnegie Mellon University)
[ arXiv:2403.07975 ] -
[14] An Extensive Hubble Space Telescope Study of the Offset and Host Light Distributions of Type I Superluminous Supernovae
Brian Hsu, Peter K. Blanchard, Edo Berger, Sebastian Gomez
The Astrophysical Journal 2024, Volume 961, Number 2 [ arXiv:2308.07271 ] -
[13] From Discovery to the First Month of the Type II Supernova 2023ixf: High and Variable Mass Loss in the Final Year before Explosion
Daichi Hiramatsu, Daichi Tsuna, Edo Berger, Koichi Itagaki, Jared A. Goldberg, Sebastian Gomez, Kishalay De, Griffin Hosseinzadeh, K. Azalee Bostroem, Peter J. Brown, Iair Arcavi, Allyson Bieryla, Peter K. Blanchard, Gilbert A. Esquerdo, Joseph Farah, D. Andrew Howell, Tatsuya Matsumoto, Curtis McCully, Megan Newsome, Estefania Padilla Gonzalez, Craig Pellegrino, Jaehyon Rhee, Giacomo Terreran, József Vinkó, J. Craig Wheeler
The Astrophysical Journal Letters 2023, Volume 955, Number 1 [ arXiv:2307.03165 ] -
[12] Multiple Peaks and a Long Precursor in the Type IIn Supernova 2021qqp: An Energetic Explosion in a Complex Circumstellar Environment
Daichi Hiramatsu, Tatsuya Matsumoto, Edo Berger, Conor Ransome, V. Ashley Villar, Sebastian Gomez, Yvette Cendes, Kishalay De, K. Azalee Bostroem, Joseph Farah, D. Andrew Howell, Curtis McCully, Megan Newsome, Estefania Padilla Gonzalez, Craig Pellegrino, Akihiro Suzuki, Giacomo Terreran
The Astrophysical Journal, 2024, Volume 964, Number 2 [ arXiv:2305.11168 ] -
[11] Limits on Simultaneous and Delayed Optical Emission from Well-localized Fast Radio Bursts
Daichi Hiramatsu, Edo Berger, Brian D. Metzger, Sebastian Gomez, Allyson Bieryla, Iair Arcavi, D. Andrew Howell, Ryan Mckinven, Nozomu Tominaga
The Astrophysical Journal Letters 2023, volume 947, number 2 [ arXiv:2211.03974 ] -
[10] Deep Learning Detection and Classification of Gravitational Waves from Neutron Star-Black Hole Mergers
Richard Qiu, Plamen Krastev, Kiranjyot Gill, Edo Berger
Physics Letters B, 2023, Volume 840 [ arXiv:2210.15888 ] -
[9] Identifying Tidal Disruption Events with an Expansion of the FLEET Machine Learning Algorithm
Sebastian Gomez, V. Ashley Villar, Edo Berger, Suvi Gezari, Sjoert van Velzen, Matt Nicholl, Peter K. Blanchard, Kate. D. Alexander
The Astrophysical Journal, 2023, Volume 949, Issue 113 [ arXiv:2210.10810 ] -
[8] The First Two Years of FLEET: an Active Search for Superluminous Supernovae
Sebastian Gomez, Edo Berger, Peter K. Blanchard, Griffin Hosseinzadeh, Matt Nicholl, Daichi Hiramatsu, V. Ashley Villar, Yao Yin
The Astrophysical Journal, 2023, Volume 949, Issue 114 [ arXiv:2210.10811 ] -
[7] Photometrically-Classified Superluminous Supernovae from the Pan-STARRS1 Medium Deep Survey: A Case Study for Science with Machine Learning-Based Classification
Brian Hsu, Griffin Hosseinzadeh, V. Ashley Villar, Edo Berger
The Astrophysical Journal, 2022, Volume 937, Number 1 [ arXiv:2204.09809 ] -
[6] Luminous Supernovae: Unveiling a Population Between Superluminous and Normal Core-collapse Supernovae
Sebastian Gomez, Edo Berger, Matt Nicholl, Peter K. Blanchard, Griffin Hosseinzadeh
The Astrophysical Journal, 2022, Volume 941, Number 2 [ arXiv:2204.08486 ] -
[5] Constraining the Time of Gravitational Wave Emission from Core-Collapse Supernovae
Kiranjyot Gill, Griffin Hosseinzadeh, Edo Berger, Michele Zanolin, Marek Szczepanczyk
The Astrophysical Journal, 2022, Volume 931, Number 2 [ arXiv:2201.03609 ] -
[4] Impact of Massive Binary Star and Cosmic Evolution on Gravitational Wave Observations II: Double Compact Object Rates and Properties
Floor S. Broekgaarden, Edo Berger, Simon Stevenson, Stephen Justham, Ilya Mandel, Martyna Churślińska, Like A. C. van Son, Tom Wagg, Alejandro Vigna-Gómez, Selma E. De Mink, Debatri Chattopadhyay, Coenraad J. Neijssel
Monthly Notices of the Royal Astronomical Society, 2022, Volume 516, Issue 4, Pages 5737–5761 [ arXiv:2112.05763 ] -
[3] A Deep-learning Approach for Live Anomaly Detection of Extragalactic Transients
Ashley Villar, Miles Cranmer, Edo Berger, Gabriella Contardo, Shirley Ho, Griffin Hosseinzadeh, Joshua Yao-Yu Lin
The Astrophysical Journal Supplement Series, Volume 255 [ ] -
[2] The Luminous and Double-Peaked Type Ic Supernova 2019stc: Evidence for Multiple Energy Sources
Sebastian Gomez, Edo Berger, Griffin Hosseinzadeh, Peter K. Blanchard, Matt Nicholl, V. Ashley Villar
The Astrophysical Journal, 2021, Vol. 913, Article 143 [ arXiv:2103.02611 ] -
[1] Detection and Parameter Estimation of Gravitational Waves from Binary Neutron-Star Mergers in Real LIGO Data using Deep Learning
Plamen G. Krastev, Kiranjyot Gill, V. Ashley Villar, Edo Berger
Physics Letters B, 2021, Vol. 815, Article 136161 [ arXiv:2012.13101 ]
Matt Schwartz
-
[6] Reconstructing S-matrix Phases with Machine Learning
Aurélien Dersy, Matthew D. Schwartz, Alexander Zhiboedov
[ arXiv:2308.09451 ] -
[5] Neural Network Field Theories: Non-Gaussianity, Actions, and Locality
Mehmet Demirtas, James Halverson, Anindita Maiti, Matthew D. Schwartz, Keegan Stoner
[ arXiv:2307.03223 ] -
[4] Simplifying Polylogarithms with Machine Learning
Aurélien Dersy, Matthew D. Schwartz, Xiaoyuan Zhang
[ arXiv:2206.04115 ] -
[3] Challenges for Unsupervised Anomaly Detection in Particle Physics
Katherine Fraser, Samuel Homiller, Rashmish K. Mishra, Bryan Ostdiek, Matthew D. Schwartz
Journal of High Energy Physics, 2022, Article 66 [ arXiv:2110.06948 ] -
[2] Modern Machine Learning and Particle Physics
Matthew D. Schwartz
Harvard Data Science Review, 2021, Issue 3.2, 13 May [ arXiv:2103.12226 ] -
[1] Parameter Inference from Event Ensembles and the Top-Quark Mass
Forrest Flesher, Katherine Fraser, Charles Hutchison, Bryan Ostdiek, Matthew D. Schwartz
Journal of High Energy Physics, 2021, Article 58 [ arXiv:2011.04666 ]
Phiala Shanahan
-
[17] QCD constraints on isospin-dense matter and the nuclear equation of state
Ryan Abbott, William Detmold, Marc Illa, Assumpta Parreño, Robert J. Perry, Fernando Romero-López, Phiala E. Shanahan, Michael L. Wagman
[ arXiv:2406.09273 ] -
[16] Practical applications of machine-learned flows on gauge fields
Ryan Abbott, Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2404.11674 ] -
[15] Multiscale Normalizing Flows for Gauge Theories
Ryan Abbott, Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2404.10819 ] -
[14] Real-time Dynamics of the Schwinger Model as an Open Quantum System with Neural Density Operators
Joshua Lin, Di Luo, Xiaojun Yao, Phiala E. Shanahan
[ arXiv:2402.06607 ] -
[13] Applications of flow models to the generation of correlated lattice QCD ensembles
Ryan Abbott, Aleksandar Botev, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2401.10874 ] -
[12] Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics
Kyle Cranmer, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Phiala E. Shanahan
Nature Reviews Physics, 2023, Volume 5 [ arXiv:2309.01156 ] -
[11] Signal-to-noise improvement through neural network contour deformations for 3D SU(2) lattice gauge theory
William Detmold, Gurtej Kanwar, Yin Lin, Phiala E. Shanahan, Michael L. Wagman
[ arXiv:2309.00600 ] -
[10] Constraint of pionless EFT using two-nucleon spectra from lattice QCD
William Detmold, Fernando Romero-López, Phiala E. Shanahan
[ arXiv:2305.06313 ] -
[9] Normalizing flows for lattice gauge theory in arbitrary space-time dimension
Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Alexander G.D.G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2305.02402 ] -
[8] Aspects of scaling and scalability for flow-based sampling of lattice QCD
Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Alexander G. D. G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
The European Physical Journal A 2023, Volume 59, Article Number 257 [ arXiv:2211.07541 ] -
[7] Sampling QCD field configurations with gauge-equivariant flow models
Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Alexander G. D. G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2208.03832 ] -
[6] Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions
Ryan Abbott, Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Betsy Tian, Julian M. Urban
Physical REview D, 2022, Volume 106, Issue 7 [ arXiv:2207.08945 ] -
[5] Flow-based sampling in the lattice Schwinger model at criticality
Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
Physical Review D, 2022, Volume 106, Article 014514 [ arXiv:2202.11712 ] -
[4] Finite-Volume Pionless Effective Field Theory for Few-Nucleon Systems with Differentiable Programming
Xiangkai Sun, William Detmold, Di Luo, Phiala E. Shanahan
[ arXiv:2202.03530 ] -
[3] Flow-based sampling for multimodal distributions in lattice field theory
Daniel C. Hackett, Chung-Chun Hsieh, Michael S. Albergo, Denis Boyda, Jiunn-Wei Chen, Kai-Feng Chen, Kyle Cranmer, Gurtej Kanwar, Phiala E. Shanahan
[ arXiv:2107.00734 ] -
[2] Flow-based sampling for fermionic lattice field theories
Michael S. Albergo, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Julian M. Urban, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Phiala E. Shanahan
Physical Review D, 2021, Vol. 104, Iss. 11 – 1 [ arXiv:2106.05934 ] -
[1] Introduction to Normalizing Flows for Lattice Field Theory
Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Kyle Cranmer, Sébastien Racanière, Danilo Jimenez Rezende, and Phiala E. Shanahan
[ arXiv:2101.08176 ]
Philip Harris
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[10] Re-Simulation-based Self-Supervised Learning for Pre-Training Foundation Models
Philip Harris, Michael Kagan, Jeffrey Krupa, Benedikt Maier, Nathaniel Woodward
[ arXiv:2403.07066 ] -
[9] Chained Quantile Morphing with Normalizing Flows
Samuel Bright-Thonney, Philip Harris, Patrick McCormack, Simon Rothman
[ arXiv:2309.15912 ] -
[8] Symbolic Regression on FPGAs for Fast Machine Learning Inference
Ho Fung Tsoi, Adrian Alan Pol, Vladimir Loncar, Ekaterina Govorkova, Miles Cranmer, Sridhara Dasu, Peter Elmer, Philip Harris, Isobel Ojalvo, Maurizio Pierini
EPJ Web of Conferences 2024, Volume 295 [ arXiv:2305.04099 ] -
[7] Neural Embedding: Learning the Embedding of the Manifold of Physics Data
Sang Eon Park, Philip Harris, Bryan Ostdiek
Journal of High Energy Physics, 2023, Volume 2023, Article 108 [ arXiv:2208.05484 ] -
[6] Hardware-accelerated Inference for Real-Time Gravitational-Wave Astronomy
Alec Gunny, Dylan Rankin, Jeffrey Krupa, Muhammed Saleem, Tri Nguyen, Michael Coughlin, Philip Harris, Erik Katsavounidis, Steven Timm, Burt Holzman
[ arXiv:2108.12430 ] -
[5] A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC
Giuseppe Di Guglielmo, Farah Fahim, Christian Herwig, Manuel Blanco Valentin, Javier Duarte, Cristian Gingu, Philip Harris, James Hirschauer, Martin Kwok, Vladimir Loncar, Yingyi Luo, Llovizna Miranda, Jennifer Ngadiuba, Daniel Noonan, Seda Ogrenci-Memik, Maurizio Pierini, Sioni Summers, Nhan Tran
IEEE Transactions on Nuclear Science, 2021, Vol. 68, Issue 8 [ arXiv:2105.01683 ] -
[4] hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices
Farah Fahim, Benjamin Hawks, Christian Herwig, James Hirschauer, Sergo Jindariani, Nhan Tran, Luca P. Carloni, Giuseppe Di Guglielmo, Philip Harris, Jeffrey Krupa, Dylan Rankin, Manuel Blanco Valentin, Josiah Hester, Yingyi Luo, John Mamish, Seda Orgrenci-Memik, Thea Aarrestad, Hamza Javed, Vladimir Loncar, Maurizio Pierini, Adrian Alan Pol, Sioni Summers, Javier Duarte, Scott Hauck, Shih-Chieh Hsu, Jennifer Ngadiuba, Mia Liu, Duc Hoang, Edward Kreinar, Zhenbin Wu
[ arXiv:2103.05579 ] -
[3] The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics
Gregor Kasieczka (ed), Benjamin Nachman (ed), David Shih (ed), Oz Amram, Anders Andreassen, Kees Benkendorfer, Blaz Bortolato, Gustaaf Brooijmans, Florencia Canelli, Jack H. Collins, Biwei Dai, Felipe F. De Freitas, Barry M. Dillon, Ioan-Mihail Dinu, Zhongtian Dong, Julien Donini, Javier Duarte, D. A. Faroughy, Julia Gonski, Philip Harris, Alan Kahn, Jernej F. Kamenik, Charanjit K. Khosa, Patrick Komiske, Luc Le Pottier, Pablo Martín-Ramiro, Andrej Matevc, Eric Metodiev, Vinicius Mikuni, Inês Ochoa, Sang Eon Park, Maurizio Pierini, Dylan Rankin, Veronica Sanz, Nilai Sarda, Urous Seljak, Aleks Smolkovic, George Stein, Cristina Mantilla Suarez, Manuel Szewc, Jesse Thaler, Steven Tsan, Silviu-Marian Udrescu, Louis Vaslin, Jean-Roch Vlimant, Daniel Williams, Mikaeel Yunus
Reports on Progress in Physics, 2021, Volume 84, Number 12 [ arXiv:2101.08320 ] -
[2] Fast convolutional neural networks on FPGAs with hls4ml
Thea Aarrestad, Vladimir Loncar, Nicolò Ghielmetti, Maurizio Pierini, Sioni Summers, Jennifer Ngadiuba, Christoffer Petersson, Hampus Linander, Yutaro Iiyama, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Dylan Rankin, Sergo Jindariani, Kevin Pedro, Nhan Tran, Mia Liu, Edward Kreinar, Zhenbin Wu, Duc Hoang
Machine Learning Science and Technology, 2021, Volume 2, Issue 4, Article 045015 [ arXiv:2101.05108 ] -
[1] Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge
Sang Eon Park, Dylan Rankin, Silviu-Marian Udrescu, Mikaeel Yunus, Philip Harris
Journal of High Energy Physics, 2021, Article 30 [ arXiv:2011.03550 | code ]
Demba Ba
-
[6] Learning Linear Groups in Neural Networks
Emmanouil Theodosis, Karim Helwani, Demba Ba
[ arXiv:2305.18552 ] -
[5] SHAPER: Can You Hear the Shape of a Jet?
