Paper Tracking (Hidden Page)

Table of Contents

Long-Term Visitors
Students

Management

Jesse Thaler

Mike Williams

Marisa LaFleur

Comfort Asumadu

{#}

Senior Investigators

Cora Dvorkin

Jim Halverson

Taritree Wongjirad

  • [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

Matt Schwartz

Phiala Shanahan

  • [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] Lattice quantum chromodynamics at large isospin density: 6144 pions in a box
    Ryan Abbott, William Detmold, Fernando Romero-López, Zohreh Davoudi, Marc Illa, Assumpta Parreño, Robert J. Perry, Phiala E. Shanahan, Michael L. Wagman
    [ arXiv:2307.15014 ]

  • [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
    [ 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

  • [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
    [ 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

  • [6] 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 ]

  • [5] Lattice quantum chromodynamics at large isospin density: 6144 pions in a box
    Ryan Abbott, William Detmold, Fernando Romero-López, Zohreh Davoudi, Marc Illa, Assumpta Parreño, Robert J. Perry, Phiala E. Shanahan, Michael L. Wagman
    [ arXiv:2307.15014 ]

  • [4] Infinite Variance in Monte Carlo Sampling of Lattice Field Theories
    Cagin Yunus, William Detmold
    Physical Review D, Volume 106, Article 094506 [ arXiv:2205.01001 ]

  • [3] Parton physics from a heavy-quark operator product expansion: Lattice QCD calculation of the second moment of the pion distribution amplitude
    William Detmold, Anthony Grebe, Issaku Kanamori, C.-J. David Lin, Santanu Mondal, Robert Perry, Yong Zhao
    Physical Review D, Volume 105, Article 034506 [ arXiv:2109.15241 ]

  • [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

  • [3] The dark matter profile of the Milky Way inferred from its circular velocity curve
    Xiaowei Ou, Anna-Christina Eilers, Lina Necib, Anna Frebel
    [ arXiv:2303.12838 ]

  • [2] Via Machinae 2.0: Full-Sky, Model-Agnostic Search for Stellar Streams in Gaia DR2
    David Shih, Matthew R. Buckley, Lina Necib
    [ arXiv:2303.01529 ]

  • [1] Uncovering dark matter density profiles in dwarf galaxies with graph neural networks
    Tri Nguyễn, Siddharth Mishra-Sharma, Reuel Williams, Lina Necib
    Physical Review D, 202, Volume 107, Issue 4 [ arXiv:2208.12825 ]

Todd Zickler

Shuchin Aeron

  • [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

  • [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

  • [4] Cosmological Field Emulation and Parameter Inference with Diffusion Models
    Nayantara Mudur, Carolina Cuesta-Lazaro, Douglas P. Finkbeiner
    [ arXiv:2312.07534 ]

  • [3] 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 ]

  • [2] Measuring the 8621 Å Diffuse Interstellar Band in Gaia DR3 RVS Spectra: Obtaining a Clean Catalog by Marginalizing over Stellar Types
    Andrew K. Saydjari, Catherine Zucker, J. E. G. Peek, Douglas P. Finkbeiner
    [ arXiv:2212.03879 ]

  • [1] Photometry on Structured Backgrounds: Local Pixelwise Infilling by Regression
    Andrew K. Saydjari, Douglas P. Finkbeiner
    The Astrophysical Journal, 2022, Volume 933, Number 2 [ arXiv:2201.07246 ]

Alexander Rakhlin

  • [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

  • [1] Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for Molecule Generation
    Ameya Daigavane, Song Kim, Mario Geiger, Tess Smidt
    [ arXiv:2311.16199 ]

Marin Soljacic

  • [1] 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 ]

Max Tegmark

  • [23] Generating Interpretable Networks using Hypernetworks
    Isaac Liao, Ziming Liu, Max Tegmark
    [ arXiv:2312.03051 ]

  • [22] 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 ]

  • [21] Grokking as Compression: A Nonlinear Complexity Perspective
    Ziming Liu, Ziqian Zhong, Max Tegmark
    [ arXiv:2310.05918 ]

  • [20] A Neural Scaling Law from Lottery Ticket Ensembling
    Ziming Liu, Max Tegmark
    [ arXiv:2310.02258 ]

  • [19] The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks
    Ziqian Zhong, Ziming Liu, Max Tegmark, Jacob Andreas
    [ arXiv:2306.17844 ]

  • [18] Discovering New Interpretable Conservation Laws as Sparse Invariants
    Ziming Liu, Patrick Obin Sturm, Saketh Bharadwaj, Sam Silva, Max Tegmark
    [ arXiv:2305.19525 ]

