Paper Tracking (Hidden Page)

Table of Contents

Students
Former IAIFI Fellows
Former IAIFI Affiliates

Management

Jesse Thaler

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

Jim Halverson

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

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

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

  • [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

Lisa Barsotti

Isaac Chuang

  • [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

  • [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

Todd Zickler

Shuchin Aeron

  • [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

Carlos Argüelles-Delgado

  • [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

  • [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

  • [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

Tracy Slatyer

Tess Smidt

  • [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

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

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

Ge Yang

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 ]

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 ]

An Huang

Tommi Jaakkola

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

Akira Sone

Christopher Stubbs

Hidenori Tanaka

Abiy Tasissa

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

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

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

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

Rikab Gambhir

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

Samuel Kim

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

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

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

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

Atınç Çağan ŞENGÜL

Christopher Shallue

Michael Skuhersky

Yitian Sun

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

Alex Wen

Noah Wolfe

Felix Yu

Xiyu Zhai

Gemma Zhang

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

Mohit Dighamber

Akshunna S. Dogra

Arkopal Dutt

Harold Erbin

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

Jasmine Gill

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

Jeffrey Lazar

  • [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

Peter Y. Lu

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

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

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

Constantin Weisser

Chris Whittle

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
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