Demba Ba, Akshunna S. Dogra, Rikab Gambhir, Abiy Tasissa, Jesse Thaler
Journal of High Energy Physics, 2023, Volume 2023, Article 195 [ arXiv:2302.12266 ] -
[4] Learning Silhouettes with Group Sparse Autoencoders
Emmanouil Theodosis and Demba Ba
Harvard CRISP Preprint [ ] -
[3] Mixture Model Auto-Encoders: Deep Clustering through Dictionary Learning
Alexander Lin, Andrew H. Song, Demba Ba
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, pp. 3368-3372 [ arXiv:2110.04683 ] -
[2] Covariance-Free Sparse Bayesian Learning
Alexander Lin, Andrew H. Song, Berkin Bilgic, Demba Ba
IEEE Transactions on Signal Processing, volume 70 [ arXiv:2105.10439 ] -
[1] On the convergence of group-sparse autoencoders
Emmanouil Theodosis, Bahareh Tolooshams, Pranay Tankala, Abiy Tasissa, Demba Ba
[ arXiv:2102.07003 ]
Lisa Barsotti
- [1] Machine Learning for Quantum-Enhanced Gravitational-Wave Observatories
Chris Whittle, Ge Yang, Matthew Evans, Lisa Barsotti
Physical Review D, Volume 108, Article 043034 [ arXiv:2305.13780 ]
Isaac Chuang
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[3] Noisy dynamical systems evolve error correcting codes and modularity
Trevor McCourt, Ila R. Fiete, Isaac L. Chuang
[ arXiv:2303.14448 ] -
[2] Pareto-optimal clustering with the primal deterministic information bottleneck
Andrew K. Tan, Max Tegmark, Isaac L. Chuang
Entropy, 2022, 24(6) [ arXiv:2204.02489 ] -
[1] Biological error correction codes generate fault-tolerant neural networks
Alexander Zlokapa, Andrew K. Tan, John M. Martyn, Max Tegmark, Isaac L. Chuang
[ arXiv:2202.12887 ]
William Detmold
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[8] QCD constraints on isospin-dense matter and the nuclear equation of state
Ryan Abbott, William Detmold, Marc Illa, Assumpta Parreño, Robert J. Perry, Fernando Romero-López, Phiala E. Shanahan, Michael L. Wagman
[ arXiv:2406.09273 ] -
[7] Signal-to-noise improvement through neural network contour deformations for 3D SU(2) lattice gauge theory
William Detmold, Gurtej Kanwar, Yin Lin, Phiala E. Shanahan, Michael L. Wagman
[ arXiv:2309.00600 ] -
[6] Constraint of pionless EFT using two-nucleon spectra from lattice QCD
William Detmold, Fernando Romero-López, Phiala E. Shanahan
[ arXiv:2305.06313 ] -
[5] Correlation function distributions for O(N) lattice field theories in the disordered phase
Cagin Yunus, William Detmold
[ arXiv:2304.03820 ] -
[4] Large-time correlation functions in bosonic lattice field theories
Cagin Yunus, William Detmold
Physics Letter B, 2023, Volume 840, 137890 [ arXiv:2210.15789 ] -
[3] Infinite Variance in Monte Carlo Sampling of Lattice Field Theories
Cagin Yunus, William Detmold
Physical Review D, Volume 106, Article 094506 [ arXiv:2205.01001 ] -
[2] Finite-Volume Pionless Effective Field Theory for Few-Nucleon Systems with Differentiable Programming
Xiangkai Sun, William Detmold, Di Luo, Phiala E. Shanahan
[ arXiv:2202.03530 ] -
[1] Path integral contour deformations for observables in SU(N) gauge theory
William Detmold, Gurtej Kanwar, Henry Lamm, Michael L. Wagman, Neill C. Warrington
Physical Review D, 2021, Vol. 103, Issue 9, Article 094517 [ arXiv:2101.12668 ]
Lina Necib
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[4] The dark matter profile of the Milky Way inferred from its circular velocity curve
Xiaowei Ou, Anna-Christina Eilers, Lina Necib, Anna Frebel
[ arXiv:2303.12838 ] -
[3] Via Machinae 2.0: Full-Sky, Model-Agnostic Search for Stellar Streams in Gaia DR2
David Shih, Matthew R. Buckley, Lina Necib
[ arXiv:2303.01529 ] -
[2] Uncovering dark matter density profiles in dwarf galaxies with graph neural networks
Tri Nguyễn, Siddharth Mishra-Sharma, Reuel Williams, Lina Necib
Physical Review D, 202, Volume 107, Issue 4 [ arXiv:2208.12825 ] -
[1] Robust Clustering of the Local Milky Way Stellar Kinematic Substructures with Gaia eDR3
Xiaowei Ou, Lina Necib, Anna Frebel
Royal Astronomical Society, 2023, Volume 521, Issue 2 [ arXiv:2208.01056 ]
Todd Zickler
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[4] Multistable Shape from Shading Emerges from Patch Diffusion
Xinran Nicole Han, Todd Zickler, Ko Nishino
[ arXiv:2405.14530 ] -
[3] Polarization Multi-Image Synthesis with Birefringent Metasurfaces
Dean Hazineh, Soon Wei Daniel Lim, Qi Guo, Federico Capasso, Todd Zickler
[ arXiv:2307.08106 ] -
[2] Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields
Dor Verbin, Peter Hedman, Ben Mildenhall, Todd Zickler, Jonathan T. Barron, Pratul P. Srinivasan
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition [ arXiv:2112.03907 ] -
[1] Field of Junctions: Extracting Boundary Structure at Low SNR
Dor Verbin, Todd Zickler
IEEE/CVF International Conference on Computer Vision, 2021 [ arXiv:2011.13866 ]
Shuchin Aeron
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[2] Towards Universal Unfolding of Detector Effects in High-Energy Physics using Denoising Diffusion Probabilistic Models
Camila Pazos, Shuchin Aeron, Pierre-Hugues Beauchemin, Vincent Croft, Martin Klassen, Taritree Wongjirad
[ arXiv:2406.01507 ] -
[1] Score-based Diffusion Models for Generating Liquid Argon Time Projection Chamber Images
Zeviel Imani, Shuchin Aeron, Taritree Wongjirad
[ arXiv:2307.13687 ]
Pulkit Agrawal
-
[12] Learning Force Control for Legged Manipulation
Tifanny Portela, Gabriel B. Margolis, Yandong Ji, Pulkit Agrawal
[ arXiv:2405.01402 ] -
[11] Learning to See Physical Properties with Active Sensing Motor Policies
Gabriel B. Margolis, Xiang Fu, Yandong Ji, Pulkit Agrawal
[ arXiv:2311.01405 ] -
[10] DribbleBot: Dynamic Legged Manipulation in the Wild
Yandong Ji, Gabriel B. Margolis, Pulkit Agrawal
[ arXiv:2304.01159 ] -
[9] Geometry of contact: contact planning for multi-legged robots via spin models duality
Baxi Chong, Di Luo, Tianyu Wang, Gabriel Margolis, Juntao He, Pulkit Agrawal, Marin Soljačić, Daniel I. Goldman
[ arXiv:2302.03019 ] -
[8] Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behavior
Gabriel B Margolis, Pulkit Agrawal
[ arXiv:2212.03238 ] -
[7] Visual Dexterity: In-Hand Reorientation of Novel and Complex Object Shapes
Tao Chen, Megha Tippur, Siyang Wu, Vikash Kumar, Edward Adelson, Pulkit Agrawal
Science Robotics, 2023, Volume 8, Issue 84 [ arXiv:2211.11744 ] -
[6] Stable Object Reorientation using Contact Plane Registration
Richard Li, Carlos Esteves, Ameesh Makadia, Pulkit Agrawal
International Conference on Robotics and Automation 2022 [ ] -
[5] Rapid Locomotion via Reinforcement Learning
Gabriel B. Margolis, Ge Yang, Kartik Paigwar, Tao Chen, Pulkit Agrawal
[ arXiv:2205.02824 ] -
[4] Neural Descriptor Fields: SE(3) Equivariant Object Representations for Manipulation
Anthony Simeonov, Yilun Du, Andrea Tagliasacchi, Joshua B. Tenenbaum, Alberto Rodriguez, Pulkit Agrawal, Vincent Sitzmann
International Conference on Robotics and Automation 2022 [ arXiv:2112.05124 | code ] -
[3] Equivariant Contrastive Learning
Rumen Dangovski, Li Jing, Charlotte Loh, Seungwook Han, Akash Srivastava, Brian Cheung, Pulkit Agrawal, Marin Soljačić
[ arXiv:2111.00899 ] -
[2] Overcoming the Spectral Bias of Neural Value Approximation
Ge Yang, Anurag Ajay, Pulkit Agrawal
ICLR 2022 Conference Proceedings [ arXiv:2206.04672 ] -
[1] Learning Task Informed Abstractions
Xiang Fu, Ge Yang, Pulkit Agrawal, Tommi Jaakkola
Proceedings of the 38th International Conference on Machine Learning, 2021, PMLR 139 [ arXiv:2106.15612 | code ]
Carlos Argüelles-Delgado
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[4] Resonant Neutrino Flavor Conversion in the Atmosphere
Connor Sponsler, Matheus Hostert, Ivan Martinez-Soler, Carlos A. Argüelles
[ arXiv:2405.12140 ] -
[3] New Pathways in Neutrino Physics via Quantum-Encoded Data Analysis
Jeffrey Lazar, Santiago Giner Olavarrieta, Giancarlo Gatti, Carlos A. Argüelles, Mikel Sanz
[ arXiv:2402.19306 ] -
[2] Two Watts is All You Need: Enabling In-Detector Real-Time Machine Learning for Neutrino Telescopes Via Edge Computing
Miaochen Jin, Yushi Hu, Carlos A. Argüelles
[ arXiv:2311.04983 ] -
[1] Prometheus: An Open-Source Neutrino Telescope Simulation
Jeffrey Lazar, Stephan Meighen-Berger, Christian Haack, David Kim, Santiago Giner, Carlos A. Argüelles
[ arXiv:2304.14526 ]
Daniel Eisenstein
-
[2] Cosmological constraints from density-split clustering in the BOSS CMASS galaxy sample
Enrique Paillas, Carolina Cuesta-Lazaro, Will J. Percival, Seshadri Nadathur, Yan-Chuan Cai, Sihan Yuan, Florian Beutler, Arnaud de Mattia, Daniel Eisenstein, Daniel Forero-Sanchez, Nelson Padilla, Mathilde Pinon, Vanina Ruhlmann-Kleider, Ariel G. Sánchez, Georgios Valogiannis, Pauline Zarrouk
[ arXiv:2309.16541 ] -
[1] SUNBIRD: A simulation-based model for full-shape density-split clustering
Carolina Cuesta-Lazaro, Enrique Paillas, Sihan Yuan, Yan-Chuan Cai, Seshadri Nadathur, Will J. Percival, Florian Beutler, Arnaud de Mattia, Daniel Eisenstein, Daniel Forero-Sanchez, Nelson Padilla, Mathilde Pinon, Vanina Ruhlmann-Kleider, Ariel G. Sánchez, Georgios Valogiannis, Pauline Zarrouk
[ arXiv:2309.16539 ]
Doug Finkbeiner
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[7] Diffusion-HMC: Parameter Inference with Diffusion Model driven Hamiltonian Monte Carlo
Nayantara Mudur, Carolina Cuesta-Lazaro, Douglas P. Finkbeiner
[ arXiv:2405.05255 ] -
[6] Cosmological Field Emulation and Parameter Inference with Diffusion Models
Nayantara Mudur, Carolina Cuesta-Lazaro, Douglas P. Finkbeiner
[ arXiv:2312.07534 ] -
[5] Simulation-based Inference for Exoplanet Atmospheric Retrieval: Insights from winning the Ariel Data Challenge 2023 using Normalizing Flows
Mayeul Aubin, Carolina Cuesta-Lazaro, Ethan Tregidga, Javier Viaña, Cecilia Garraffo, Iouli E. Gordon, Mercedes López-Morales, Robert J. Hargreaves, Vladimir Yu. Makhnev, Jeremy J. Drake, Douglas P. Finkbeiner, Phillip Cargile
[ arXiv:2309.09337 ] -
[4] Measuring the 8621 Å Diffuse Interstellar Band in Gaia DR3 RVS Spectra: Obtaining a Clean Catalog by Marginalizing over Stellar Types
Andrew K. Saydjari, Catherine Zucker, J. E. G. Peek, Douglas P. Finkbeiner
[ arXiv:2212.03879 ] -
[3] Can denoising diffusion probabilistic models generate realistic astrophysical fields?
Nayantara Mudur, Douglas P. Finkbeiner
[ arXiv:2211.12444 ] -
[2] Quantification of high dimensional non-Gaussianities and its implication to Fisher analysis in cosmology
Core Francisco Park, Erwan Allys, Francisco Villaescusa-Navarro, Douglas P. Finkbeiner
The Astrophysical Journal, Volume 946, Number 2 [ arXiv:2204.05435 ] -
[1] Photometry on Structured Backgrounds: Local Pixelwise Infilling by Regression
Andrew K. Saydjari, Douglas P. Finkbeiner
The Astrophysical Journal, 2022, Volume 933, Number 2 [ arXiv:2201.07246 ]
Alexander Rakhlin
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[3] Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF
Tengyang Xie, Dylan J. Foster, Akshay Krishnamurthy, Corby Rosset, Ahmed Awadallah, Alexander Rakhlin
[ arXiv:2405.21046 ] -
[2] Deep learning: a statistical viewpoint
Peter L. Bartlett, Andrea Montanari, and Alexander Rakhlin
[ arXiv:2103.09177 ] -
[1] On the Minimal Error of Empirical Risk Minimization
Gil Kur, Alexander Rakhlin
[ arXiv:2102.12066 ]
Fabian Ruehle
-
[10] KAN: Kolmogorov-Arnold Networks
Ziming Liu, Yixuan Wang, Sachin Vaidya, Fabian Ruehle, James Halverson, Marin Soljačić, Thomas Y. Hou, Max Tegmark
[ arXiv:2404.19756 ] -
[9] The Frozen Phase of Heterotic F-theory Duality
Paul-Konstantin Oehlmann, Fabian Ruehle, Benjamin Sung
[ arXiv:2404.02191 ] -
[8] On classical de Sitter solutions and parametric control
David Andriot, Fabian Ruehle
[ arXiv:2403.07065 ] -
[7] Rigor with Machine Learning from Field Theory to the Poincaré Conjecture
Sergei Gukov, James Halverson, Fabian Ruehle
Nature Reviews Physics 2024 [ arXiv:2402.13321 ] -
[6] T-Duality and Flavor Symmetries in Little String Theories
Hamza Ahmed, Paul-Konstantin Oehlmann, Fabian Ruehle
[ arXiv:2311.02168 ] -
[5] Metric Flows with Neural Networks
James Halverson, Fabian Ruehle
[ arXiv:2310.19870 ] -
[4] Searching for ribbons with machine learning
Sergei Gukov, James Halverson, Ciprian Manolescu, Fabian Ruehle
[ arXiv:2304.09304 ] -
[3] Level Crossings, Attractor Points and Complex Multiplication
Hamza Ahmed, Fabian Ruehle
Journal of High Energy Physics, 2023, Volume 2023, Article number 164 [ arXiv:2304.00027 ] -
[2] Symmetries of Calabi-Yau Prepotentials with Isomorphic Flops
Andre Lukas, Fabian Ruehle
Journal of High Energy 2023, Article 175 [ arXiv:2210.09369 ] -
[1] Learning to Unknot
Sergei Gukov, James Halverson, Fabian Ruehle, and Piotr Sułkowski
Machine Learning - Science and Technology, 2021, Volume 2, Number 2, Article 025035 [ arXiv:2010.16263 ]
Tracy Slatyer
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[3] What does cosmology teach us about non-gravitational properties of dark matter?
Tracy R. Slatyer
Nuclear Physics B, 2024, Volume 1003 [ ] -
[2] Characterizing the Expected Behavior of Non-Poissonian Template Fitting
Luis Gabriel C. Bariuan, Tracy R. Slatyer
Physical Review D, 2023, Volume 107, Issue 10–15 [ arXiv:2207.13097 ] -
[1] Modeling early-universe energy injection with Dense Neural Networks
Yitian Sun, Tracy R. Slatyer
Physical Review D, Volume 107, Article 063541 [ arXiv:2207.06425 ]
Tess Smidt
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[3] Equivariant Symmetry Breaking Sets
YuQing Xie, Tess Smidt
[ arXiv:2402.02681 ] -
[2] Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for Molecule Generation
Ameya Daigavane, Song Kim, Mario Geiger, Tess Smidt
[ arXiv:2311.16199 ] -
[1] Learning Integrable Dynamics with Action-Angle Networks
Ameya Daigavane, Arthur Kosmala, Miles Cranmer, Tess Smidt, Shirley Ho
[ arXiv:2211.15338 ]
Marin Soljacic
-
[24] Stochastic logic in biased coupled photonic probabilistic bits
Michael Horodynski, Charles Roques-Carmes, Yannick Salamin, Seou Choi, Jamison Sloan, Di Luo, Marin Soljačić
[ arXiv:2406.04000 ] -
[23] QuanTA: Efficient High-Rank Fine-Tuning of LLMs with Quantum-Informed Tensor Adaptation
Zhuo Chen, Rumen Dangovski, Charlotte Loh, Owen Dugan, Di Luo, Marin Soljačić
[ arXiv:2406.00132 ] -
[22] KAN: Kolmogorov-Arnold Networks
Ziming Liu, Yixuan Wang, Sachin Vaidya, Fabian Ruehle, James Halverson, Marin Soljačić, Thomas Y. Hou, Max Tegmark
[ arXiv:2404.19756 ] -
[21] TENG: Time-Evolving Natural Gradient for Solving PDEs with Deep Neural Net
Zhuo Chen, Jacob McCarran, Esteban Vizcaino, Marin Soljačić, Di Luo
Open Review, Submission Number 10038 [ arXiv:2404.10771 ] -
[20] Photonic probabilistic machine learning using quantum vacuum noise
Seou Choi, Yannick Salamin, Charles Roques-Carmes, Rumen Dangovski, Di Luo, Zhuo Chen, Michael Horodynski, Jamison Sloan, Shiekh Zia Uddin, Marin Soljacic
[ arXiv:2403.04731 ] -
[19] Mitigating Confirmation Bias in Semi-supervised Learning via Efficient Bayesian Model Averaging
Charlotte Loh, Rumen Dangovski, Shivchander Sudalairaj, Seungwook Han, Ligong Han, Leonid Karlinsky, Marin Soljacic, Akash Srivastava
Transactions on Machine Learning Research 2023, Submission number 1013 [ | code ] -
[18] ANTN: Bridging Autoregressive Neural Networks and Tensor Networks for Quantum Many-Body Simulation
Zhuo Chen, Laker Newhouse, Eddie Chen, Di Luo, Marin Soljačić
[ arXiv:2304.01996 ] -
[17] Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries
Charlotte Loh, Seungwook Han, Shivchander Sudalairaj, Rumen Dangovski, Kai Xu, Florian Wenzel, Marin Soljacic, Akash Srivastava
[ arXiv:2303.02484 ] -
[16] Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows
Owen Dugan, Peter Y. Lu, Rumen Dangovski, Di Luo, Marin Soljačić
[ arXiv:2302.12235 ] -
[15] Geometry of contact: contact planning for multi-legged robots via spin models duality
Baxi Chong, Di Luo, Tianyu Wang, Gabriel Margolis, Juntao He, Pulkit Agrawal, Marin Soljačić, Daniel I. Goldman
[ arXiv:2302.03019 ] -
[14] Creating large Fock states and massively squeezed states in optics using systems with nonlinear bound states in the continuum
Nicholas Rivera, Jamison Sloan, Yannick Salamin, John D. Joannopoulos, Marin Soljacic
PNAS, 2023, Vol. 120, No. 9 [ arXiv:2211.01514 ] -
[13] QuACK: Accelerating Gradient-Based Quantum Optimization with Koopman Operator Learning
Di Luo, Jiayu Shen, Rumen Dangovski, Marin Soljačić
[ arXiv:2211.01365 ] -
[12] Data-driven Acceleration of Quantum Optimization and Machine Learning via Koopman Operator Learning
Di Luo, Jiayu Shen, Rumen Dangovski, Marin Soljacic
NeurIPS 2022 Workshop AI4Science [ ] -
[11] Learning to Optimize Quasi-Newton Methods
Isaac Liao, Rumen R. Dangovski, Jakob N. Foerster, Marin Soljačić
Transactions on Machine Learning Research, 2023 [ arXiv:2210.06171 ] -
[10] On the Importance of Calibration in Semi-supervised Learning
Charlotte Loh, Rumen Dangovski, Shivchander Sudalairaj, Seungwook Han, Ligong Han, Leonid Karlinsky, Marin Soljacic, Akash Srivastava
[ arXiv:2210.04783 ] -
[9] Discovering Conservation Laws using Optimal Transport and Manifold Learning
Peter Y. Lu, Rumen Dangovski, Marin Soljačić
Nature Communications [ arXiv:2208.14995 ] -
[8] Deep Learning and Symbolic Regression for Discovering Parametric Equations
Michael Zhang, Samuel Kim, Peter Y. Lu, Marin Soljačić
IEEE Journals, 2023, PubMed ID 37721885 [ arXiv:2207.00529 ] -
[7] DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings
Yung-Sung Chuang, Rumen Dangovski, Hongyin Luo, Yang Zhang, Shiyu Chang, Marin Soljačić, Shang-Wen Li, Wen-tau Yin, Yoon Kim, James Glass
[ arXiv:2204.10298 ] -
[6] Topogivity: A Machine-Learned Chemical Rule for Discovering Topological Materials
Andrew Ma, Yang Zhang, Thomas Christensen, Hoi Chun Po, Li Jing, Liang Fu, Marin Soljačić
American Chemical Society Publications [ arXiv:2202.05255 ] -
[5] Equivariant Contrastive Learning
Rumen Dangovski, Li Jing, Charlotte Loh, Seungwook Han, Akash Srivastava, Brian Cheung, Pulkit Agrawal, Marin Soljačić
[ arXiv:2111.00899 ] -
[4] Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science
Charlotte Loh, Thomas Christensen, Rumen Dangovski, Samuel Kim, Marin Soljačić
Nature Communications, 2022, Volume 13, Article 4223 [ arXiv:2110.08406 ] -
[3] Observation of enhanced free-electron radiation from photonic flatband resonancesg