  • [17] Seeing is Believing: Brain-Inspired Modular Training for Mechanistic Interpretability
    Ziming Liu, Eric Gan, Max Tegmark
    [ arXiv:2305.08746 ]

  • [16] GenPhys: From Physical Processes to Generative Models
    Ziming Liu, Di Luo, Yilun Xu, Tommi Jaakkola, Max Tegmark
    [ arXiv:2304.02637 ]

  • [15] The Quantization Model of Neural Scaling
    Eric J. Michaud, Ziming Liu, Uzay Girit, Max Tegmark
    [ arXiv:2303.13506 ]

  • [14] PFGM++: Unlocking the Potential of Physics-Inspired Generative Models
    Yilun Xu, Ziming Liu, Yonglong Tian, Shangyuan Tong, Max Tegmark, Tommi Jaakkola
    [ arXiv:2302.04265 ]

  • [13] Precision Machine Learning
    Eric J. Michaud, Ziming Liu, Max Tegmark
    Entropy, 2023, 25(1) [ arXiv:2210.13447 ]

  • [12] Omnigrok: Grokking Beyond Algorithmic Data
    Ziming Liu, Eric J. Michaud, Max Tegmark
    [ arXiv:2210.01117 ]

  • [11] Poisson Flow Generative Models
    Yilun Xu, Ziming Liu, Max Tegmark, Tommi Jaakkola
    [ arXiv:2209.11178 | code ]

  • [10] 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 [ ]

  • [9] 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 ]

  • [8] Pareto-optimal clustering with the primal deterministic information bottleneck
    Andrew K. Tan, Max Tegmark, Isaac L. Chuang
    Entropy, 2022, 24(6) [ arXiv:2204.02489 ]

  • [7] 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 ]

  • [6] 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 ]

  • [5] Physics-Augmented Learning: A New Paradigm Beyond Physics-Informed Learning
    Ziming Liu, Yunyue Chen, Yuanqi Du, Max Tegmark
    [ arXiv:2109.13901 ]

  • [4] Machine-learning hidden symmetries
    Ziming Liu, Max Tegmark
    Physical Review Letters, 2022, 128, 180201 [ arXiv:2109.09721 ]

  • [3] Machine-Learning media bias
    Samantha D’Alonzo, Max Tegmark
    PLOS ONE, 2022, Volume 17, Issue 8, Article e0271947 [ arXiv:2109.00024 ]

  • [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 ]

IAIFI Fellows

Denis Boyda

  • [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
    [ 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 ]

Carolina Cuesta

  • [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 ]

Alexander Gagliano

  • [1] A Physics-Informed Variational Autoencoder for Rapid Galaxy Inference and Anomaly Detection
    Alexander Gagliano, V. Ashley Villar
    [ arXiv:2312.16687 ]

Gaia Grosso

Di Luo

  • [13] Pairing-based graph neural network for simulating quantum materials
    Di Luo, David D. Dai, Liang Fu
    [ arXiv:2311.02143 ]

  • [12] Pairing-based graph neural network for simulating quantum materials
    Di Luo, David D. Dai, Liang Fu
    [ arXiv:2311.02143 ]

  • [11] GenPhys: From Physical Processes to Generative Models
    Ziming Liu, Di Luo, Yilun Xu, Tommi Jaakkola, Max Tegmark
    [ arXiv:2304.02637 ]

  • [10] Autoregressive Neural TensorNet: Bridging Neural Networks and Tensor Networks for Quantum Many-Body Simulationg
    Zhuo Chen, Laker Newhouse, Eddie Chen, Di Luo, Marin Soljačić
    [ arXiv:2304.01996 ]

  • [9] Artificial intelligence for artificial materials: moiré atom
    Di Luo, Aidan P. Reddy, Trithep Devakul, Liang Fu
    [ arXiv:2303.08162 ]

  • [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] 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 ]

  • [6] 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 ]

  • [5] Variational Neural-Network Ansatz for Continuum Quantum Field Theory
    John M. Martyn, Khadijeh Najafi, Di Luo
    [ arXiv:2212.00782 ]

  • [4] 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 ]

  • [3] Koopman Operator learning for Accelerating Quantum Optimization and Machine Learning
    Di Luo, Jiayu Shen, Rumen Dangovski, Marin Soljačić
    [ arXiv:2211.01365 ]