Yi Yang, Charles Roques-Carmes, Steven E. Kooi, Haoning Tang, Justin Beroz, Eric Mazur, Ido Kaminer, John D. Joannopoulos, Marin Soljačić
Nature, 2023 [ arXiv:2110.03550 ] -
[2] Discovering Sparse Interpretable Dynamics from Partial Observations
Peter Y. Lu, Joan Ariño, Marin Soljačić
Communications Physics, 2022, Vol 5, Article 206 [ arXiv:2107.10879 ] -
[1] Scalable and Flexible Deep Bayesian Optimization with Auxiliary Information for Scientific Problems
Samuel Kim, Peter Y. Lu, Charlotte Loh, Jamie Smith, Jasper Snoek, Marin Soljačić
Transactions on Machine Learning Research, September 2022 [ arXiv:2104.11667 ]
Max Tegmark
-
[34] Survival of the Fittest Representation: A Case Study with Modular Addition
Xiaoman Delores Ding, Zifan Carl Guo, Eric J. Michaud, Ziming Liu, Max Tegmark
[ arXiv:2405.17420 ] -
[33] How Do Transformers ‘Do’ Physics? Investigating the Simple Harmonic Oscillator
Subhash Kantamneni, Ziming Liu, Max Tegmark
[ arXiv:2405.17209 ] -
[32] Not All Language Model Features Are Linear
Joshua Engels, Isaac Liao, Eric J. Michaud, Wes Gurnee, Max Tegmark
[ arXiv:2405.14860 ] -
[31] OptPDE: Discovering Novel Integrable Systems via AI-Human Collaboration
Subhash Kantamneni, Ziming Liu, Max Tegmark
[ arXiv:2405.04484 ] -
[30] KAN: Kolmogorov-Arnold Networks
Ziming Liu, Yixuan Wang, Sachin Vaidya, Fabian Ruehle, James Halverson, Marin Soljačić, Thomas Y. Hou, Max Tegmark
[ arXiv:2404.19756 ] -
[29] A Resource Model For Neural Scaling Law
Jinyeop Song, Ziming Liu, Max Tegmark, Jeff Gore
[ arXiv:2402.05164 ] -
[28] Opening the AI black box: program synthesis via mechanistic interpretability
Eric J. Michaud, Isaac Liao, Vedang Lad, Ziming Liu, Anish Mudide, Chloe Loughridge, Zifan Carl Guo, Tara Rezaei Kheirkhah, Mateja Vukelić, Max Tegmark
[ arXiv:2402.05110 ] -
[27] Generating Interpretable Networks using Hypernetworks
Isaac Liao, Ziming Liu, Max Tegmark
[ arXiv:2312.03051 ] -
[26] Growing Brains in Recurrent Neural Networks for Multiple Cognitive Tasks
Ziming Liu, Mikail Khona, Ila Fiete, Max Tegmark
NeurIPS 2023 Workshop NeurReps [ ] -
[25] Growing Brains: Co-emergence of Anatomical and Functional Modularity in Recurrent Neural Networks
Ziming Liu, Mikail Khona, Ila R. Fiete, Max Tegmark
[ arXiv:2310.07711 ] -
[24] The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets
Samuel Marks, Max Tegmark
[ arXiv:2310.06824 ] -
[23] Grokking as Compression: A Nonlinear Complexity Perspective
Ziming Liu, Ziqian Zhong, Max Tegmark
[ arXiv:2310.05918 ] -
[22] Language Models Represent Space and Time
Wes Gurnee, Max Tegmark
[ arXiv:2310.02207 ] -
[21] A Neural Scaling Law from Lottery Ticket Ensembling
Ziming Liu, Max Tegmark
[ arXiv:2310.02258 ] -
[20] The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks
Ziqian Zhong, Ziming Liu, Max Tegmark, Jacob Andreas
[ arXiv:2306.17844 ] -
[19] Discovering New Interpretable Conservation Laws as Sparse Invariants
Ziming Liu, Patrick Obin Sturm, Saketh Bharadwaj, Sam Silva, Max Tegmark
[ arXiv:2305.19525 ] -
[18] Seeing is Believing: Brain-Inspired Modular Training for Mechanistic Interpretability
Ziming Liu, Eric Gan, Max Tegmark
[ arXiv:2305.08746 ] -
[17] GenPhys: From Physical Processes to Generative Models
Ziming Liu, Di Luo, Yilun Xu, Tommi Jaakkola, Max Tegmark
[ arXiv:2304.02637 ] -
[16] The Quantization Model of Neural Scaling
Eric J. Michaud, Ziming Liu, Uzay Girit, Max Tegmark
[ arXiv:2303.13506 ] -
[15] PFGM++: Unlocking the Potential of Physics-Inspired Generative Models
Yilun Xu, Ziming Liu, Yonglong Tian, Shangyuan Tong, Max Tegmark, Tommi Jaakkola
[ arXiv:2302.04265 ] -
[14] Precision Machine Learning
Eric J. Michaud, Ziming Liu, Max Tegmark
Entropy, 2023, 25(1) [ arXiv:2210.13447 ] -
[13] Omnigrok: Grokking Beyond Algorithmic Data
Ziming Liu, Eric J. Michaud, Max Tegmark
[ arXiv:2210.01117 ] -
[12] Poisson Flow Generative Models
Yilun Xu, Ziming Liu, Max Tegmark, Tommi Jaakkola
[ arXiv:2209.11178 | code ] -
[11] Toward a more accurate 3D atlas of C. elegans neurons
Michael Skuhersky, Tailin Wu, Eviatar Yemini, Amin Nejatbakhsh, Edward Boyden & Max Tegmark
BMC Bioinformatics, Volume 23, Article 195 [ ] -
[10] Towards Understanding Grokking: An Effective Theory of Representation Learning
Ziming Liu, Ouail Kitouni, Niklas Nolte, Eric J. Michaud, Max Tegmark, Mike Williams
[ arXiv:2205.10343 ] -
[9] Pareto-optimal clustering with the primal deterministic information bottleneck
Andrew K. Tan, Max Tegmark, Isaac L. Chuang
Entropy, 2022, 24(6) [ arXiv:2204.02489 ] -
[8] AI Poincaré 2.0: Machine Learning Conservation Laws from Differential Equations
Ziming Liu, Varun Madhavan, Max Tegmark
Physical Review E, 2022, Volume 106, Article 045307 [ arXiv:2203.12610 ] -
[7] Biological error correction codes generate fault-tolerant neural networks
Alexander Zlokapa, Andrew K. Tan, John M. Martyn, Max Tegmark, Isaac L. Chuang
[ arXiv:2202.12887 ] -
[6] Physics-Augmented Learning: A New Paradigm Beyond Physics-Informed Learning
Ziming Liu, Yunyue Chen, Yuanqi Du, Max Tegmark
[ arXiv:2109.13901 ] -
[5] Machine-learning hidden symmetries
Ziming Liu, Max Tegmark
Physical Review Letters, 2022, 128, 180201 [ arXiv:2109.09721 ] -
[4] Machine-Learning media bias
Samantha D’Alonzo, Max Tegmark
PLOS ONE, 2022, Volume 17, Issue 8, Article e0271947 [ arXiv:2109.00024 ] -
[3] Machine-Learning Non-Conservative Dynamics for New-Physics Detection
Ziming Liu, Bohan Wang, Qi Meng, Wei Chen, Max Tegmark, Tie-Yan Liu
Physical Review E, 2021, Vol. 104, Article 055302 [ arXiv:2106.00026 ] -
[2] AI Poincaré: Machine Learning Conservation Laws from Trajectories
Ziming Liu and Max Tegmark
Physical Review Letters, 2021, Volume 126, Issue 18, Article 180604 [ arXiv:2011.04698 ] -
[1] AI Feynman: a Physics-Inspired Method for Symbolic Regression
Silviu-Marian Udrescu, Max Tegmark
Sciences Advances, 2020, 6:easy2631 [ arXiv:1905.11481 ]
Ashley Villar
-
[10] Superphot+: Realtime Fitting and Classification of Supernova Light Curves
Kaylee M. de Soto (1), Ashley Villar (1), Edo Berger (1 and 2), Sebastian Gomez (3), Griffin Hosseinzadeh (4), Doug Branton (5), Sandro Campos (6), Melissa DeLucchi (6), Jeremy Kubica (6), Olivia Lynn (6), Konstantin Malanchev (6), Alex I. Malz (6) ((1) Center for Astrophysics | Harvard & Smithsonian, (2) The NSF AI Institute for Artificial Intelligence and Fundamental Interactions, (3) Space Telescope Science Institute, (4) Steward Observatory | University of Arizona, (5) DiRAC Institute and the Department of Astronomy | University of Washington, (6) McWilliams Center for Cosmology | Department of Physics at Carnegie Mellon University)
[ arXiv:2403.07975 ] -
[9] A Physics-Informed Variational Autoencoder for Rapid Galaxy Inference and Anomaly Detection
Alexander Gagliano, V. Ashley Villar
[ arXiv:2312.16687 ] -
[8] SN2023ixf in Messier 101: A Variable Red Supergiant as the Progenitor Candidate to a Type II Supernova
Charles D. Kilpatrick, Ryan J. Foley, Wynn V. Jacobson-Galán, Anthony L. Piro, Stephen J. Smartt, Maria R. Drout, Alexander Gagliano, Christa Gall, Jens Hjorth, David O. Jones, Kaisey S. Mandel, Raffaella Margutti, Conor L. Ransome, V. Ashley Villar, David A. Coulter, Hua Gao, David Jacob Matthews, Yossef Zenati
The Astrophysical Journal Letters, 2023, Volume 952, Number 1 [ arXiv:2306.04722 ] -
[7] Multiple Peaks and a Long Precursor in the Type IIn Supernova 2021qqp: An Energetic Explosion in a Complex Circumstellar Environment
Daichi Hiramatsu, Tatsuya Matsumoto, Edo Berger, Conor Ransome, V. Ashley Villar, Sebastian Gomez, Yvette Cendes, Kishalay De, K. Azalee Bostroem, Joseph Farah, D. Andrew Howell, Curtis McCully, Megan Newsome, Estefania Padilla Gonzalez, Craig Pellegrino, Akihiro Suzuki, Giacomo Terreran
The Astrophysical Journal, 2024, Volume 964, Number 2 [ arXiv:2305.11168 ] -
[6] Identifying Tidal Disruption Events with an Expansion of the FLEET Machine Learning Algorithm
Sebastian Gomez, V. Ashley Villar, Edo Berger, Suvi Gezari, Sjoert van Velzen, Matt Nicholl, Peter K. Blanchard, Kate. D. Alexander
The Astrophysical Journal, 2023, Volume 949, Issue 113 [ arXiv:2210.10810 ] -
[5] The First Two Years of FLEET: an Active Search for Superluminous Supernovae
Sebastian Gomez, Edo Berger, Peter K. Blanchard, Griffin Hosseinzadeh, Matt Nicholl, Daichi Hiramatsu, V. Ashley Villar, Yao Yin
The Astrophysical Journal, 2023, Volume 949, Issue 114 [ arXiv:2210.10811 ] -
[4] Photometrically-Classified Superluminous Supernovae from the Pan-STARRS1 Medium Deep Survey: A Case Study for Science with Machine Learning-Based Classification
Brian Hsu, Griffin Hosseinzadeh, V. Ashley Villar, Edo Berger
The Astrophysical Journal, 2022, Volume 937, Number 1 [ arXiv:2204.09809 ] -
[3] A Deep-learning Approach for Live Anomaly Detection of Extragalactic Transients
Ashley Villar, Miles Cranmer, Edo Berger, Gabriella Contardo, Shirley Ho, Griffin Hosseinzadeh, Joshua Yao-Yu Lin
The Astrophysical Journal Supplement Series, Volume 255 [ ] -
[2] The Luminous and Double-Peaked Type Ic Supernova 2019stc: Evidence for Multiple Energy Sources
Sebastian Gomez, Edo Berger, Griffin Hosseinzadeh, Peter K. Blanchard, Matt Nicholl, V. Ashley Villar
The Astrophysical Journal, 2021, Vol. 913, Article 143 [ arXiv:2103.02611 ] -
[1] Detection and Parameter Estimation of Gravitational Waves from Binary Neutron-Star Mergers in Real LIGO Data using Deep Learning
Plamen G. Krastev, Kiranjyot Gill, V. Ashley Villar, Edo Berger
Physics Letters B, 2021, Vol. 815, Article 136161 [ arXiv:2012.13101 ]
IAIFI Fellows
Michael Albergo
-
[10] Practical applications of machine-learned flows on gauge fields
Ryan Abbott, Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2404.11674 ] -
[9] Multiscale Normalizing Flows for Gauge Theories
Ryan Abbott, Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2404.10819 ] -
[8] Normalizing flows for lattice gauge theory in arbitrary space-time dimension
Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Alexander G.D.G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2305.02402 ] -
[7] Aspects of scaling and scalability for flow-based sampling of lattice QCD
Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Alexander G. D. G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
The European Physical Journal A 2023, Volume 59, Article Number 257 [ arXiv:2211.07541 ] -
[6] Sampling QCD field configurations with gauge-equivariant flow models
Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Alexander G. D. G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2208.03832 ] -
[5] Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions
Ryan Abbott, Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Betsy Tian, Julian M. Urban
Physical REview D, 2022, Volume 106, Issue 7 [ arXiv:2207.08945 ] -
[4] Flow-based sampling in the lattice Schwinger model at criticality
Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
Physical Review D, 2022, Volume 106, Article 014514 [ arXiv:2202.11712 ] -
[3] Flow-based sampling for multimodal distributions in lattice field theory
Daniel C. Hackett, Chung-Chun Hsieh, Michael S. Albergo, Denis Boyda, Jiunn-Wei Chen, Kai-Feng Chen, Kyle Cranmer, Gurtej Kanwar, Phiala E. Shanahan
[ arXiv:2107.00734 ] -
[2] Flow-based sampling for fermionic lattice field theories
Michael S. Albergo, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Julian M. Urban, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Phiala E. Shanahan
Physical Review D, 2021, Vol. 104, Iss. 11 – 1 [ arXiv:2106.05934 ] -
[1] Introduction to Normalizing Flows for Lattice Field Theory
Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Kyle Cranmer, Sébastien Racanière, Danilo Jimenez Rezende, and Phiala E. Shanahan
[ arXiv:2101.08176 ]
Denis Boyda
-
[11] Practical applications of machine-learned flows on gauge fields
Ryan Abbott, Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2404.11674 ] -
[10] Multiscale Normalizing Flows for Gauge Theories
Ryan Abbott, Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2404.10819 ] -
[9] Applications of flow models to the generation of correlated lattice QCD ensembles
Ryan Abbott, Aleksandar Botev, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2401.10874 ] -
[8] Normalizing flows for lattice gauge theory in arbitrary space-time dimension
Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Alexander G.D.G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2305.02402 ] -
[7] Aspects of scaling and scalability for flow-based sampling of lattice QCD
Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Alexander G. D. G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
The European Physical Journal A 2023, Volume 59, Article Number 257 [ arXiv:2211.07541 ] -
[6] Sampling QCD field configurations with gauge-equivariant flow models
Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Alexander G. D. G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2208.03832 ] -
[5] Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions
Ryan Abbott, Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Betsy Tian, Julian M. Urban
Physical REview D, 2022, Volume 106, Issue 7 [ arXiv:2207.08945 ] -
[4] Flow-based sampling in the lattice Schwinger model at criticality
Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
Physical Review D, 2022, Volume 106, Article 014514 [ arXiv:2202.11712 ] -
[3] Flow-based sampling for multimodal distributions in lattice field theory
Daniel C. Hackett, Chung-Chun Hsieh, Michael S. Albergo, Denis Boyda, Jiunn-Wei Chen, Kai-Feng Chen, Kyle Cranmer, Gurtej Kanwar, Phiala E. Shanahan
[ arXiv:2107.00734 ] -
[2] Flow-based sampling for fermionic lattice field theories
Michael S. Albergo, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Julian M. Urban, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Phiala E. Shanahan
Physical Review D, 2021, Vol. 104, Iss. 11 – 1 [ arXiv:2106.05934 ] -
[1] Introduction to Normalizing Flows for Lattice Field Theory
Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Kyle Cranmer, Sébastien Racanière, Danilo Jimenez Rezende, and Phiala E. Shanahan
[ arXiv:2101.08176 ]
Samuel Bright-Thonney
-
[2] Safe but Incalculable: Energy-weighting is not all you need
Samuel Bright-Thonney, Benjamin Nachman, Jesse Thaler
[ arXiv:2311.07652 ] -
[1] Chained Quantile Morphing with Normalizing Flows
Samuel Bright-Thonney, Philip Harris, Patrick McCormack, Simon Rothman
[ arXiv:2309.15912 ]
Carolina Cuesta
-
[13] Unsupervised Searches for Cosmological Parity Violation: Improving Detection Power with the Neural Field Scattering Transform
Matthew Craigie, Peter L. Taylor, Yuan-Sen Ting, Carolina Cuesta-Lazaro, Rossana Ruggeri, Tamara M. Davis
[ arXiv:2405.13083 ] -
[12] Diffusion-HMC: Parameter Inference with Diffusion Model driven Hamiltonian Monte Carlo
Nayantara Mudur, Carolina Cuesta-Lazaro, Douglas P. Finkbeiner
[ arXiv:2405.05255 ] -
[11] A Parameter-Masked Mock Data Challenge for Beyond-Two-Point Galaxy Clustering Statistics
Beyond-2pt Collaboration - Elisabeth Krause, Yosuke Kobayashi, Andrés N. Salcedo, Mikhail M. Ivanov, Tom Abel, Kazuyuki Akitsu, Raul E. Angulo, Giovanni Cabass, Sofia Contarini, Carolina Cuesta-Lazaro, ChangHoon Hahn, Nico Hamaus, Donghui Jeong, Chirag Modi, Nhat-Minh Nguyen, Takahiro Nishimichi, Enrique Paillas, Marcos Pellejero Ibañez, Oliver H. E. Philcox, Alice Pisani, Fabian Schmidt, Satoshi Tanaka, Giovanni Verza, Sihan Yuan, Matteo Zennaro
[ arXiv:2405.02252 ] -
[10] Debiasing with Diffusion: Probabilistic reconstruction of Dark Matter fields from galaxies with CAMELS
Victoria Ono, Core Francisco Park, Nayantara Mudur, Yueying Ni, Carolina Cuesta-Lazaro, Francisco Villaescusa-Navarro
[ arXiv:2403.10648 ] -
[9] Full-shape analysis with simulation-based priors: constraints on single field inflation from BOSS
Mikhail M. Ivanov, Carolina Cuesta-Lazaro, Siddharth Mishra-Sharma, Andrej Obuljen, Michael W. Toomey
[ arXiv:2402.13310 ] -
[8] LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics and Cosmology
Matthew Ho, Deaglan J. Bartlett, Nicolas Chartier, Carolina Cuesta-Lazaro, Simon Ding, Axel Lapel, Pablo Lemos, Christopher C. Lovell, T. Lucas Makinen, Chirag Modi, Viraj Pandya, Shivam Pandey, Lucia A. Perez, Benjamin Wandelt, Greg L. Bryan
The Open Journal of Astrophysics, 2024, Volume 7 [ arXiv:2402.05137 ] -
[7] Cosmological Field Emulation and Parameter Inference with Diffusion Models
Nayantara Mudur, Carolina Cuesta-Lazaro, Douglas P. Finkbeiner
[ arXiv:2312.07534 ] -
[6] Learning an Effective Evolution Equation for Particle-Mesh Simulations Across Cosmologies
Nicolas Payot, Pablo Lemos, Laurence Perreault-Levasseur, Carolina Cuesta-Lazaro, Chirag Modi, Yashar Hezaveh
[ arXiv:2311.18017 ] -
[5] A point cloud approach to generative modeling for galaxy surveys at the field level
Carolina Cuesta-Lazaro, Siddharth Mishra-Sharma
[ arXiv:2311.17141 ] -
[4] Probabilistic reconstruction of Dark Matter fields from biased tracers using diffusion models
Core Francisco Park, Victoria Ono, Nayantara Mudur, Yueying Ni, Carolina Cuesta-Lazaro
[ arXiv:2311.08558 ] -
[3] Cosmological constraints from density-split clustering in the BOSS CMASS galaxy sample
Enrique Paillas, Carolina Cuesta-Lazaro, Will J. Percival, Seshadri Nadathur, Yan-Chuan Cai, Sihan Yuan, Florian Beutler, Arnaud de Mattia, Daniel Eisenstein, Daniel Forero-Sanchez, Nelson Padilla, Mathilde Pinon, Vanina Ruhlmann-Kleider, Ariel G. Sánchez, Georgios Valogiannis, Pauline Zarrouk
[ arXiv:2309.16541 ] -
[2] SUNBIRD: A simulation-based model for full-shape density-split clustering
Carolina Cuesta-Lazaro, Enrique Paillas, Sihan Yuan, Yan-Chuan Cai, Seshadri Nadathur, Will J. Percival, Florian Beutler, Arnaud de Mattia, Daniel Eisenstein, Daniel Forero-Sanchez, Nelson Padilla, Mathilde Pinon, Vanina Ruhlmann-Kleider, Ariel G. Sánchez, Georgios Valogiannis, Pauline Zarrouk
[ arXiv:2309.16539 ] -
[1] Simulation-based Inference for Exoplanet Atmospheric Retrieval: Insights from winning the Ariel Data Challenge 2023 using Normalizing Flows
Mayeul Aubin, Carolina Cuesta-Lazaro, Ethan Tregidga, Javier Viaña, Cecilia Garraffo, Iouli E. Gordon, Mercedes López-Morales, Robert J. Hargreaves, Vladimir Yu. Makhnev, Jeremy J. Drake, Douglas P. Finkbeiner, Phillip Cargile
[ arXiv:2309.09337 ]
Akshunna S. Dogra
- [1] SHAPER: Can You Hear the Shape of a Jet?
Demba Ba, Akshunna S. Dogra, Rikab Gambhir, Abiy Tasissa, Jesse Thaler
Journal of High Energy Physics, 2023, Volume 2023, Article 195 [ arXiv:2302.12266 ]
Alexander Gagliano
-
[5] Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows
John Franklin Crenshaw, J. Bryce Kalmbach, Alexander Gagliano, Ziang Yan, Andrew J. Connolly, Alex I. Malz, Samuel J. Schmidt, The LSST Dark Energy Science Collaboration
[ arXiv:2405.04740 ] -
[4] Multi-filter UV to NIR Data-driven Light Curve Templates for Stripped Envelope Supernovae
Somayeh Khakpash, Federica B. Bianco, Maryam Modjaz, Willow F. Fortino, Alexander Gagliano, Conor Larison, Tyler A. Pritchard
[ arXiv:2405.01672 ] -
[3] A Physics-Informed Variational Autoencoder for Rapid Galaxy Inference and Anomaly Detection
Alexander Gagliano, V. Ashley Villar
[ arXiv:2312.16687 ] -
[2] SN2023ixf in Messier 101: A Variable Red Supergiant as the Progenitor Candidate to a Type II Supernova
Charles D. Kilpatrick, Ryan J. Foley, Wynn V. Jacobson-Galán, Anthony L. Piro, Stephen J. Smartt, Maria R. Drout, Alexander Gagliano, Christa Gall, Jens Hjorth, David O. Jones, Kaisey S. Mandel, Raffaella Margutti, Conor L. Ransome, V. Ashley Villar, David A. Coulter, Hua Gao, David Jacob Matthews, Yossef Zenati
The Astrophysical Journal Letters, 2023, Volume 952, Number 1 [ arXiv:2306.04722 ] -
[1] First Impressions: Early-Time Classification of Supernovae using Host Galaxy Information and Shallow Learning
Alexander Gagliano, Gabriella Contardo, Daniel Foreman-Mackey, Alex I. Malz, Patrick D. Aleo
[ arXiv:2305.08894 ]
Gaia Grosso
- [1] Anomaly-aware summary statistic from data batches
Gaia Grosso
[ arXiv:2407.01249 ]
Thomas Harvey
Di Luo
-
[22] Simulating moiré quantum matter with neural network
Di Luo, David D. Dai, Liang Fu
[ arXiv:2406.17645 ] -
[21] Stochastic logic in biased coupled photonic probabilistic bits
Michael Horodynski, Charles Roques-Carmes, Yannick Salamin, Seou Choi, Jamison Sloan, Di Luo, Marin Soljačić
[ arXiv:2406.04000 ] -
[20] QuanTA: Efficient High-Rank Fine-Tuning of LLMs with Quantum-Informed Tensor Adaptation
Zhuo Chen, Rumen Dangovski, Charlotte Loh, Owen Dugan, Di Luo, Marin Soljačić
[ arXiv:2406.00132 ] -
[19] TENG: Time-Evolving Natural Gradient for Solving PDEs with Deep Neural Net
Zhuo Chen, Jacob McCarran, Esteban Vizcaino, Marin Soljačić, Di Luo
Open Review, Submission Number 10038 [ arXiv:2404.10771 ] -
[18] Photonic probabilistic machine learning using quantum vacuum noise
Seou Choi, Yannick Salamin, Charles Roques-Carmes, Rumen Dangovski, Di Luo, Zhuo Chen, Michael Horodynski, Jamison Sloan, Shiekh Zia Uddin, Marin Soljacic
[ arXiv:2403.04731 ] -
[17] Operator Learning Renormalization Group
Xiu-Zhe Luo, Di Luo, Roger G. Melko
[ arXiv:2403.03199 ] -
[16] Real-time Dynamics of the Schwinger Model as an Open Quantum System with Neural Density Operators
Joshua Lin, Di Luo, Xiaojun Yao, Phiala E. Shanahan
[ arXiv:2402.06607 ] -
[15] Pairing-based graph neural network for simulating quantum materials
Di Luo, David D. Dai, Liang Fu
[ arXiv:2311.02143 ] -
[14] Quantum Computation and Simulation using Fermion-Pair Registers
Xiangkai Sun, Di Luo, Soonwon Choi
[ arXiv:2306.03905 ] -
[13] GenPhys: From Physical Processes to Generative Models
Ziming Liu, Di Luo, Yilun Xu, Tommi Jaakkola, Max Tegmark
[ arXiv:2304.02637 ] -
[12] ANTN: Bridging Autoregressive Neural Networks and Tensor Networks for Quantum Many-Body Simulation
Zhuo Chen, Laker Newhouse, Eddie Chen, Di Luo, Marin Soljačić
[ arXiv:2304.01996 ] -
[11] Artificial intelligence for artificial materials: moiré atom
Di Luo, Aidan P. Reddy, Trithep Devakul, Liang Fu
[ arXiv:2303.08162 ] -
[10] Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows
Owen Dugan, Peter Y. Lu, Rumen Dangovski, Di Luo, Marin Soljačić
[ arXiv:2302.12235 ] -
[9] Geometry of contact: contact planning for multi-legged robots via spin models duality
Baxi Chong, Di Luo, Tianyu Wang, Gabriel Margolis, Juntao He, Pulkit Agrawal, Marin Soljačić, Daniel I. Goldman
[ arXiv:2302.03019 ] -
[8] Simulating 2+1D Lattice Quantum Electrodynamics at Finite Density with Neural Flow Wavefunctions
Zhuo Chen, Di Luo, Kaiwen Hu, Bryan K. Clark
[ arXiv:2212.06835 ] -
[7] Variational Neural-Network Ansatz for Continuum Quantum Field Theory
John M. Martyn, Khadijeh Najafi, Di Luo
APS Journals 2023, Volume 131, Issue 8 [ arXiv:2212.00782 ] -
[6] Gauge Equivariant Neural Networks for 2+1D U(1) Gauge Theory Simulations in Hamiltonian Formulation
Di Luo, Shunyue Yuan, James Stokes, Bryan K. Clark
[ arXiv:2211.03198 ] -
[5] QuACK: Accelerating Gradient-Based Quantum Optimization with Koopman Operator Learning
Di Luo, Jiayu Shen, Rumen Dangovski, Marin Soljačić
[ arXiv:2211.01365 ] -
[4] Data-driven Acceleration of Quantum Optimization and Machine Learning via Koopman Operator Learning
Di Luo, Jiayu Shen, Rumen Dangovski, Marin Soljacic
NeurIPS 2022 Workshop AI4Science [ ] -
[3] Finite-Volume Pionless Effective Field Theory for Few-Nucleon Systems with Differentiable Programming
Xiangkai Sun, William Detmold, Di Luo, Phiala E. Shanahan
[ arXiv:2202.03530 ] -
[2] Infinite Neural Network Quantum States
Di Luo, James Halverson
Machine Learning: Science and Technology, 2023, Volume 4, Number 2 [ arXiv:2112.00723 ] -
[1] Classical Shadows for Quantum Process Tomography on Near-term Quantum Computers
Ryan Levy, Di Luo, Bryan K. Clark
Physical Review Research, 2024, Volume 6, Issue 1 [ arXiv:2110.02965 ]
Jessie Micallef
Siddharth Mishra-Sharma
-
[11] PAPERCLIP: Associating Astronomical Observations and Natural Language with Multi-Modal Models
Siddharth Mishra-Sharma, Yiding Song, Jesse Thaler
[ arXiv:2403.08851 ] -
[10] Full-shape analysis with simulation-based priors: constraints on single field inflation from BOSS
Mikhail M. Ivanov, Carolina Cuesta-Lazaro, Siddharth Mishra-Sharma, Andrej Obuljen, Michael W. Toomey
[ arXiv:2402.13310 ] -
[9] A point cloud approach to generative modeling for galaxy surveys at the field level
Carolina Cuesta-Lazaro, Siddharth Mishra-Sharma
[ arXiv:2311.17141 ] -
[8] Data Compression and Inference in Cosmology with Self-Supervised Machine Learning
Aizhan Akhmetzhanova, Siddharth Mishra-Sharma, Cora Dvorkin
Monthly Notices of the Royal Astronomical Society, 2023, Volume 527, Issue 3 [ arXiv:2308.09751 ] -
[7] Hierarchical Neural Simulation-Based Inference Over Event Ensembles
Lukas Heinrich, Siddharth Mishra-Sharma, Chris Pollard, Philipp Windischhofer
[ arXiv:2306.12584 ] -
[6] Inferring subhalo effective density slopes from strong lensing observations with neural likelihood-ratio estimation
Gemma Zhang, Siddharth Mishra-Sharma, Cora Dvorkin
Monthly Notices of the Royal Astronomical Society, 2022, Volume 517, Issue 3 [ arXiv:2208.13796 ] -
[5] Uncovering dark matter density profiles in dwarf galaxies with graph neural networks
Tri Nguyễn, Siddharth Mishra-Sharma, Reuel Williams, Lina Necib
Physical Review D, 202, Volume 107, Issue 4 [ arXiv:2208.12825 ] -
[4] Radio excess from stimulated dark matter decay
Andrea Caputo,Hongwan Liu, Siddharth Mishra-Sharma,Maxim Pospelov, Joshua T. Ruderman
Physical REview D, 2023, Volume 107, Issue 12 [ ] -
[3] Strong Lensing Source Reconstruction Using Continuous Neural Fields
Siddharth Mishra-Sharma, Ge Yang
[ arXiv:2206.14820 ] -
[2] A neural simulation-based inference approach for characterizing the Galactic Center γ-ray excess
Siddharth Mishra-Sharma, Kyle Cranmer
Physical Review D, 2922, Volume 105, Article 063017 [ arXiv:2110.06931 ] -
[1] Inferring dark matter substructure with astrometric lensing beyond the power spectrum
Siddharth Mishra-Sharma
[ arXiv:2110.01620 ]
Ge Yang
-
[10] Feature Splatting: Language-Driven Physics-Based Scene Synthesis and Editing
Ri-Zhao Qiu, Ge Yang, Weijia Zeng, Xiaolong Wang
[ arXiv:2404.01223 ] -
[9] Distilled Feature Fields Enable Few-Shot Language-Guided Manipulation
William Shen, Ge Yang, Alan Yu, Jansen Wong, Leslie Pack Kaelbling, Phillip Isola
[ arXiv:2308.07931 ] -
[8] Machine Learning for Quantum-Enhanced Gravitational-Wave Observatories
Chris Whittle, Ge Yang, Matthew Evans, Lisa Barsotti
Physical Review D, Volume 108, Article 043034 [ arXiv:2305.13780 ] -
[7] Neural Volumetric Memory for Visual Locomotion Control
Ruihan Yang, Ge Yang, Xiaolong Wang
[ arXiv:2304.01201 ] -
[6] Strong Lensing Source Reconstruction Using Continuous Neural Fields
Siddharth Mishra-Sharma, Ge Yang
[ arXiv:2206.14820 ] -
[5] Rapid Locomotion via Reinforcement Learning
Gabriel B. Margolis, Ge Yang, Kartik Paigwar, Tao Chen, Pulkit Agrawal
[ arXiv:2205.02824 ] -
[4] Invariance Through Latent Alignment
Takuma Yoneda, Ge Yang, Matthew R. Walter, Bradly Stadie
[ arXiv:2112.08526 ] -
[3] Overcoming the Spectral Bias of Neural Value Approximation
Ge Yang, Anurag Ajay, Pulkit Agrawal
ICLR 2022 Conference Proceedings [ arXiv:2206.04672 ] -
[2] Learning Task Informed Abstractions
Xiang Fu, Ge Yang, Pulkit Agrawal, Tommi Jaakkola
Proceedings of the 38th International Conference on Machine Learning, 2021, PMLR 139 [ arXiv:2106.15612 | code ] -
[1] Single electrons on solid neon as a solid-state quit platform
Xianjing Zhou, Gerwin Koolstra, Xufeng Zhang, Ge Yang, Xu Han, Brennan Dizdar, Divan Ralu, Wei Guo, Kater W. Murch, David I. Shuster, Dafei Jin
Nature, 2022, 605, 46-50 [ arXiv:2106.10326 ]
Long-Term Visitors
Adam Zacharia Anil
Helena Brittain
Francisco Galvan
Roger Rusack
Nathaniel Santiago
Victor Verschuren
IAIFI Affiliates
Aram Apyan
Ning Bao
George Barbastathis
Pierre-Hugues Beauchemin
- [1] Towards Universal Unfolding of Detector Effects in High-Energy Physics using Denoising Diffusion Probabilistic Models
Camila Pazos, Shuchin Aeron, Pierre-Hugues Beauchemin, Vincent Croft, Martin Klassen, Taritree Wongjirad
[ arXiv:2406.01507 ]
Jonathan Blazek
-
[2] Learning Galaxy Intrinsic Alignment Correlations
Sneh Pandya, Yuanyuan Yang, Nicholas Van Alfen, Jonathan Blazek, Robin Walters
[ arXiv:2404.13702 ] -
[1] E(2) Equivariant Neural Networks for Robust Galaxy Morphology Classification
Sneh Pandya, Purvik Patel, Franc O, Jonathan Blazek
[ arXiv:2311.01500 | code ]
Michael Douglas
Liang Fu
-
[4] Simulating moiré quantum matter with neural network
Di Luo, David D. Dai, Liang Fu
[ arXiv:2406.17645 ] -
[3] Pairing-based graph neural network for simulating quantum materials
Di Luo, David D. Dai, Liang Fu
[ arXiv:2311.02143 ] -
[2] Artificial intelligence for artificial materials: moiré atom
Di Luo, Aidan P. Reddy, Trithep Devakul, Liang Fu
[ arXiv:2303.08162 ] -
[1] Topogivity: A Machine-Learned Chemical Rule for Discovering Topological Materials
Andrew Ma, Yang Zhang, Thomas Christensen, Hoi Chun Po, Li Jing, Liang Fu, Marin Soljačić
American Chemical Society Publications [ arXiv:2202.05255 ]
Cecilia Garraffo
- [1] Simulation-based Inference for Exoplanet Atmospheric Retrieval: Insights from winning the Ariel Data Challenge 2023 using Normalizing Flows
Mayeul Aubin, Carolina Cuesta-Lazaro, Ethan Tregidga, Javier Viaña, Cecilia Garraffo, Iouli E. Gordon, Mercedes López-Morales, Robert J. Hargreaves, Vladimir Yu. Makhnev, Jeremy J. Drake, Douglas P. Finkbeiner, Phillip Cargile
[ arXiv:2309.09337 ]
Kaiming He
An Huang
Tommi Jaakkola
-
[4] GenPhys: From Physical Processes to Generative Models
Ziming Liu, Di Luo, Yilun Xu, Tommi Jaakkola, Max Tegmark
[ arXiv:2304.02637 ] -
[3] PFGM++: Unlocking the Potential of Physics-Inspired Generative Models
Yilun Xu, Ziming Liu, Yonglong Tian, Shangyuan Tong, Max Tegmark, Tommi Jaakkola
[ arXiv:2302.04265 ] -
[2] Poisson Flow Generative Models
Yilun Xu, Ziming Liu, Max Tegmark, Tommi Jaakkola
[ arXiv:2209.11178 | code ] -
[1] Learning Task Informed Abstractions
Xiang Fu, Ge Yang, Pulkit Agrawal, Tommi Jaakkola
Proceedings of the 38th International Conference on Machine Learning, 2021, PMLR 139 [ arXiv:2106.15612 | code ]
Erik Katsavounidis
-
[2] GWAK: Gravitational-Wave Anomalous Knowledge with Recurrent Autoencoders
Ryan Raikman, Eric A. Moreno, Ekaterina Govorkova, Ethan J Marx, Alec Gunny, William Benoit, Deep Chatterjee, Rafia Omer, Muhammed Saleem, Dylan S Rankin, Michael W Coughlin, Philip C Harris, Erik Katsavounidis
Journal of High Energy Physics 2024, Volume 2024, Article number 158 [ arXiv:2309.11537 ] -
[1] Hardware-accelerated Inference for Real-Time Gravitational-Wave Astronomy
Alec Gunny, Dylan Rankin, Jeffrey Krupa, Muhammed Saleem, Tri Nguyen, Michael Coughlin, Philip Harris, Erik Katsavounidis, Steven Timm, Burt Holzman
[ arXiv:2108.12430 ]
Rahul Kulkarni
Sudhir Malik
Vidya Manian
Tyler Maunu
Brent D. Nelson
Olga Goulko
Cengiz Pehlevan
Dan Roberts
-
[6] Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data
Matthias Gerstgrasser, Rylan Schaeffer, Apratim Dey, Rafael Rafailov, Henry Sleight, John Hughes, Tomasz Korbak, Rajashree Agrawal, Dhruv Pai, Andrey Gromov, Daniel A. Roberts, Diyi Yang, David L. Donoho, Sanmi Koyejo
[ arXiv:2404.01413 ] -
[5] The Unreasonable Ineffectiveness of the Deeper Layers
Andrey Gromov, Kushal Tirumala, Hassan Shapourian, Paolo Glorioso, Daniel A. Roberts
[ arXiv:2403.17887 ] -
[4] A Solvable Model of Neural Scaling Laws
Alexander Maloney, Daniel A. Roberts, James Sully
[ arXiv:2210.16859 ] -
[3] The Principles of Deep Learning Theory
Daniel A. Roberts, Sho Yaida, Boris Hanin
Cambridge University Press (Book), 2022 [ arXiv:2106.10165 ] -
[2] Why is AI hard and Physics simple?
Daniel A. Roberts
[ arXiv:2104.00008 ] -
[1] Topological obstructions to autoencoding
Joshua Batson, C. Grace Haaf, Yonatan Kahn, Daniel A. Roberts
Journal of High Energy Physics, 2021, Issue 4, Article 280 [ arXiv:2102.08380 ]
Artan Sheshmani
-
[3] Categorical Representation Learning and RG flow operators for algorithmic classifiers
Artan Sheshmani, Yizhuang You, Wenbo Fu, Ahmadreza Azizi
Machine Learning Science and Technology, Volume 4, Number 1, Article 015012 [ arXiv:2203.07975 ] -
[2] Strictification and gluing of Lagrangian distributions on derived schemes with shifted symplectic forms
Dennis Borisov, Ludmil Katzarkov, Artan Sheshmani, Shing-Tung Yau
Science Direct Journals, 2024, Volume 438 [ arXiv:1908.00651 ] -
[1] Elliptic stable envelopes and hypertoric loop spaces
Michael McBreen, Artan Sheshmani, Shing-Tung Yau
Selecta Mathematica, 2023, Volume 29, Article number 73 [ arXiv:2010.0067 ]
Akira Sone
- [1] Quantum inception score
Akira Sone, Akira Tanji, and Naoki Yamamoto
Physical Review Research, 2024, Volume 6, Issue 3 [ ]
Christopher Stubbs
Hidenori Tanaka
Abiy Tasissa
-
[2] SHAPER: Can You Hear the Shape of a Jet?