  • [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] Infinite Neural Network Quantum States
    Di Luo, James Halverson
    Machine Learning: Science and Technology, 2023, Volume 4, Number 2 [ arXiv:2112.00723 ]

Jessie Micallef

Siddharth Mishra-Sharma

Ge Yang

Long-Term Visitors

Roger Rusack

IAIFI Affiliates

Aram Apyan

Ning Bao

George Barbastathis

Michael Douglas

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

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
    [ 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 ]

Sudhir Malik

Vidya Manian

Tyler Maunu

Brent D. Nelson

Cengiz Pehlevan

Dan Roberts

Artan Sheshmani

Haim Sompolinsky

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 ]

Ashley Villar

Mark Vogelsberger

Susanne Yelin

Post-Docs and Research Scientists

Daniel Johnson

Plamen Krastev

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 ]

Mehmet Demirtas

  • [1] Neural Network Field Theories: Non-Gaussianity, Actions, and Locality
    Mehmet Demirtas, James Halverson, Anindita Maiti, Matthew D. Schwartz, Keegan Stoner
    [ arXiv:2307.03223 ]

Lena Funcke

Daniel Hackett

Arthur Hennequin

Daichi Hiramatsu

  • [2] 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 ]

  • [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

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 ]

Patrick McCormack

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

Bryan Ostdiek

Fernando Romero-Lopez

Matthew Rosenberg

Sokratis Trifinopoulos

  • [1] NuCLR, Nuclear Co-Learned Representations
    Ouail Kitouni, Niklas Nolte, Sokratis Trifinopoulos, Subhash Kantamneni, Mike Williams
    [ arXiv:2306.06099 ]

Georgios Valogiannis

  • [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

Polina Abratenko

Aizhan Akhmetzhanova

Omar Alterkait

Samuel Alipour-fard

Sean Benevedes

{#}

Zhuo Chen

  • [2] Autoregressive Neural TensorNet: Bridging Neural Networks and Tensor Networks for Quantum Many-Body Simulationg
    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

Rumen Dangovski

  • [7] 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 ]

  • [6] Koopman Operator learning for Accelerating Quantum Optimization and Machine Learning
    Di Luo, Jiayu Shen, Rumen Dangovski, Marin Soljačić
    [ arXiv:2211.01365 ]

  • [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

  • [1] Simplifying Polylogarithms with Machine Learning
    Aurélien Dersy, Matthew D. Schwartz, Xiaoyuan Zhang
    [ arXiv:2206.04115 ]

Arkopal Dutt

Gokhan Egri

Atakan Hilmi Firat

Katherine Fraser

Rikab Gambhir

Kiranjyot (Jasmine) Gill {#Kiranjyot-(Jasmine)-Gill}

Sebastian Gomez

  • [6] 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
    [ arXiv:2308.07271 ]

  • [5] 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 ]

  • [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 ]

Mark Hamilton

  • [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

Zhang-Wei Hong

Maryam Hussaini

Zev Imani

Wenxuan Jia

Gurtej Kanwar

  • [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

  • [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 ]

Jeffrey Lazar

  • [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 ]

Zhaoyi Li

Alexander Lin

Ziming Liu

  • [19] Generating Interpretable Networks using Hypernetworks
    Isaac Liao, Ziming Liu, Max Tegmark
    [ arXiv:2312.03051 ]

  • [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

  • [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

  • [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 ]

John Martyn

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

Eric Moreno

Nayantara Mudur

  • [3] Cosmological Field Emulation and Parameter Inference with Diffusion Models
    Nayantara Mudur, Carolina Cuesta-Lazaro, Douglas P. Finkbeiner
    [ arXiv:2312.07534 ]

  • [2] 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 ]

  • [1] 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 ]

Aviv Netanyahu

Tri Nguyen

  • [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

  • [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

Kate Richardson

Simon Rothman

Andrew Saydjari

Atınç Çağan ŞENGÜL

Christopher Shallue

Jonathan P Shoemaker

Michael Skuhersky

Yitian Sun

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 ]

Emmanouil Theodosis

  • [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

Dor Verbin

Chris Whittle

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 ]

Alec Gunny

  • [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
    [ 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 ]

Oreoluwa Alao

Julia Balla

Donato Jimenez Beneto

Elias Benghiat

Aidan Chambers

Anugrah Chemparathy

Matthew Chen

Owen Dugan

  • [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 ]

Orion Foo

Varun Hariprasad

Benjamin Harris

Adriano Hernandez

Manami Kanemura

Serhii Kryhin

Marco Pretell

Nikita Saxena

Mia Sodini

Nate Woodward

Lana Xu