Demba Ba, Akshunna S. Dogra, Rikab Gambhir, Abiy Tasissa, Jesse Thaler
Journal of High Energy Physics, 2023, Volume 2023, Article 195 [ arXiv:2302.12266 ] -
[1] On the convergence of group-sparse autoencoders
Emmanouil Theodosis, Bahareh Tolooshams, Pranay Tankala, Abiy Tasissa, Demba Ba
[ arXiv:2102.07003 ]
Washington Taylor
- [1] Identifying equivalent Calabi–Yau topologies: A discrete challenge from math and physics for machine learning
Vishnu Jejjala, Washington Taylor, Andrew Turner
[ arXiv:2202.07590 ]
Mark Vogelsberger
Susanne Yelin
Post-Docs and Research Scientists
Steven Eulig
Plamen Krastev
- [1] Deep Learning Detection and Classification of Gravitational Waves from Neutron Star-Black Hole Mergers
Richard Qiu, Plamen Krastev, Kiranjyot Gill, Edo Berger
Physics Letters B, 2023, Volume 840 [ arXiv:2210.15888 ]
Victor Samuel Pérez Díaz
Cari Cesarotti
Blaise Delaney
- [1] Applications of Lipschitz neural networks to the Run 3 LHCb trigger system
Blaise Delaney, Nicole Schulte, Gregory Ciezarek, Niklas Nolte, Mike Williams, Johannes Albrecht
[ arXiv:2312.14265 ]
Daniel Hackett
-
[11] Practical applications of machine-learned flows on gauge fields
Ryan Abbott, Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2404.11674 ] -
[10] Multiscale Normalizing Flows for Gauge Theories
Ryan Abbott, Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2404.10819 ] -
[9] Applications of flow models to the generation of correlated lattice QCD ensembles
Ryan Abbott, Aleksandar Botev, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2401.10874 ] -
[8] Normalizing flows for lattice gauge theory in arbitrary space-time dimension
Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Alexander G.D.G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2305.02402 ] -
[7] Aspects of scaling and scalability for flow-based sampling of lattice QCD
Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Alexander G. D. G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
The European Physical Journal A 2023, Volume 59, Article Number 257 [ arXiv:2211.07541 ] -
[6] Sampling QCD field configurations with gauge-equivariant flow models
Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Alexander G. D. G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2208.03832 ] -
[5] Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions
Ryan Abbott, Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Betsy Tian, Julian M. Urban
Physical REview D, 2022, Volume 106, Issue 7 [ arXiv:2207.08945 ] -
[4] Flow-based sampling in the lattice Schwinger model at criticality
Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
Physical Review D, 2022, Volume 106, Article 014514 [ arXiv:2202.11712 ] -
[3] Flow-based sampling for multimodal distributions in lattice field theory
Daniel C. Hackett, Chung-Chun Hsieh, Michael S. Albergo, Denis Boyda, Jiunn-Wei Chen, Kai-Feng Chen, Kyle Cranmer, Gurtej Kanwar, Phiala E. Shanahan
[ arXiv:2107.00734 ] -
[2] Flow-based sampling for fermionic lattice field theories
Michael S. Albergo, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Julian M. Urban, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Phiala E. Shanahan
Physical Review D, 2021, Vol. 104, Iss. 11 – 1 [ arXiv:2106.05934 ] -
[1] Introduction to Normalizing Flows for Lattice Field Theory
Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Kyle Cranmer, Sébastien Racanière, Danilo Jimenez Rezende, and Phiala E. Shanahan
[ arXiv:2101.08176 ]
Daichi Hiramatsu
-
[4] From Discovery to the First Month of the Type II Supernova 2023ixf: High and Variable Mass Loss in the Final Year before Explosion
Daichi Hiramatsu, Daichi Tsuna, Edo Berger, Koichi Itagaki, Jared A. Goldberg, Sebastian Gomez, Kishalay De, Griffin Hosseinzadeh, K. Azalee Bostroem, Peter J. Brown, Iair Arcavi, Allyson Bieryla, Peter K. Blanchard, Gilbert A. Esquerdo, Joseph Farah, D. Andrew Howell, Tatsuya Matsumoto, Curtis McCully, Megan Newsome, Estefania Padilla Gonzalez, Craig Pellegrino, Jaehyon Rhee, Giacomo Terreran, József Vinkó, J. Craig Wheeler
The Astrophysical Journal Letters 2023, Volume 955, Number 1 [ arXiv:2307.03165 ] -
[3] Multiple Peaks and a Long Precursor in the Type IIn Supernova 2021qqp: An Energetic Explosion in a Complex Circumstellar Environment
Daichi Hiramatsu, Tatsuya Matsumoto, Edo Berger, Conor Ransome, V. Ashley Villar, Sebastian Gomez, Yvette Cendes, Kishalay De, K. Azalee Bostroem, Joseph Farah, D. Andrew Howell, Curtis McCully, Megan Newsome, Estefania Padilla Gonzalez, Craig Pellegrino, Akihiro Suzuki, Giacomo Terreran
The Astrophysical Journal, 2024, Volume 964, Number 2 [ arXiv:2305.11168 ] -
[2] Limits on Simultaneous and Delayed Optical Emission from Well-localized Fast Radio Bursts
Daichi Hiramatsu, Edo Berger, Brian D. Metzger, Sebastian Gomez, Allyson Bieryla, Iair Arcavi, D. Andrew Howell, Ryan Mckinven, Nozomu Tominaga
The Astrophysical Journal Letters 2023, volume 947, number 2 [ arXiv:2211.03974 ] -
[1] The First Two Years of FLEET: an Active Search for Superluminous Supernovae
Sebastian Gomez, Edo Berger, Peter K. Blanchard, Griffin Hosseinzadeh, Matt Nicholl, Daichi Hiramatsu, V. Ashley Villar, Yao Yin
The Astrophysical Journal, 2023, Volume 949, Issue 114 [ arXiv:2210.10811 ]
Samuel Homiller
- [1] Challenges for Unsupervised Anomaly Detection in Particle Physics
Katherine Fraser, Samuel Homiller, Rashmish K. Mishra, Bryan Ostdiek, Matthew D. Schwartz
Journal of High Energy Physics, 2022, Article 66 [ arXiv:2110.06948 ]
Matheus Hostert
- [1] Resonant Neutrino Flavor Conversion in the Atmosphere
Connor Sponsler, Matheus Hostert, Ivan Martinez-Soler, Carlos A. Argüelles
[ arXiv:2405.12140 ]
Harsh Kumar
Yin Lin
- [1] Signal-to-noise improvement through neural network contour deformations for 3D SU(2) lattice gauge theory
William Detmold, Gurtej Kanwar, Yin Lin, Phiala E. Shanahan, Michael L. Wagman
[ arXiv:2309.00600 ]
Rashmish Mishra
Matthew Mould
Nikhil Mukund
- [1] First demonstration of neural sensing and control in a kilometer-scale gravitational wave observatory
Nikhil Mukund, James Lough, Aparna Bisht, Holger Wittel, Séverin Landry Nadji, Christoph Affeldt, Fabio Bergamin, Marc Brinkmann, Volker Kringel, Harald Lück, Michael Weinert, Karsten Danzmann
Physical Review Applied, 2023, Volume 20, Issue 6 [ arXiv:2301.06221 ]
Andrzej Novak
Fernando Romero-Lopez
-
[10] QCD constraints on isospin-dense matter and the nuclear equation of state
Ryan Abbott, William Detmold, Marc Illa, Assumpta Parreño, Robert J. Perry, Fernando Romero-López, Phiala E. Shanahan, Michael L. Wagman
[ arXiv:2406.09273 ] -
[9] Practical applications of machine-learned flows on gauge fields
Ryan Abbott, Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2404.11674 ] -
[8] Multiscale Normalizing Flows for Gauge Theories
Ryan Abbott, Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2404.10819 ] -
[7] Applications of flow models to the generation of correlated lattice QCD ensembles
Ryan Abbott, Aleksandar Botev, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2401.10874 ] -
[6] Constraint of pionless EFT using two-nucleon spectra from lattice QCD
William Detmold, Fernando Romero-López, Phiala E. Shanahan
[ arXiv:2305.06313 ] -
[5] Normalizing flows for lattice gauge theory in arbitrary space-time dimension
Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Alexander G.D.G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2305.02402 ] -
[4] Aspects of scaling and scalability for flow-based sampling of lattice QCD
Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Alexander G. D. G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
The European Physical Journal A 2023, Volume 59, Article Number 257 [ arXiv:2211.07541 ] -
[3] Sampling QCD field configurations with gauge-equivariant flow models
Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Alexander G. D. G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2208.03832 ] -
[2] Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions
Ryan Abbott, Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Betsy Tian, Julian M. Urban
Physical REview D, 2022, Volume 106, Issue 7 [ arXiv:2207.08945 ] -
[1] Flow-based sampling in the lattice Schwinger model at criticality
Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
Physical Review D, 2022, Volume 106, Article 014514 [ arXiv:2202.11712 ]
Christina Reissel
Matthew Rosenberg
Carlos Sarasty
Sokratis Trifinopoulos
-
[2] From Neurons to Neutrons: A Case Study in Interpretability
Ouail Kitouni, Niklas Nolte, Víctor Samuel Pérez-Díaz, Sokratis Trifinopoulos, Mike Williams
[ arXiv:2405.17425 ] -
[1] NuCLR, Nuclear Co-Learned Representations
Ouail Kitouni, Niklas Nolte, Sokratis Trifinopoulos, Subhash Kantamneni, Mike Williams
[ arXiv:2306.06099 ]
Julian Urban
Georgios Valogiannis
-
[5] Precise Cosmological Constraints from BOSS Galaxy Clustering with a Simulation-Based Emulator of the Wavelet Scattering Transform
Georgios Valogiannis, Sihan Yuan, Cora Dvorkin
Physical Review D 2024, Volume 109, Issue 10 [ arXiv:2310.16116 ] -
[4] Cosmological constraints from density-split clustering in the BOSS CMASS galaxy sample
Enrique Paillas, Carolina Cuesta-Lazaro, Will J. Percival, Seshadri Nadathur, Yan-Chuan Cai, Sihan Yuan, Florian Beutler, Arnaud de Mattia, Daniel Eisenstein, Daniel Forero-Sanchez, Nelson Padilla, Mathilde Pinon, Vanina Ruhlmann-Kleider, Ariel G. Sánchez, Georgios Valogiannis, Pauline Zarrouk
[ arXiv:2309.16541 ] -
[3] SUNBIRD: A simulation-based model for full-shape density-split clustering
Carolina Cuesta-Lazaro, Enrique Paillas, Sihan Yuan, Yan-Chuan Cai, Seshadri Nadathur, Will J. Percival, Florian Beutler, Arnaud de Mattia, Daniel Eisenstein, Daniel Forero-Sanchez, Nelson Padilla, Mathilde Pinon, Vanina Ruhlmann-Kleider, Ariel G. Sánchez, Georgios Valogiannis, Pauline Zarrouk
[ arXiv:2309.16539 ] -
[2] Going Beyond the Galaxy Power Spectrum: an Analysis of BOSS Data with Wavelet Scattering Transforms
Georgios Valogiannis, Cora Dvorkin
Physical Review D, 2022, Volume 106, Article 103509 [ arXiv:2204.13717 ] -
[1] Towards an Optimal Estimation of Cosmological Parameters with the Wavelet Scattering Transform
Georgios Valogiannis, Cora Dvorkin
Physical Review D, 2022, 105, 103534 [ arXiv:2108.07821 ]
Students
Ryan Abbott
-
[8] QCD constraints on isospin-dense matter and the nuclear equation of state
Ryan Abbott, William Detmold, Marc Illa, Assumpta Parreño, Robert J. Perry, Fernando Romero-López, Phiala E. Shanahan, Michael L. Wagman
[ arXiv:2406.09273 ] -
[7] Practical applications of machine-learned flows on gauge fields
Ryan Abbott, Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2404.11674 ] -
[6] Multiscale Normalizing Flows for Gauge Theories
Ryan Abbott, Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2404.10819 ] -
[5] Applications of flow models to the generation of correlated lattice QCD ensembles
Ryan Abbott, Aleksandar Botev, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2401.10874 ] -
[4] Normalizing flows for lattice gauge theory in arbitrary space-time dimension
Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Alexander G.D.G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2305.02402 ] -
[3] Aspects of scaling and scalability for flow-based sampling of lattice QCD
Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Alexander G. D. G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
The European Physical Journal A 2023, Volume 59, Article Number 257 [ arXiv:2211.07541 ] -
[2] Sampling QCD field configurations with gauge-equivariant flow models
Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Alexander G. D. G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2208.03832 ] -
[1] Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions
Ryan Abbott, Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Betsy Tian, Julian M. Urban
Physical REview D, 2022, Volume 106, Issue 7 [ arXiv:2207.08945 ]
Polina Abratenko
Jacob Adamczyk
- [1] Boosting Soft Q-Learning by Bounding
Jacob Adamczyk, Volodymyr Makarenko, Stas Tiomkin, Rahul V. Kulkarni
[ arXiv:2406.18033 ]
Aizhan Akhmetzhanova
- [1] Data Compression and Inference in Cosmology with Self-Supervised Machine Learning
Aizhan Akhmetzhanova, Siddharth Mishra-Sharma, Cora Dvorkin
Monthly Notices of the Royal Astronomical Society, 2023, Volume 527, Issue 3 [ arXiv:2308.09751 ]
Omar Alterkait
Samuel Alipour-fard
Oscar Barrera
Sean Benevedes
Kiara Carloni
Chandrika Chandrashekar
Zhuo Chen
-
[5] QuanTA: Efficient High-Rank Fine-Tuning of LLMs with Quantum-Informed Tensor Adaptation
Zhuo Chen, Rumen Dangovski, Charlotte Loh, Owen Dugan, Di Luo, Marin Soljačić
[ arXiv:2406.00132 ] -
[4] TENG: Time-Evolving Natural Gradient for Solving PDEs with Deep Neural Net
Zhuo Chen, Jacob McCarran, Esteban Vizcaino, Marin Soljačić, Di Luo
Open Review, Submission Number 10038 [ arXiv:2404.10771 ] -
[3] Photonic probabilistic machine learning using quantum vacuum noise
Seou Choi, Yannick Salamin, Charles Roques-Carmes, Rumen Dangovski, Di Luo, Zhuo Chen, Michael Horodynski, Jamison Sloan, Shiekh Zia Uddin, Marin Soljacic
[ arXiv:2403.04731 ] -
[2] ANTN: Bridging Autoregressive Neural Networks and Tensor Networks for Quantum Many-Body Simulation
Zhuo Chen, Laker Newhouse, Eddie Chen, Di Luo, Marin Soljačić
[ arXiv:2304.01996 ] -
[1] Simulating 2+1D Lattice Quantum Electrodynamics at Finite Density with Neural Flow Wavefunctions
Zhuo Chen, Di Luo, Kaiwen Hu, Bryan K. Clark
[ arXiv:2212.06835 ]
Ameya Shrikant Daigavane
-
[2] Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for Molecule Generation
Ameya Daigavane, Song Kim, Mario Geiger, Tess Smidt
[ arXiv:2311.16199 ] -
[1] Learning Integrable Dynamics with Action-Angle Networks
Ameya Daigavane, Arthur Kosmala, Miles Cranmer, Tess Smidt, Shirley Ho
[ arXiv:2211.15338 ]
Rumen Dangovski
-
[12] QuanTA: Efficient High-Rank Fine-Tuning of LLMs with Quantum-Informed Tensor Adaptation
Zhuo Chen, Rumen Dangovski, Charlotte Loh, Owen Dugan, Di Luo, Marin Soljačić
[ arXiv:2406.00132 ] -
[11] Photonic probabilistic machine learning using quantum vacuum noise
Seou Choi, Yannick Salamin, Charles Roques-Carmes, Rumen Dangovski, Di Luo, Zhuo Chen, Michael Horodynski, Jamison Sloan, Shiekh Zia Uddin, Marin Soljacic
[ arXiv:2403.04731 ] -
[10] Mitigating Confirmation Bias in Semi-supervised Learning via Efficient Bayesian Model Averaging
Charlotte Loh, Rumen Dangovski, Shivchander Sudalairaj, Seungwook Han, Ligong Han, Leonid Karlinsky, Marin Soljacic, Akash Srivastava
Transactions on Machine Learning Research 2023, Submission number 1013 [ | code ] -
[9] Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries
Charlotte Loh, Seungwook Han, Shivchander Sudalairaj, Rumen Dangovski, Kai Xu, Florian Wenzel, Marin Soljacic, Akash Srivastava
[ arXiv:2303.02484 ] -
[8] Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows
Owen Dugan, Peter Y. Lu, Rumen Dangovski, Di Luo, Marin Soljačić
[ arXiv:2302.12235 ] -
[7] QuACK: Accelerating Gradient-Based Quantum Optimization with Koopman Operator Learning
Di Luo, Jiayu Shen, Rumen Dangovski, Marin Soljačić
[ arXiv:2211.01365 ] -
[6] Data-driven Acceleration of Quantum Optimization and Machine Learning via Koopman Operator Learning
Di Luo, Jiayu Shen, Rumen Dangovski, Marin Soljacic
NeurIPS 2022 Workshop AI4Science [ ] -
[5] On the Importance of Calibration in Semi-supervised Learning
Charlotte Loh, Rumen Dangovski, Shivchander Sudalairaj, Seungwook Han, Ligong Han, Leonid Karlinsky, Marin Soljacic, Akash Srivastava
[ arXiv:2210.04783 ] -
[4] Discovering Conservation Laws using Optimal Transport and Manifold Learning
Peter Y. Lu, Rumen Dangovski, Marin Soljačić
Nature Communications [ arXiv:2208.14995 ] -
[3] DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings
Yung-Sung Chuang, Rumen Dangovski, Hongyin Luo, Yang Zhang, Shiyu Chang, Marin Soljačić, Shang-Wen Li, Wen-tau Yin, Yoon Kim, James Glass
[ arXiv:2204.10298 ] -
[2] Equivariant Contrastive Learning
Rumen Dangovski, Li Jing, Charlotte Loh, Seungwook Han, Akash Srivastava, Brian Cheung, Pulkit Agrawal, Marin Soljačić
[ arXiv:2111.00899 ] -
[1] Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science
Charlotte Loh, Thomas Christensen, Rumen Dangovski, Samuel Kim, Marin Soljačić
Nature Communications, 2022, Volume 13, Article 4223 [ arXiv:2110.08406 ]
Aurélien Dersy
-
[2] Reconstructing S-matrix Phases with Machine Learning
Aurélien Dersy, Matthew D. Schwartz, Alexander Zhiboedov
[ arXiv:2308.09451 ] -
[1] Simplifying Polylogarithms with Machine Learning
Aurélien Dersy, Matthew D. Schwartz, Xiaoyuan Zhang
[ arXiv:2206.04115 ]
Gokhan Egri
Atakan Hilmi Firat
Andre Grossi Fonseca
Katherine Fraser
-
[2] Challenges for Unsupervised Anomaly Detection in Particle Physics
Katherine Fraser, Samuel Homiller, Rashmish K. Mishra, Bryan Ostdiek, Matthew D. Schwartz
Journal of High Energy Physics, 2022, Article 66 [ arXiv:2110.06948 ] -
[1] Parameter Inference from Event Ensembles and the Top-Quark Mass
Forrest Flesher, Katherine Fraser, Charles Hutchison, Bryan Ostdiek, Matthew D. Schwartz
Journal of High Energy Physics, 2021, Article 58 [ arXiv:2011.04666 ]
Rikab Gambhir
-
[5] Moments of Clarity: Streamlining Latent Spaces in Machine Learning using Moment Pooling
Rikab Gambhir, Athis Osathapan, Jesse Thaler
[ arXiv:2403.08854 ] -
[4] Seeing Double: Calibrating Two Jets at Once
Rikab Gambhir, Benjamin Nachman
[ arXiv:2402.14067 ] -
[3] SHAPER: Can You Hear the Shape of a Jet?
Demba Ba, Akshunna S. Dogra, Rikab Gambhir, Abiy Tasissa, Jesse Thaler
Journal of High Energy Physics, 2023, Volume 2023, Article 195 [ arXiv:2302.12266 ] -
[2] Bias and Priors in Machine Learning Calibrations for High Energy Physics
Rikab Gambhir, Benjamin Nachman, Jesse Thaler
Physical Review D, Volume 106, Article 036011 [ arXiv:2205.05084 ] -
[1] Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics
Rikab Gambhir, Benjamin Nachman, Jesse Thaler
Physical Review Letters, 2022, Volume 129, Article 082001 [ arXiv:2205.03413 ]
Ali Ghorashi
Mark Hamilton
-
[3] Separating the ‘Chirp’ from the ‘Chat’: Self-supervised Visual Grounding of Sound and Language
Mark Hamilton, Andrew Zisserman, John R. Hershey, William T. Freeman
[ arXiv:2406.05629 ] -
[2] FeatUp: A Model-Agnostic Framework for Features at Any Resolution
Stephanie Fu, Mark Hamilton, Laura Brandt, Axel Feldman, Zhoutong Zhang, William T. Freeman
[ arXiv:2403.10516 ] -
[1] Unsupervised Semantic Segmentation by Distilling Feature Correspondences
Mark Hamilton, Zhoutong Zhang, Bharath Hariharan, Noah Snavely, William T. Freeman
[ arXiv:2203.08414 ]
Dean Hazineh
- [1] Polarization Multi-Image Synthesis with Birefringent Metasurfaces
Dean Hazineh, Soon Wei Daniel Lim, Qi Guo, Federico Capasso, Todd Zickler
[ arXiv:2307.08106 ]
Duc Hoang
-
[2] hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices
Farah Fahim, Benjamin Hawks, Christian Herwig, James Hirschauer, Sergo Jindariani, Nhan Tran, Luca P. Carloni, Giuseppe Di Guglielmo, Philip Harris, Jeffrey Krupa, Dylan Rankin, Manuel Blanco Valentin, Josiah Hester, Yingyi Luo, John Mamish, Seda Orgrenci-Memik, Thea Aarrestad, Hamza Javed, Vladimir Loncar, Maurizio Pierini, Adrian Alan Pol, Sioni Summers, Javier Duarte, Scott Hauck, Shih-Chieh Hsu, Jennifer Ngadiuba, Mia Liu, Duc Hoang, Edward Kreinar, Zhenbin Wu
[ arXiv:2103.05579 ] -
[1] Fast convolutional neural networks on FPGAs with hls4ml
Thea Aarrestad, Vladimir Loncar, Nicolò Ghielmetti, Maurizio Pierini, Sioni Summers, Jennifer Ngadiuba, Christoffer Petersson, Hampus Linander, Yutaro Iiyama, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Dylan Rankin, Sergo Jindariani, Kevin Pedro, Nhan Tran, Mia Liu, Edward Kreinar, Zhenbin Wu, Duc Hoang
Machine Learning Science and Technology, 2021, Volume 2, Issue 4, Article 045015 [ arXiv:2101.05108 ]
Elyssa Hofgard
- [1] Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolution
Rui Wang, Elyssa Hofgard, Han Gao, Robin Walters, Tess E. Smidt
[ arXiv:2310.02299 ]
Zhang-Wei Hong
Maryam Hussaini
Zev Imani
- [1] Score-based Diffusion Models for Generating Liquid Argon Time Projection Chamber Images
Zeviel Imani, Shuchin Aeron, Taritree Wongjirad
[ arXiv:2307.13687 ]
Gurtej Kanwar
-
[14] Practical applications of machine-learned flows on gauge fields
Ryan Abbott, Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2404.11674 ] -
[13] Multiscale Normalizing Flows for Gauge Theories
Ryan Abbott, Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2404.10819 ] -
[12] Applications of flow models to the generation of correlated lattice QCD ensembles
Ryan Abbott, Aleksandar Botev, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2401.10874 ] -
[11] Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics
Kyle Cranmer, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Phiala E. Shanahan
Nature Reviews Physics, 2023, Volume 5 [ arXiv:2309.01156 ] -
[10] Signal-to-noise improvement through neural network contour deformations for 3D SU(2) lattice gauge theory
William Detmold, Gurtej Kanwar, Yin Lin, Phiala E. Shanahan, Michael L. Wagman
[ arXiv:2309.00600 ] -
[9] Normalizing flows for lattice gauge theory in arbitrary space-time dimension
Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Alexander G.D.G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2305.02402 ] -
[8] Sampling QCD field configurations with gauge-equivariant flow models
Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Alexander G. D. G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2208.03832 ] -
[7] Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions
Ryan Abbott, Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Betsy Tian, Julian M. Urban
Physical REview D, 2022, Volume 106, Issue 7 [ arXiv:2207.08945 ] -
[6] Flow-based sampling in the lattice Schwinger model at criticality
Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
Physical Review D, 2022, Volume 106, Article 014514 [ arXiv:2202.11712 ] -
[5] Real-time lattice gauge theory actions: unitarity, convergence, and path integral contour deformations
Gurtej Kanwar, Michael L. Wagman
Physical Review D, Volume 104, Article 014513 [ arXiv:2103.02602 ] -
[4] Flow-based sampling for multimodal distributions in lattice field theory
Daniel C. Hackett, Chung-Chun Hsieh, Michael S. Albergo, Denis Boyda, Jiunn-Wei Chen, Kai-Feng Chen, Kyle Cranmer, Gurtej Kanwar, Phiala E. Shanahan
[ arXiv:2107.00734 ] -
[3] Flow-based sampling for fermionic lattice field theories
Michael S. Albergo, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Julian M. Urban, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Phiala E. Shanahan
Physical Review D, 2021, Vol. 104, Iss. 11 – 1 [ arXiv:2106.05934 ] -
[2] Path integral contour deformations for observables in SU(N) gauge theory
William Detmold, Gurtej Kanwar, Henry Lamm, Michael L. Wagman, Neill C. Warrington
Physical Review D, 2021, Vol. 103, Issue 9, Article 094517 [ arXiv:2101.12668 ] -
[1] Introduction to Normalizing Flows for Lattice Field Theory
Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Kyle Cranmer, Sébastien Racanière, Danilo Jimenez Rezende, and Phiala E. Shanahan
[ arXiv:2101.08176 ]
Ouail Kitouni
-
[7] From Neurons to Neutrons: A Case Study in Interpretability
Ouail Kitouni, Niklas Nolte, Víctor Samuel Pérez-Díaz, Sokratis Trifinopoulos, Mike Williams
[ arXiv:2405.17425 ] -
[6] NuCLR, Nuclear Co-Learned Representations
Ouail Kitouni, Niklas Nolte, Sokratis Trifinopoulos, Subhash Kantamneni, Mike Williams
[ arXiv:2306.06099 ] -
[5] Expressive Monotonic Neural Networks
Niklas Nolte, Ouail Kitouni, Mike Williams
International Conference on Learning Representations 2023 [ ] -
[4] Finding NEEMo: Geometric Fitting using Neural Estimation of the Energy Mover’s Distance
Ouail Kitouni, Niklas Nolte, Mike Williams
[ arXiv:2209.15624 ] -
[3] Towards Understanding Grokking: An Effective Theory of Representation Learning
Ziming Liu, Ouail Kitouni, Niklas Nolte, Eric J. Michaud, Max Tegmark, Mike Williams
[ arXiv:2205.10343 ] -
[2] Robust and Provably Motonic Networks
Ouail Kitouni, Niklas Nolte, Mike Williams
Fourth Workshop on Machine Learning and the Physical Sciences (NeurIPS 2021) Proceedings, [ arXiv:2112.00038 ] -
[1] Enhancing searches for resonances with machine learning and moment decomposition
Ouail Kitouni, Benjamin Nachman, Constantin Weisser, and Mike Williams
Journal of High Energy Physics, 2021, Article 70 [ arXiv:2010.09745 | code ]
Samuel Kim
-
[4] Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure
Samuel Kim, Peter Y. Lu, Charlotte Loh, Jamie Smith, Jasper Snoek, Marin Soljačić
Transactions on Machine Learning Research 2022 [ ] -
[3] Deep Learning and Symbolic Regression for Discovering Parametric Equations
Michael Zhang, Samuel Kim, Peter Y. Lu, Marin Soljačić
IEEE Journals, 2023, PubMed ID 37721885 [ arXiv:2207.00529 ] -
[2] Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science
Charlotte Loh, Thomas Christensen, Rumen Dangovski, Samuel Kim, Marin Soljačić
Nature Communications, 2022, Volume 13, Article 4223 [ arXiv:2110.08406 ] -
[1] Scalable and Flexible Deep Bayesian Optimization with Auxiliary Information for Scientific Problems
Samuel Kim, Peter Y. Lu, Charlotte Loh, Jamie Smith, Jasper Snoek, Marin Soljačić
Transactions on Machine Learning Research, September 2022 [ arXiv:2104.11667 ]
Mit Kotak
Jeffrey Krupa
-
[3] Re-Simulation-based Self-Supervised Learning for Pre-Training Foundation Models
Philip Harris, Michael Kagan, Jeffrey Krupa, Benedikt Maier, Nathaniel Woodward
[ arXiv:2403.07066 ] -
[2] Hardware-accelerated Inference for Real-Time Gravitational-Wave Astronomy
Alec Gunny, Dylan Rankin, Jeffrey Krupa, Muhammed Saleem, Tri Nguyen, Michael Coughlin, Philip Harris, Erik Katsavounidis, Steven Timm, Burt Holzman
[ arXiv:2108.12430 ] -
[1] hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices
Farah Fahim, Benjamin Hawks, Christian Herwig, James Hirschauer, Sergo Jindariani, Nhan Tran, Luca P. Carloni, Giuseppe Di Guglielmo, Philip Harris, Jeffrey Krupa, Dylan Rankin, Manuel Blanco Valentin, Josiah Hester, Yingyi Luo, John Mamish, Seda Orgrenci-Memik, Thea Aarrestad, Hamza Javed, Vladimir Loncar, Maurizio Pierini, Adrian Alan Pol, Sioni Summers, Javier Duarte, Scott Hauck, Shih-Chieh Hsu, Jennifer Ngadiuba, Mia Liu, Duc Hoang, Edward Kreinar, Zhenbin Wu
[ arXiv:2103.05579 ]
Zhaoyi Li
Ziming Liu
-
[26] Survival of the Fittest Representation: A Case Study with Modular Addition
Xiaoman Delores Ding, Zifan Carl Guo, Eric J. Michaud, Ziming Liu, Max Tegmark
[ arXiv:2405.17420 ] -
[25] How Do Transformers ‘Do’ Physics? Investigating the Simple Harmonic Oscillator
Subhash Kantamneni, Ziming Liu, Max Tegmark
[ arXiv:2405.17209 ] -
[24] OptPDE: Discovering Novel Integrable Systems via AI-Human Collaboration
Subhash Kantamneni, Ziming Liu, Max Tegmark
[ arXiv:2405.04484 ] -
[23] KAN: Kolmogorov-Arnold Networks
Ziming Liu, Yixuan Wang, Sachin Vaidya, Fabian Ruehle, James Halverson, Marin Soljačić, Thomas Y. Hou, Max Tegmark
[ arXiv:2404.19756 ] -
[22] A Resource Model For Neural Scaling Law
Jinyeop Song, Ziming Liu, Max Tegmark, Jeff Gore
[ arXiv:2402.05164 ] -
[21] Opening the AI black box: program synthesis via mechanistic interpretability
Eric J. Michaud, Isaac Liao, Vedang Lad, Ziming Liu, Anish Mudide, Chloe Loughridge, Zifan Carl Guo, Tara Rezaei Kheirkhah, Mateja Vukelić, Max Tegmark
[ arXiv:2402.05110 ] -
[20] Generating Interpretable Networks using Hypernetworks
Isaac Liao, Ziming Liu, Max Tegmark
[ arXiv:2312.03051 ] -
[19] Growing Brains in Recurrent Neural Networks for Multiple Cognitive Tasks
Ziming Liu, Mikail Khona, Ila Fiete, Max Tegmark
NeurIPS 2023 Workshop NeurReps [ ] -
[18] Growing Brains: Co-emergence of Anatomical and Functional Modularity in Recurrent Neural Networks
Ziming Liu, Mikail Khona, Ila R. Fiete, Max Tegmark
[ arXiv:2310.07711 ] -
[17] Grokking as Compression: A Nonlinear Complexity Perspective
Ziming Liu, Ziqian Zhong, Max Tegmark
[ arXiv:2310.05918 ] -
[16] A Neural Scaling Law from Lottery Ticket Ensembling
Ziming Liu, Max Tegmark
[ arXiv:2310.02258 ] -
[15] The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks
Ziqian Zhong, Ziming Liu, Max Tegmark, Jacob Andreas
[ arXiv:2306.17844 ] -
[14] Discovering New Interpretable Conservation Laws as Sparse Invariants
Ziming Liu, Patrick Obin Sturm, Saketh Bharadwaj, Sam Silva, Max Tegmark
[ arXiv:2305.19525 ] -
[13] Seeing is Believing: Brain-Inspired Modular Training for Mechanistic Interpretability
Ziming Liu, Eric Gan, Max Tegmark
[ arXiv:2305.08746 ] -
[12] GenPhys: From Physical Processes to Generative Models
Ziming Liu, Di Luo, Yilun Xu, Tommi Jaakkola, Max Tegmark
[ arXiv:2304.02637 ] -
[11] The Quantization Model of Neural Scaling
Eric J. Michaud, Ziming Liu, Uzay Girit, Max Tegmark
[ arXiv:2303.13506 ] -
[10] PFGM++: Unlocking the Potential of Physics-Inspired Generative Models
Yilun Xu, Ziming Liu, Yonglong Tian, Shangyuan Tong, Max Tegmark, Tommi Jaakkola
[ arXiv:2302.04265 ] -
[9] Precision Machine Learning
Eric J. Michaud, Ziming Liu, Max Tegmark
Entropy, 2023, 25(1) [ arXiv:2210.13447 ] -
[8] Omnigrok: Grokking Beyond Algorithmic Data
Ziming Liu, Eric J. Michaud, Max Tegmark
[ arXiv:2210.01117 ] -
[7] Poisson Flow Generative Models
Yilun Xu, Ziming Liu, Max Tegmark, Tommi Jaakkola
[ arXiv:2209.11178 | code ] -
[6] Towards Understanding Grokking: An Effective Theory of Representation Learning
Ziming Liu, Ouail Kitouni, Niklas Nolte, Eric J. Michaud, Max Tegmark, Mike Williams
[ arXiv:2205.10343 ] -
[5] AI Poincaré 2.0: Machine Learning Conservation Laws from Differential Equations
Ziming Liu, Varun Madhavan, Max Tegmark
Physical Review E, 2022, Volume 106, Article 045307 [ arXiv:2203.12610 ] -
[4] Physics-Augmented Learning: A New Paradigm Beyond Physics-Informed Learning
Ziming Liu, Yunyue Chen, Yuanqi Du, Max Tegmark
[ arXiv:2109.13901 ] -
[3] Machine-learning hidden symmetries
Ziming Liu, Max Tegmark
Physical Review Letters, 2022, 128, 180201 [ arXiv:2109.09721 ] -
[2] Machine-Learning Non-Conservative Dynamics for New-Physics Detection
Ziming Liu, Bohan Wang, Qi Meng, Wei Chen, Max Tegmark, Tie-Yan Liu
Physical Review E, 2021, Vol. 104, Article 055302 [ arXiv:2106.00026 ] -
[1] AI Poincaré: Machine Learning Conservation Laws from Trajectories
Ziming Liu and Max Tegmark
Physical Review Letters, 2021, Volume 126, Issue 18, Article 180604 [ arXiv:2011.04698 ]
Charlotte Loh
-
[8] QuanTA: Efficient High-Rank Fine-Tuning of LLMs with Quantum-Informed Tensor Adaptation
Zhuo Chen, Rumen Dangovski, Charlotte Loh, Owen Dugan, Di Luo, Marin Soljačić
[ arXiv:2406.00132 ] -
[7] Mitigating Confirmation Bias in Semi-supervised Learning via Efficient Bayesian Model Averaging
Charlotte Loh, Rumen Dangovski, Shivchander Sudalairaj, Seungwook Han, Ligong Han, Leonid Karlinsky, Marin Soljacic, Akash Srivastava
Transactions on Machine Learning Research 2023, Submission number 1013 [ | code ] -
[6] Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries
Charlotte Loh, Seungwook Han, Shivchander Sudalairaj, Rumen Dangovski, Kai Xu, Florian Wenzel, Marin Soljacic, Akash Srivastava
[ arXiv:2303.02484 ] -
[5] Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure
Samuel Kim, Peter Y. Lu, Charlotte Loh, Jamie Smith, Jasper Snoek, Marin Soljačić
Transactions on Machine Learning Research 2022 [ ] -
[4] On the Importance of Calibration in Semi-supervised Learning
Charlotte Loh, Rumen Dangovski, Shivchander Sudalairaj, Seungwook Han, Ligong Han, Leonid Karlinsky, Marin Soljacic, Akash Srivastava
[ arXiv:2210.04783 ] -
[3] Equivariant Contrastive Learning
Rumen Dangovski, Li Jing, Charlotte Loh, Seungwook Han, Akash Srivastava, Brian Cheung, Pulkit Agrawal, Marin Soljačić
[ arXiv:2111.00899 ] -
[2] Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science
Charlotte Loh, Thomas Christensen, Rumen Dangovski, Samuel Kim, Marin Soljačić
Nature Communications, 2022, Volume 13, Article 4223 [ arXiv:2110.08406 ] -
[1] Scalable and Flexible Deep Bayesian Optimization with Auxiliary Information for Scientific Problems
Samuel Kim, Peter Y. Lu, Charlotte Loh, Jamie Smith, Jasper Snoek, Marin Soljačić
Transactions on Machine Learning Research, September 2022 [ arXiv:2104.11667 ]
Andrew Ma
- [1] Topogivity: A Machine-Learned Chemical Rule for Discovering Topological Materials
Andrew Ma, Yang Zhang, Thomas Christensen, Hoi Chun Po, Li Jing, Liang Fu, Marin Soljačić
American Chemical Society Publications [ arXiv:2202.05255 ]
Silviu-Marian Udrescu
-
[3] The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics
Gregor Kasieczka (ed), Benjamin Nachman (ed), David Shih (ed), Oz Amram, Anders Andreassen, Kees Benkendorfer, Blaz Bortolato, Gustaaf Brooijmans, Florencia Canelli, Jack H. Collins, Biwei Dai, Felipe F. De Freitas, Barry M. Dillon, Ioan-Mihail Dinu, Zhongtian Dong, Julien Donini, Javier Duarte, D. A. Faroughy, Julia Gonski, Philip Harris, Alan Kahn, Jernej F. Kamenik, Charanjit K. Khosa, Patrick Komiske, Luc Le Pottier, Pablo Martín-Ramiro, Andrej Matevc, Eric Metodiev, Vinicius Mikuni, Inês Ochoa, Sang Eon Park, Maurizio Pierini, Dylan Rankin, Veronica Sanz, Nilai Sarda, Urous Seljak, Aleks Smolkovic, George Stein, Cristina Mantilla Suarez, Manuel Szewc, Jesse Thaler, Steven Tsan, Silviu-Marian Udrescu, Louis Vaslin, Jean-Roch Vlimant, Daniel Williams, Mikaeel Yunus
Reports on Progress in Physics, 2021, Volume 84, Number 12 [ arXiv:2101.08320 ] -
[2] Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge
Sang Eon Park, Dylan Rankin, Silviu-Marian Udrescu, Mikaeel Yunus, Philip Harris
Journal of High Energy Physics, 2021, Article 30 [ arXiv:2011.03550 | code ] -
[1] AI Feynman: a Physics-Inspired Method for Symbolic Regression
Silviu-Marian Udrescu, Max Tegmark
Sciences Advances, 2020, 6:easy2631 [ arXiv:1905.11481 ]
John Martyn
-
[2] Variational Neural-Network Ansatz for Continuum Quantum Field Theory
John M. Martyn, Khadijeh Najafi, Di Luo
APS Journals 2023, Volume 131, Issue 8 [ arXiv:2212.00782 ] -
[1] Biological error correction codes generate fault-tolerant neural networks
Alexander Zlokapa, Andrew K. Tan, John M. Martyn, Max Tegmark, Isaac L. Chuang
[ arXiv:2202.12887 ]
Ethan Marx
- [1] GWAK: Gravitational-Wave Anomalous Knowledge with Recurrent Autoencoders
Ryan Raikman, Eric A. Moreno, Ekaterina Govorkova, Ethan J Marx, Alec Gunny, William Benoit, Deep Chatterjee, Rafia Omer, Muhammed Saleem, Dylan S Rankin, Michael W Coughlin, Philip C Harris, Erik Katsavounidis
Journal of High Energy Physics 2024, Volume 2024, Article number 158 [ arXiv:2309.11537 ]
Trevor McCourt
- [1] Noisy dynamical systems evolve error correcting codes and modularity
Trevor McCourt, Ila R. Fiete, Isaac L. Chuang
[ arXiv:2303.14448 ]
Eric Michaud
-
[7] Survival of the Fittest Representation: A Case Study with Modular Addition
Xiaoman Delores Ding, Zifan Carl Guo, Eric J. Michaud, Ziming Liu, Max Tegmark
[ arXiv:2405.17420 ] -
[6] Not All Language Model Features Are Linear
Joshua Engels, Isaac Liao, Eric J. Michaud, Wes Gurnee, Max Tegmark
[ arXiv:2405.14860 ] -
[5] Opening the AI black box: program synthesis via mechanistic interpretability
Eric J. Michaud, Isaac Liao, Vedang Lad, Ziming Liu, Anish Mudide, Chloe Loughridge, Zifan Carl Guo, Tara Rezaei Kheirkhah, Mateja Vukelić, Max Tegmark
[ arXiv:2402.05110 ] -
[4] The Quantization Model of Neural Scaling
Eric J. Michaud, Ziming Liu, Uzay Girit, Max Tegmark
[ arXiv:2303.13506 ] -
[3] Precision Machine Learning
Eric J. Michaud, Ziming Liu, Max Tegmark
Entropy, 2023, 25(1) [ arXiv:2210.13447 ] -
[2] Omnigrok: Grokking Beyond Algorithmic Data
Ziming Liu, Eric J. Michaud, Max Tegmark
[ arXiv:2210.01117 ] -
[1] Towards Understanding Grokking: An Effective Theory of Representation Learning
Ziming Liu, Ouail Kitouni, Niklas Nolte, Eric J. Michaud, Max Tegmark, Mike Williams
[ arXiv:2205.10343 ]
Eric Moreno
- [1] GWAK: Gravitational-Wave Anomalous Knowledge with Recurrent Autoencoders
Ryan Raikman, Eric A. Moreno, Ekaterina Govorkova, Ethan J Marx, Alec Gunny, William Benoit, Deep Chatterjee, Rafia Omer, Muhammed Saleem, Dylan S Rankin, Michael W Coughlin, Philip C Harris, Erik Katsavounidis
Journal of High Energy Physics 2024, Volume 2024, Article number 158 [ arXiv:2309.11537 ]
Nayantara Mudur
-
[6] Diffusion-HMC: Parameter Inference with Diffusion Model driven Hamiltonian Monte Carlo
Nayantara Mudur, Carolina Cuesta-Lazaro, Douglas P. Finkbeiner
[ arXiv:2405.05255 ] -
[5] Debiasing with Diffusion: Probabilistic reconstruction of Dark Matter fields from galaxies with CAMELS
Victoria Ono, Core Francisco Park, Nayantara Mudur, Yueying Ni, Carolina Cuesta-Lazaro, Francisco Villaescusa-Navarro
[ arXiv:2403.10648 ] -
[4] Cosmological Field Emulation and Parameter Inference with Diffusion Models
Nayantara Mudur, Carolina Cuesta-Lazaro, Douglas P. Finkbeiner
[ arXiv:2312.07534 ] -
[3] Probabilistic reconstruction of Dark Matter fields from biased tracers using diffusion models
Core Francisco Park, Victoria Ono, Nayantara Mudur, Yueying Ni, Carolina Cuesta-Lazaro
[ arXiv:2311.08558 ] -
[2] Stellar Reddening Based Extinction Maps for Cosmological Applications
Nayantara Mudur, Core Francisco Park, Douglas P Finkbeiner
The Astrophysical Journal, 2023, Volume 949, Number 2 [ arXiv:2212.04514 ] -
[1] Can denoising diffusion probabilistic models generate realistic astrophysical fields?
Nayantara Mudur, Douglas P. Finkbeiner
[ arXiv:2211.12444 ]
Joydeep Naskar
Aviv Netanyahu
Tri Nguyen
-
[2] FLORAH: A generative model for halo assembly histories
Tri Nguyen, Chirag Modi, L. Y. Aaron Yung, Rachel S. Somerville
[ arXiv:2308.05145 ] -
[1] Hardware-accelerated Inference for Real-Time Gravitational-Wave Astronomy
Alec Gunny, Dylan Rankin, Jeffrey Krupa, Muhammed Saleem, Tri Nguyen, Michael Coughlin, Philip Harris, Erik Katsavounidis, Steven Timm, Burt Holzman
[ arXiv:2108.12430 ]
Noah Paladino
Sneh Pandya
-
[2] Learning Galaxy Intrinsic Alignment Correlations
Sneh Pandya, Yuanyuan Yang, Nicholas Van Alfen, Jonathan Blazek, Robin Walters
[ arXiv:2404.13702 ] -
[1] E(2) Equivariant Neural Networks for Robust Galaxy Morphology Classification
Sneh Pandya, Purvik Patel, Franc O, Jonathan Blazek
[ arXiv:2311.01500 | code ]
Sangeon Park
-
[3] Neural Embedding: Learning the Embedding of the Manifold of Physics Data
Sang Eon Park, Philip Harris, Bryan Ostdiek
Journal of High Energy Physics, 2023, Volume 2023, Article 108 [ arXiv:2208.05484 ] -
[2] The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics
Gregor Kasieczka (ed), Benjamin Nachman (ed), David Shih (ed), Oz Amram, Anders Andreassen, Kees Benkendorfer, Blaz Bortolato, Gustaaf Brooijmans, Florencia Canelli, Jack H. Collins, Biwei Dai, Felipe F. De Freitas, Barry M. Dillon, Ioan-Mihail Dinu, Zhongtian Dong, Julien Donini, Javier Duarte, D. A. Faroughy, Julia Gonski, Philip Harris, Alan Kahn, Jernej F. Kamenik, Charanjit K. Khosa, Patrick Komiske, Luc Le Pottier, Pablo Martín-Ramiro, Andrej Matevc, Eric Metodiev, Vinicius Mikuni, Inês Ochoa, Sang Eon Park, Maurizio Pierini, Dylan Rankin, Veronica Sanz, Nilai Sarda, Urous Seljak, Aleks Smolkovic, George Stein, Cristina Mantilla Suarez, Manuel Szewc, Jesse Thaler, Steven Tsan, Silviu-Marian Udrescu, Louis Vaslin, Jean-Roch Vlimant, Daniel Williams, Mikaeel Yunus
Reports on Progress in Physics, 2021, Volume 84, Number 12 [ arXiv:2101.08320 ] -
[1] Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge
Sang Eon Park, Dylan Rankin, Silviu-Marian Udrescu, Mikaeel Yunus, Philip Harris
Journal of High Energy Physics, 2021, Article 30 [ arXiv:2011.03550 | code ]
Yidi Qi
- [1] Harmonic 1-forms on real loci of Calabi-Yau manifolds
Michael R. Douglas, Daniel Platt, Yidi Qi
[ arXiv:2405.19402 ]
Kate Richardson
Simon Rothman
- [1] Chained Quantile Morphing with Normalizing Flows
Samuel Bright-Thonney, Philip Harris, Patrick McCormack, Simon Rothman
[ arXiv:2309.15912 ]
Andrew Saydjari
-
[3] A Parsec-Scale Galactic 3D Dust Map out to 1.25 kpc from the Sun
Gordian Edenhofer, Catherine Zucker, Philipp Frank, Andrew K. Saydjari, Joshua S. Speagle, Douglas Finkbeiner, Torsten Enßlin
Astronomy & Astrophysics, Forthcoming article, 2024, Section Interstellar and circumstellar matter [ arXiv:2308.01295 ] -
[2] Measuring the 8621 Å Diffuse Interstellar Band in Gaia DR3 RVS Spectra: Obtaining a Clean Catalog by Marginalizing over Stellar Types
Andrew K. Saydjari, Catherine Zucker, J. E. G. Peek, Douglas P. Finkbeiner
[ arXiv:2212.03879 ] -
[1] Photometry on Structured Backgrounds: Local Pixelwise Infilling by Regression
Andrew K. Saydjari, Douglas P. Finkbeiner
The Astrophysical Journal, 2022, Volume 933, Number 2 [ arXiv:2201.07246 ]
Atınç Çağan ŞENGÜL
-
[3] Substructure Detection in Realistic Strong Lensing Systems with Machine LearningSubstructure Detection in Realistic Strong Lensing Systems with Machine Learning
Arthur Tsang, Atınç Çağan Şengül, Cora Dvorkin
[ arXiv:2401.16624 ] -
[2] Subhalo effective density slope measurements from HST strong lensing data with neural likelihood-ratio estimation
Gemma Zhang, Atınç Çağan Şengül, Cora Dvorkin
Monthly Notices of the Royal Astronomical Society, 2024, Volume 527, Issue 2 [ arXiv:2308.09739 ] -
[1] Substructure Detection Reanalyzed: Dark Perturber shown to be a Line-of-Sight Halo
Atınç Çağan Şengül, Cora Dvorkin, Bryan Ostdiek, Arthur Tsang
Monthly Notices of the Royal Astronomical Society, 2022, Volume 515, Issue 3, Pages 4391–4401 [ arXiv:2112.00749 ]
Christopher Shallue
- [1] Reconstructing Cosmological Initial Conditions from Late-Time Structure with Convolutional Neural Networks
Christopher J. Shallue, Daniel J. Eisenstein
Monthly Notices of the Royal Astronomical Society, 2023, Volume 520, Issue 4 [ arXiv:2207.12511 ]
Michael Skuhersky
- [1] Toward a more accurate 3D atlas of C. elegans neurons
Michael Skuhersky, Tailin Wu, Eviatar Yemini, Amin Nejatbakhsh, Edward Boyden & Max Tegmark
BMC Bioinformatics, Volume 23, Article 195 [ ]
Yitian Sun
- [1] Modeling early-universe energy injection with Dense Neural Networks
Yitian Sun, Tracy R. Slatyer
Physical Review D, Volume 107, Article 063541 [ arXiv:2207.06425 ]
Emmanouil Theodosis
-
[3] Learning Linear Groups in Neural Networks
Emmanouil Theodosis, Karim Helwani, Demba Ba
[ arXiv:2305.18552 ] -
[2] Learning Silhouettes with Group Sparse Autoencoders
Emmanouil Theodosis and Demba Ba
Harvard CRISP Preprint [ ] -
[1] On the convergence of group-sparse autoencoders
Emmanouil Theodosis, Bahareh Tolooshams, Pranay Tankala, Abiy Tasissa, Demba Ba
[ arXiv:2102.07003 ]
Arthur Tsang
-
[2] Substructure Detection in Realistic Strong Lensing Systems with Machine LearningSubstructure Detection in Realistic Strong Lensing Systems with Machine Learning
Arthur Tsang, Atınç Çağan Şengül, Cora Dvorkin
[ arXiv:2401.16624 ] -
[1] Substructure Detection Reanalyzed: Dark Perturber shown to be a Line-of-Sight Halo
Atınç Çağan Şengül, Cora Dvorkin, Bryan Ostdiek, Arthur Tsang
Monthly Notices of the Royal Astronomical Society, 2022, Volume 515, Issue 3, Pages 4391–4401 [ arXiv:2112.00749 ]
Alex Wen
Noah Wolfe
Felix Yu
Xiyu Zhai
Gemma Zhang
-
[2] Subhalo effective density slope measurements from HST strong lensing data with neural likelihood-ratio estimation
Gemma Zhang, Atınç Çağan Şengül, Cora Dvorkin
Monthly Notices of the Royal Astronomical Society, 2024, Volume 527, Issue 2 [ arXiv:2308.09739 ] -
[1] Inferring subhalo effective density slopes from strong lensing observations with neural likelihood-ratio estimation
Gemma Zhang, Siddharth Mishra-Sharma, Cora Dvorkin
Monthly Notices of the Royal Astronomical Society, 2022, Volume 517, Issue 3 [ arXiv:2208.13796 ]
Pavel Zhelnin
Alexander Zlokapa
- [1] Biological error correction codes generate fault-tolerant neural networks
Alexander Zlokapa, Andrew K. Tan, John M. Martyn, Max Tegmark, Isaac L. Chuang
[ arXiv:2202.12887 ]
Oreoluwa Alao
Julia Balla
Donato Jimenez Beneto
Elias Benghiat
Anugrah Chemparathy
Matthew Chen
Fiona Daly
Owen Dugan
-
[2] QuanTA: Efficient High-Rank Fine-Tuning of LLMs with Quantum-Informed Tensor Adaptation
Zhuo Chen, Rumen Dangovski, Charlotte Loh, Owen Dugan, Di Luo, Marin Soljačić
[ arXiv:2406.00132 ] -
[1] Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows
Owen Dugan, Peter Y. Lu, Rumen Dangovski, Di Luo, Marin Soljačić
[ arXiv:2302.12235 ]
Nino Ephremidze
Orion Foo
Varun Hariprasad
Adriano Hernandez
Elizabeth Panner
Marco Pretell
Marshall Taylor
Vinh Tran
Nate Woodward
Lana Xu
Former IAIFI Fellows
Anna Golubeva
-
[3] Dynamic Sparse Training with Structured Sparsity
Mike Lasby, Anna Golubeva, Utku Evci, Mihai Nica, Yani Ioannou
[ arXiv:2305.02299 ] -
[2] Bounding generalization error with input compression: An empirical study with infinite-width networks
Angus Galloway, Anna Golubeva, Mahmoud Salem, Mihai Nica, Yani Ioannou, Graham W. Taylor
[ arXiv:2207.09408 ] -
[1] Pruning a restricted Boltzmann machine for quantum state reconstruction
Anna Golubeva, Roger G. Melko
Physical Review B, 2022, Volume 105, Article 125124 [ arXiv:2110.03676 ]
Former IAIFI Affiliates
Justin Solomon
Haim Sompolinsky
Former Junior Investigators
Floor Broekgaarden
Salvatore Cali
Aidan Chambers
Mehmet Demirtas
-
[3] Neural Network Field Theories: Non-Gaussianity, Actions, and Locality
Mehmet Demirtas, James Halverson, Anindita Maiti, Matthew D. Schwartz, Keegan Stoner
[ arXiv:2307.03223 ] -
[2] Computational Mirror Symmetry
Mehmet Demirtas, Manki Kim, Liam McAllister, Jakob Moritz, Andres Rios-Tascon
Journal of High Energy Physics, 2024, Volume 2024, Article number 184 [ arXiv:2303.00757 ] -
[1] PQ Axiverse
Mehmet Demirtas, Naomi Gendler, Cody Long, Liam McAllister, Jakob Moritz
Journal of High Energy Physics 2023, Volume 2023, Article number 92 [ arXiv:2112.04503 ]
Mohit Dighamber
Akshunna S. Dogra
- [1] SHAPER: Can You Hear the Shape of a Jet?
Demba Ba, Akshunna S. Dogra, Rikab Gambhir, Abiy Tasissa, Jesse Thaler
Journal of High Energy Physics, 2023, Volume 2023, Article 195 [ arXiv:2302.12266 ]
Arkopal Dutt
Harold Erbin
-
[4] Gravitational action for a massive Majorana fermion in 2d quantum gravity
Corinne de Lacroix, Harold Erbin, Vincent Lahoche
Journal of High Energy Physics, 2024, Volume 2024, Article number 68 [ arXiv:2308.08342 ] -
[3] Characterizing 4-string contact interaction using machine learning
Harold Erbin, Atakan Hilmi Fırat
Journal of High Energy Physics, 2024, Article 16 [ arXiv:2211.09129 ] -
[2] Deep multi-task mining Calabi-Yau four-folds
Harold Erbin, Riccardo Finotello, Robin Schneider, Mohamed Tamaazousti
Machine Learning: Science and Technology, 2021, Volume 3, Number 1 [ arXiv:2108.02221 ] -
[1] Nonperturbative renormalization for the neural network–QFT correspondence
Harold Erbin, Vincent Lahoche, Dine Ousmane Samary
Machine Learning Science and Technology, 2022, Volume 3, Number 1, Article 015027 [ arXiv:2108.01403 ]
Cristiano Fanelli
- [1] Machine Learning in Nuclear Physics
Amber Boehnlein, Markus Diefenthaler, Nobuo Sato, Malachi Schram, Veronique Ziegler, Cristiano Fanelli, Morten Hjorth-Jensen, Tanja Horn, Michelle P. Kuchera, Dean Lee, Witold Nazarewicz, Peter Ostroumov, Kostas Orginos, Alan Poon, Xin-Nian Wang, Alexander Scheinker, Michael S. Smith, and Long-Gang Pang
Reviews of Modern Physics, 2022, Volume 94, Article 031003 [ arXiv:2112.02309 ]
Lena Funcke
-
[3] Exploring the CP-violating Dashen phase in the Schwinger model with tensor networks
Lena Funcke, Karl Jansen, Stefan Kühn
Physical Review D, 2023, Volume 108, Issue 1 [ arXiv:2303.03799 ] -
[2] Degeneracy Engineering for Classical and Quantum Annealing: A Case Study of Sparse Linear Regression in Collider Physics
Eric R. Anschuetz, Lena Funcke, Patrick T. Komiske, Serhii Kryhin, Jesse Thaler
Physical Review D, Volume 106, Article 056008 [ arXiv:2205.10375 ] -
[1] Towards Quantum Simulations in Particle Physics and Beyond on Noisy Intermediate-Scale Quantum Devices
Lena Funcke, Tobias Hartung, Karl Jansen, Stefan Kühn, Manuel Schneider, Paolo Stornati, Xiaoyang Wang
Philosophical Transactions of the Royal Society A [ arXiv:2110.03809 ]
Jasmine Gill
-
[3] Deep Learning Detection and Classification of Gravitational Waves from Neutron Star-Black Hole Mergers
Richard Qiu, Plamen Krastev, Kiranjyot Gill, Edo Berger
Physics Letters B, 2023, Volume 840 [ arXiv:2210.15888 ] -
[2] Constraining the Time of Gravitational Wave Emission from Core-Collapse Supernovae
Kiranjyot Gill, Griffin Hosseinzadeh, Edo Berger, Michele Zanolin, Marek Szczepanczyk
The Astrophysical Journal, 2022, Volume 931, Number 2 [ arXiv:2201.03609 ] -
[1] Detection and Parameter Estimation of Gravitational Waves from Binary Neutron-Star Mergers in Real LIGO Data using Deep Learning
Plamen G. Krastev, Kiranjyot Gill, V. Ashley Villar, Edo Berger
Physics Letters B, 2021, Vol. 815, Article 136161 [ arXiv:2012.13101 ]
Sebastian Gomez
-
[9] Superphot+: Realtime Fitting and Classification of Supernova Light Curves
Kaylee M. de Soto (1), Ashley Villar (1), Edo Berger (1 and 2), Sebastian Gomez (3), Griffin Hosseinzadeh (4), Doug Branton (5), Sandro Campos (6), Melissa DeLucchi (6), Jeremy Kubica (6), Olivia Lynn (6), Konstantin Malanchev (6), Alex I. Malz (6) ((1) Center for Astrophysics | Harvard & Smithsonian, (2) The NSF AI Institute for Artificial Intelligence and Fundamental Interactions, (3) Space Telescope Science Institute, (4) Steward Observatory | University of Arizona, (5) DiRAC Institute and the Department of Astronomy | University of Washington, (6) McWilliams Center for Cosmology | Department of Physics at Carnegie Mellon University)
[ arXiv:2403.07975 ] -
[8] An Extensive Hubble Space Telescope Study of the Offset and Host Light Distributions of Type I Superluminous Supernovae
Brian Hsu, Peter K. Blanchard, Edo Berger, Sebastian Gomez
The Astrophysical Journal 2024, Volume 961, Number 2 [ arXiv:2308.07271 ] -
[7] From Discovery to the First Month of the Type II Supernova 2023ixf: High and Variable Mass Loss in the Final Year before Explosion
Daichi Hiramatsu, Daichi Tsuna, Edo Berger, Koichi Itagaki, Jared A. Goldberg, Sebastian Gomez, Kishalay De, Griffin Hosseinzadeh, K. Azalee Bostroem, Peter J. Brown, Iair Arcavi, Allyson Bieryla, Peter K. Blanchard, Gilbert A. Esquerdo, Joseph Farah, D. Andrew Howell, Tatsuya Matsumoto, Curtis McCully, Megan Newsome, Estefania Padilla Gonzalez, Craig Pellegrino, Jaehyon Rhee, Giacomo Terreran, József Vinkó, J. Craig Wheeler
The Astrophysical Journal Letters 2023, Volume 955, Number 1 [ arXiv:2307.03165 ] -
[6] Multiple Peaks and a Long Precursor in the Type IIn Supernova 2021qqp: An Energetic Explosion in a Complex Circumstellar Environment
Daichi Hiramatsu, Tatsuya Matsumoto, Edo Berger, Conor Ransome, V. Ashley Villar, Sebastian Gomez, Yvette Cendes, Kishalay De, K. Azalee Bostroem, Joseph Farah, D. Andrew Howell, Curtis McCully, Megan Newsome, Estefania Padilla Gonzalez, Craig Pellegrino, Akihiro Suzuki, Giacomo Terreran
The Astrophysical Journal, 2024, Volume 964, Number 2 [ arXiv:2305.11168 ] -
[5] Limits on Simultaneous and Delayed Optical Emission from Well-localized Fast Radio Bursts
Daichi Hiramatsu, Edo Berger, Brian D. Metzger, Sebastian Gomez, Allyson Bieryla, Iair Arcavi, D. Andrew Howell, Ryan Mckinven, Nozomu Tominaga
The Astrophysical Journal Letters 2023, volume 947, number 2 [ arXiv:2211.03974 ] -
[4] Identifying Tidal Disruption Events with an Expansion of the FLEET Machine Learning Algorithm
Sebastian Gomez, V. Ashley Villar, Edo Berger, Suvi Gezari, Sjoert van Velzen, Matt Nicholl, Peter K. Blanchard, Kate. D. Alexander
The Astrophysical Journal, 2023, Volume 949, Issue 113 [ arXiv:2210.10810 ] -
[3] The First Two Years of FLEET: an Active Search for Superluminous Supernovae
Sebastian Gomez, Edo Berger, Peter K. Blanchard, Griffin Hosseinzadeh, Matt Nicholl, Daichi Hiramatsu, V. Ashley Villar, Yao Yin
The Astrophysical Journal, 2023, Volume 949, Issue 114 [ arXiv:2210.10811 ] -
[2] Luminous Supernovae: Unveiling a Population Between Superluminous and Normal Core-collapse Supernovae
Sebastian Gomez, Edo Berger, Matt Nicholl, Peter K. Blanchard, Griffin Hosseinzadeh
The Astrophysical Journal, 2022, Volume 941, Number 2 [ arXiv:2204.08486 ] -
[1] The Luminous and Double-Peaked Type Ic Supernova 2019stc: Evidence for Multiple Energy Sources
Sebastian Gomez, Edo Berger, Griffin Hosseinzadeh, Peter K. Blanchard, Matt Nicholl, V. Ashley Villar
The Astrophysical Journal, 2021, Vol. 913, Article 143 [ arXiv:2103.02611 ]
Qi Guo
- [1] Polarization Multi-Image Synthesis with Birefringent Metasurfaces
Dean Hazineh, Soon Wei Daniel Lim, Qi Guo, Federico Capasso, Todd Zickler
[ arXiv:2307.08106 ]
Benjamin Harris
Arthur Hennequin
Griffin Hosseinzadeh
-
[8] Superphot+: Realtime Fitting and Classification of Supernova Light Curves
Kaylee M. de Soto (1), Ashley Villar (1), Edo Berger (1 and 2), Sebastian Gomez (3), Griffin Hosseinzadeh (4), Doug Branton (5), Sandro Campos (6), Melissa DeLucchi (6), Jeremy Kubica (6), Olivia Lynn (6), Konstantin Malanchev (6), Alex I. Malz (6) ((1) Center for Astrophysics | Harvard & Smithsonian, (2) The NSF AI Institute for Artificial Intelligence and Fundamental Interactions, (3) Space Telescope Science Institute, (4) Steward Observatory | University of Arizona, (5) DiRAC Institute and the Department of Astronomy | University of Washington, (6) McWilliams Center for Cosmology | Department of Physics at Carnegie Mellon University)
[ arXiv:2403.07975 ] -
[7] From Discovery to the First Month of the Type II Supernova 2023ixf: High and Variable Mass Loss in the Final Year before Explosion
Daichi Hiramatsu, Daichi Tsuna, Edo Berger, Koichi Itagaki, Jared A. Goldberg, Sebastian Gomez, Kishalay De, Griffin Hosseinzadeh, K. Azalee Bostroem, Peter J. Brown, Iair Arcavi, Allyson Bieryla, Peter K. Blanchard, Gilbert A. Esquerdo, Joseph Farah, D. Andrew Howell, Tatsuya Matsumoto, Curtis McCully, Megan Newsome, Estefania Padilla Gonzalez, Craig Pellegrino, Jaehyon Rhee, Giacomo Terreran, József Vinkó, J. Craig Wheeler
The Astrophysical Journal Letters 2023, Volume 955, Number 1 [ arXiv:2307.03165 ] -
[6] The First Two Years of FLEET: an Active Search for Superluminous Supernovae
Sebastian Gomez, Edo Berger, Peter K. Blanchard, Griffin Hosseinzadeh, Matt Nicholl, Daichi Hiramatsu, V. Ashley Villar, Yao Yin
The Astrophysical Journal, 2023, Volume 949, Issue 114 [ arXiv:2210.10811 ] -
[5] Photometrically-Classified Superluminous Supernovae from the Pan-STARRS1 Medium Deep Survey: A Case Study for Science with Machine Learning-Based Classification
Brian Hsu, Griffin Hosseinzadeh, V. Ashley Villar, Edo Berger
The Astrophysical Journal, 2022, Volume 937, Number 1 [ arXiv:2204.09809 ] -
[4] Luminous Supernovae: Unveiling a Population Between Superluminous and Normal Core-collapse Supernovae
Sebastian Gomez, Edo Berger, Matt Nicholl, Peter K. Blanchard, Griffin Hosseinzadeh
The Astrophysical Journal, 2022, Volume 941, Number 2 [ arXiv:2204.08486 ] -
[3] Constraining the Time of Gravitational Wave Emission from Core-Collapse Supernovae
Kiranjyot Gill, Griffin Hosseinzadeh, Edo Berger, Michele Zanolin, Marek Szczepanczyk
The Astrophysical Journal, 2022, Volume 931, Number 2 [ arXiv:2201.03609 ] -
[2] A Deep-learning Approach for Live Anomaly Detection of Extragalactic Transients
Ashley Villar, Miles Cranmer, Edo Berger, Gabriella Contardo, Shirley Ho, Griffin Hosseinzadeh, Joshua Yao-Yu Lin
The Astrophysical Journal Supplement Series, Volume 255 [ ] -
[1] The Luminous and Double-Peaked Type Ic Supernova 2019stc: Evidence for Multiple Energy Sources
Sebastian Gomez, Edo Berger, Griffin Hosseinzadeh, Peter K. Blanchard, Matt Nicholl, V. Ashley Villar
The Astrophysical Journal, 2021, Vol. 913, Article 143 [ arXiv:2103.02611 ]
Jared Hwang
Daniel Johnson
Manami Kanemura
Gurtej Kanwar
-
[14] Practical applications of machine-learned flows on gauge fields
Ryan Abbott, Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2404.11674 ] -
[13] Multiscale Normalizing Flows for Gauge Theories
Ryan Abbott, Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2404.10819 ] -
[12] Applications of flow models to the generation of correlated lattice QCD ensembles
Ryan Abbott, Aleksandar Botev, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2401.10874 ] -
[11] Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics
Kyle Cranmer, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Phiala E. Shanahan
Nature Reviews Physics, 2023, Volume 5 [ arXiv:2309.01156 ] -
[10] Signal-to-noise improvement through neural network contour deformations for 3D SU(2) lattice gauge theory
William Detmold, Gurtej Kanwar, Yin Lin, Phiala E. Shanahan, Michael L. Wagman
[ arXiv:2309.00600 ] -
[9] Normalizing flows for lattice gauge theory in arbitrary space-time dimension
Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Alexander G.D.G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2305.02402 ] -
[8] Sampling QCD field configurations with gauge-equivariant flow models
Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Alexander G. D. G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
[ arXiv:2208.03832 ] -
[7] Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions
Ryan Abbott, Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Betsy Tian, Julian M. Urban
Physical REview D, 2022, Volume 106, Issue 7 [ arXiv:2207.08945 ] -
[6] Flow-based sampling in the lattice Schwinger model at criticality
Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
Physical Review D, 2022, Volume 106, Article 014514 [ arXiv:2202.11712 ] -
[5] Real-time lattice gauge theory actions: unitarity, convergence, and path integral contour deformations
Gurtej Kanwar, Michael L. Wagman
Physical Review D, Volume 104, Article 014513 [ arXiv:2103.02602 ] -
[4] Flow-based sampling for multimodal distributions in lattice field theory
Daniel C. Hackett, Chung-Chun Hsieh, Michael S. Albergo, Denis Boyda, Jiunn-Wei Chen, Kai-Feng Chen, Kyle Cranmer, Gurtej Kanwar, Phiala E. Shanahan
[ arXiv:2107.00734 ] -
[3] Flow-based sampling for fermionic lattice field theories
Michael S. Albergo, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Julian M. Urban, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Phiala E. Shanahan
Physical Review D, 2021, Vol. 104, Iss. 11 – 1 [ arXiv:2106.05934 ] -
[2] Path integral contour deformations for observables in SU(N) gauge theory
William Detmold, Gurtej Kanwar, Henry Lamm, Michael L. Wagman, Neill C. Warrington
Physical Review D, 2021, Vol. 103, Issue 9, Article 094517 [ arXiv:2101.12668 ] -
[1] Introduction to Normalizing Flows for Lattice Field Theory
Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Kyle Cranmer, Sébastien Racanière, Danilo Jimenez Rezende, and Phiala E. Shanahan
[ arXiv:2101.08176 ]
Patrick Komiske
-
[2] Preserving New Physics while Simultaneously Unfolding All Observables
Patrick Komiske, W. Patrick McCormack, Benjamin Nachman
Physical Review D, Volume 104, Article 076027 [ arXiv:2105.09923 ] -
[1] The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics
Gregor Kasieczka (ed), Benjamin Nachman (ed), David Shih (ed), Oz Amram, Anders Andreassen, Kees Benkendorfer, Blaz Bortolato, Gustaaf Brooijmans, Florencia Canelli, Jack H. Collins, Biwei Dai, Felipe F. De Freitas, Barry M. Dillon, Ioan-Mihail Dinu, Zhongtian Dong, Julien Donini, Javier Duarte, D. A. Faroughy, Julia Gonski, Philip Harris, Alan Kahn, Jernej F. Kamenik, Charanjit K. Khosa, Patrick Komiske, Luc Le Pottier, Pablo Martín-Ramiro, Andrej Matevc, Eric Metodiev, Vinicius Mikuni, Inês Ochoa, Sang Eon Park, Maurizio Pierini, Dylan Rankin, Veronica Sanz, Nilai Sarda, Urous Seljak, Aleks Smolkovic, George Stein, Cristina Mantilla Suarez, Manuel Szewc, Jesse Thaler, Steven Tsan, Silviu-Marian Udrescu, Louis Vaslin, Jean-Roch Vlimant, Daniel Williams, Mikaeel Yunus
Reports on Progress in Physics, 2021, Volume 84, Number 12 [ arXiv:2101.08320 ]
Serhii Kryhin
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[2] Degeneracy Engineering for Classical and Quantum Annealing: A Case Study of Sparse Linear Regression in Collider Physics
Eric R. Anschuetz, Lena Funcke, Patrick T. Komiske, Serhii Kryhin, Jesse Thaler
Physical Review D, Volume 106, Article 056008 [ arXiv:2205.10375 ] -
[1] Disentangling Quarks and Gluons with CMS Open Data
Patrick T. Komiske, Serhii Kryhin, Jesse Thaler
Physical Review D, 2022, Volume 106 Article 094021 [ arXiv:2205.04459 ]
Jeffrey Lazar
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[2] New Pathways in Neutrino Physics via Quantum-Encoded Data Analysis
Jeffrey Lazar, Santiago Giner Olavarrieta, Giancarlo Gatti, Carlos A. Argüelles, Mikel Sanz
[ arXiv:2402.19306 ] -
[1] Prometheus: An Open-Source Neutrino Telescope Simulation
Jeffrey Lazar, Stephan Meighen-Berger, Christian Haack, David Kim, Santiago Giner, Carlos A. Argüelles
[ arXiv:2304.14526 ]
Keiran Lewellen
Alexander Lin
-
[2] Mixture Model Auto-Encoders: Deep Clustering through Dictionary Learning
Alexander Lin, Andrew H. Song, Demba Ba
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, pp. 3368-3372 [ arXiv:2110.04683 ] -
[1] Covariance-Free Sparse Bayesian Learning
Alexander Lin, Andrew H. Song, Berkin Bilgic, Demba Ba
IEEE Transactions on Signal Processing, volume 70 [ arXiv:2105.10439 ]
Peter Y. Lu
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[6] Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows
Owen Dugan, Peter Y. Lu, Rumen Dangovski, Di Luo, Marin Soljačić
[ arXiv:2302.12235 ] -
[5] Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure
Samuel Kim, Peter Y. Lu, Charlotte Loh, Jamie Smith, Jasper Snoek, Marin Soljačić
Transactions on Machine Learning Research 2022 [ ] -
[4] Discovering Conservation Laws using Optimal Transport and Manifold Learning
Peter Y. Lu, Rumen Dangovski, Marin Soljačić
Nature Communications [ arXiv:2208.14995 ] -
[3] Deep Learning and Symbolic Regression for Discovering Parametric Equations
Michael Zhang, Samuel Kim, Peter Y. Lu, Marin Soljačić
IEEE Journals, 2023, PubMed ID 37721885 [ arXiv:2207.00529 ] -
[2] Discovering Sparse Interpretable Dynamics from Partial Observations
Peter Y. Lu, Joan Ariño, Marin Soljačić
Communications Physics, 2022, Vol 5, Article 206 [ arXiv:2107.10879 ] -
[1] Scalable and Flexible Deep Bayesian Optimization with Auxiliary Information for Scientific Problems
Samuel Kim, Peter Y. Lu, Charlotte Loh, Jamie Smith, Jasper Snoek, Marin Soljačić
Transactions on Machine Learning Research, September 2022 [ arXiv:2104.11667 ]
Anindita Maiti
-
[2] Neural Network Field Theories: Non-Gaussianity, Actions, and Locality
Mehmet Demirtas, James Halverson, Anindita Maiti, Matthew D. Schwartz, Keegan Stoner
[ arXiv:2307.03223 ] -
[1] Symmetry-via-Duality: Invariant Neural Network Densities from Parameter-Space Correlators
Anindita Maiti, Keegan Stoner, James Halverson
[ arXiv:2106.00694 ]
Katie Mason
Patrick McCormack
-
[2] Chained Quantile Morphing with Normalizing Flows
Samuel Bright-Thonney, Philip Harris, Patrick McCormack, Simon Rothman
[ arXiv:2309.15912 ] -
[1] Preserving New Physics while Simultaneously Unfolding All Observables
Patrick Komiske, W. Patrick McCormack, Benjamin Nachman
Physical Review D, Volume 104, Article 076027 [ arXiv:2105.09923 ]
Eric Metodiev
- [1] The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics
Gregor Kasieczka (ed), Benjamin Nachman (ed), David Shih (ed), Oz Amram, Anders Andreassen, Kees Benkendorfer, Blaz Bortolato, Gustaaf Brooijmans, Florencia Canelli, Jack H. Collins, Biwei Dai, Felipe F. De Freitas, Barry M. Dillon, Ioan-Mihail Dinu, Zhongtian Dong, Julien Donini, Javier Duarte, D. A. Faroughy, Julia Gonski, Philip Harris, Alan Kahn, Jernej F. Kamenik, Charanjit K. Khosa, Patrick Komiske, Luc Le Pottier, Pablo Martín-Ramiro, Andrej Matevc, Eric Metodiev, Vinicius Mikuni, Inês Ochoa, Sang Eon Park, Maurizio Pierini, Dylan Rankin, Veronica Sanz, Nilai Sarda, Urous Seljak, Aleks Smolkovic, George Stein, Cristina Mantilla Suarez, Manuel Szewc, Jesse Thaler, Steven Tsan, Silviu-Marian Udrescu, Louis Vaslin, Jean-Roch Vlimant, Daniel Williams, Mikaeel Yunus
Reports on Progress in Physics, 2021, Volume 84, Number 12 [ arXiv:2101.08320 ]
Joshua Mills
Niklas Nolte
-
[8] From Neurons to Neutrons: A Case Study in Interpretability
Ouail Kitouni, Niklas Nolte, Víctor Samuel Pérez-Díaz, Sokratis Trifinopoulos, Mike Williams
[ arXiv:2405.17425 ] -
[7] Applications of Lipschitz neural networks to the Run 3 LHCb trigger system
Blaise Delaney, Nicole Schulte, Gregory Ciezarek, Niklas Nolte, Mike Williams, Johannes Albrecht
[ arXiv:2312.14265 ] -
[6] NuCLR, Nuclear Co-Learned Representations
Ouail Kitouni, Niklas Nolte, Sokratis Trifinopoulos, Subhash Kantamneni, Mike Williams
[ arXiv:2306.06099 ] -
[5] Development of the Topological Trigger for LHCb Run 3
Nicole Schulte, Blaise Raheem Delaney, Niklas Nolte, Gregory Max Ciezarek, Johannes Albrecht, Mike Williams
[ arXiv:2306.09873 ] -
[4] Expressive Monotonic Neural Networks
Niklas Nolte, Ouail Kitouni, Mike Williams
International Conference on Learning Representations 2023 [ ] -
[3] Finding NEEMo: Geometric Fitting using Neural Estimation of the Energy Mover’s Distance
Ouail Kitouni, Niklas Nolte, Mike Williams
[ arXiv:2209.15624 ] -
[2] Towards Understanding Grokking: An Effective Theory of Representation Learning
Ziming Liu, Ouail Kitouni, Niklas Nolte, Eric J. Michaud, Max Tegmark, Mike Williams
[ arXiv:2205.10343 ] -
[1] Robust and Provably Motonic Networks
Ouail Kitouni, Niklas Nolte, Mike Williams
Fourth Workshop on Machine Learning and the Physical Sciences (NeurIPS 2021) Proceedings, [ arXiv:2112.00038 ]
Curtis Northcutt
Bryan Ostdiek
-
[8] Neural Embedding: Learning the Embedding of the Manifold of Physics Data
Sang Eon Park, Philip Harris, Bryan Ostdiek
Journal of High Energy Physics, 2023, Volume 2023, Article 108 [ arXiv:2208.05484 ] -
[7] Revealing the Milky Way’s Most Recent Major Merger with a Gaia EDR3 Catalog of Machine-Learned Line-of-Sight Velocities
Adriana Dropulic, Hongwan Liu, Bryan Ostdiek, Mariangela Lisanti
Monthly Notices of the Royal Astronomical Society, May 2023, Volume 521, Issue 2 [ arXiv:2205.12278 ] -
[6] Creating Simple, Interpretable Anomaly Detectors for New Physics in Jet Substructure
Layne Bradshaw, Spencer Chang, Bryan Ostdiek
Physical Review D, 2022, Volume 106, Article 035014 [ arXiv:2203.01343 ] -
[5] Substructure Detection Reanalyzed: Dark Perturber shown to be a Line-of-Sight Halo
Atınç Çağan Şengül, Cora Dvorkin, Bryan Ostdiek, Arthur Tsang
Monthly Notices of the Royal Astronomical Society, 2022, Volume 515, Issue 3, Pages 4391–4401 [ arXiv:2112.00749 ] -
[4] Challenges for Unsupervised Anomaly Detection in Particle Physics
Katherine Fraser, Samuel Homiller, Rashmish K. Mishra, Bryan Ostdiek, Matthew D. Schwartz
Journal of High Energy Physics, 2022, Article 66 [ arXiv:2110.06948 ] -
[3] Deep Set Auto Encoders for Anomaly Detection in Particle Physics
Bryan Ostdiek
SciPost Physics, 2022, Vol. 12, Issue 1 [ arXiv:2109.01695 ] -
[2] Machine Learning the 6th Dimension: Stellar Radial Velocities from 5D Phase-Space Correlations
Adriana Dropulic, Bryan Ostdiek, Laura J. Chang, Hongwan Liu, Timothy Cohen, and Mariangela Lisanti
The Astrophysical Journal Letters, 2021, 915, L14 [ arXiv:2103.14039 ] -
[1] Parameter Inference from Event Ensembles and the Top-Quark Mass
Forrest Flesher, Katherine Fraser, Charles Hutchison, Bryan Ostdiek, Matthew D. Schwartz
Journal of High Energy Physics, 2021, Article 58 [ arXiv:2011.04666 ]
Dylan Rankin
-
[5] Hardware-accelerated Inference for Real-Time Gravitational-Wave Astronomy
Alec Gunny, Dylan Rankin, Jeffrey Krupa, Muhammed Saleem, Tri Nguyen, Michael Coughlin, Philip Harris, Erik Katsavounidis, Steven Timm, Burt Holzman
[ arXiv:2108.12430 ] -
[4] hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices
Farah Fahim, Benjamin Hawks, Christian Herwig, James Hirschauer, Sergo Jindariani, Nhan Tran, Luca P. Carloni, Giuseppe Di Guglielmo, Philip Harris, Jeffrey Krupa, Dylan Rankin, Manuel Blanco Valentin, Josiah Hester, Yingyi Luo, John Mamish, Seda Orgrenci-Memik, Thea Aarrestad, Hamza Javed, Vladimir Loncar, Maurizio Pierini, Adrian Alan Pol, Sioni Summers, Javier Duarte, Scott Hauck, Shih-Chieh Hsu, Jennifer Ngadiuba, Mia Liu, Duc Hoang, Edward Kreinar, Zhenbin Wu
[ arXiv:2103.05579 ] -
[3] The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics
Gregor Kasieczka (ed), Benjamin Nachman (ed), David Shih (ed), Oz Amram, Anders Andreassen, Kees Benkendorfer, Blaz Bortolato, Gustaaf Brooijmans, Florencia Canelli, Jack H. Collins, Biwei Dai, Felipe F. De Freitas, Barry M. Dillon, Ioan-Mihail Dinu, Zhongtian Dong, Julien Donini, Javier Duarte, D. A. Faroughy, Julia Gonski, Philip Harris, Alan Kahn, Jernej F. Kamenik, Charanjit K. Khosa, Patrick Komiske, Luc Le Pottier, Pablo Martín-Ramiro, Andrej Matevc, Eric Metodiev, Vinicius Mikuni, Inês Ochoa, Sang Eon Park, Maurizio Pierini, Dylan Rankin, Veronica Sanz, Nilai Sarda, Urous Seljak, Aleks Smolkovic, George Stein, Cristina Mantilla Suarez, Manuel Szewc, Jesse Thaler, Steven Tsan, Silviu-Marian Udrescu, Louis Vaslin, Jean-Roch Vlimant, Daniel Williams, Mikaeel Yunus
Reports on Progress in Physics, 2021, Volume 84, Number 12 [ arXiv:2101.08320 ] -
[2] Fast convolutional neural networks on FPGAs with hls4ml
Thea Aarrestad, Vladimir Loncar, Nicolò Ghielmetti, Maurizio Pierini, Sioni Summers, Jennifer Ngadiuba, Christoffer Petersson, Hampus Linander, Yutaro Iiyama, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Dylan Rankin, Sergo Jindariani, Kevin Pedro, Nhan Tran, Mia Liu, Edward Kreinar, Zhenbin Wu, Duc Hoang
Machine Learning Science and Technology, 2021, Volume 2, Issue 4, Article 045015 [ arXiv:2101.05108 ] -
[1] Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge
Sang Eon Park, Dylan Rankin, Silviu-Marian Udrescu, Mikaeel Yunus, Philip Harris
Journal of High Energy Physics, 2021, Article 30 [ arXiv:2011.03550 | code ]
Gustavo Salinas
Nikita Saxena
Ralitsa Sharankova
Jonathan P Shoemaker
Keegan Stoner
-
[2] Neural Network Field Theories: Non-Gaussianity, Actions, and Locality
Mehmet Demirtas, James Halverson, Anindita Maiti, Matthew D. Schwartz, Keegan Stoner
[ arXiv:2307.03223 ] -
[1] Symmetry-via-Duality: Invariant Neural Network Densities from Parameter-Space Correlators
Anindita Maiti, Keegan Stoner, James Halverson
[ arXiv:2106.00694 ]
Andrew K. Tan
-
[2] Pareto-optimal clustering with the primal deterministic information bottleneck
Andrew K. Tan, Max Tegmark, Isaac L. Chuang
Entropy, 2022, 24(6) [ arXiv:2204.02489 ] -
[1] Biological error correction codes generate fault-tolerant neural networks
Alexander Zlokapa, Andrew K. Tan, John M. Martyn, Max Tegmark, Isaac L. Chuang
[ arXiv:2202.12887 ]
Dor Verbin
-
[2] Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields
Dor Verbin, Peter Hedman, Ben Mildenhall, Todd Zickler, Jonathan T. Barron, Pratul P. Srinivasan
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition [ arXiv:2112.03907 ] -
[1] Field of Junctions: Extracting Boundary Structure at Low SNR
Dor Verbin, Todd Zickler
IEEE/CVF International Conference on Computer Vision, 2021 [ arXiv:2011.13866 ]
Constantin Weisser
- [1] Enhancing searches for resonances with machine learning and moment decomposition
Ouail Kitouni, Benjamin Nachman, Constantin Weisser, and Mike Williams
Journal of High Energy Physics, 2021, Article 70 [ arXiv:2010.09745 | code ]
Chris Whittle
- [1] Machine Learning for Quantum-Enhanced Gravitational-Wave Observatories
Chris Whittle, Ge Yang, Matthew Evans, Lisa Barsotti
Physical Review D, Volume 108, Article 043034 [ arXiv:2305.13780 ]
Ray Wynne
Jun Yin
Felix Yu
Mikaeel Yunus
-
[2] The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics
Gregor Kasieczka (ed), Benjamin Nachman (ed), David Shih (ed), Oz Amram, Anders Andreassen, Kees Benkendorfer, Blaz Bortolato, Gustaaf Brooijmans, Florencia Canelli, Jack H. Collins, Biwei Dai, Felipe F. De Freitas, Barry M. Dillon, Ioan-Mihail Dinu, Zhongtian Dong, Julien Donini, Javier Duarte, D. A. Faroughy, Julia Gonski, Philip Harris, Alan Kahn, Jernej F. Kamenik, Charanjit K. Khosa, Patrick Komiske, Luc Le Pottier, Pablo Martín-Ramiro, Andrej Matevc, Eric Metodiev, Vinicius Mikuni, Inês Ochoa, Sang Eon Park, Maurizio Pierini, Dylan Rankin, Veronica Sanz, Nilai Sarda, Urous Seljak, Aleks Smolkovic, George Stein, Cristina Mantilla Suarez, Manuel Szewc, Jesse Thaler, Steven Tsan, Silviu-Marian Udrescu, Louis Vaslin, Jean-Roch Vlimant, Daniel Williams, Mikaeel Yunus
Reports on Progress in Physics, 2021, Volume 84, Number 12 [ arXiv:2101.08320 ] -
[1] Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge
Sang Eon Park, Dylan Rankin, Silviu-Marian Udrescu, Mikaeel Yunus, Philip Harris
Journal of High Energy Physics, 2021, Article 30 [ arXiv:2011.03550 | code ]