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Poster discussion
Roman Novak · Maxime Gabella · Frederic Dreyer · Siavash Golkar · Anh Tong · Irina Higgins · Mirco Milletari · Joe Antognini · Sebastian Goldt · Adín Ramírez Rivera · Roberto Bondesan · Ryo Karakida · Remi Tachet des Combes · Michael Mahoney · Nicholas Walker · Stanislav Fort · Samuel Smith · Rohan Ghosh · Aristide Baratin · Diego Granziol · Stephen Roberts · Dmitry Vetrov · Andrew Wilson · César Laurent · Valentin Thomas · Simon Lacoste-Julien · Dar Gilboa · Daniel Soudry · Anupam Gupta · Anirudh Goyal · Yoshua Bengio · Erich Elsen · Soham De · Stanislaw Jastrzebski · Charles H Martin · Samira Shabanian · Aaron Courville · Shorato Akaho · Lenka Zdeborova · Ethan Dyer · Maurice Weiler · Pim de Haan · Taco Cohen · Max Welling · Ping Luo · zhanglin peng · Nasim Rahaman · Loic Matthey · Danilo J. Rezende · Jaesik Choi · Kyle Cranmer · Lechao Xiao · Jaehoon Lee · Yasaman Bahri · Jeffrey Pennington · Greg Yang · Jiri Hron · Jascha Sohl-Dickstein · Guy Gur-Ari
Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes Roman Novak (Google Brain)*; Lechao Xiao (Google Brain); Jaehoon Lee (Google Brain); Yasaman Bahri (Google Brain); Greg Yang (Microsoft Research AI); Jiri Hron (University of Cambridge); Daniel Abolafia (Google Brain); Jeffrey Pennington (Google Brain); Jascha Sohl-Dickstein (Google Brain)
Topology of Learning in Artificial Neural Networks Maxime Gabella (Magma Learning)*
Jet grooming through reinforcement learning Frederic Dreyer (University of Oxford)*; Stefano Carrazza (University of Milan)
Inferring the quantum density matrix with machine learning Kyle Cranmer (New York University); Siavash Golkar (NYU)*; Duccio Pappadopulo (Bloomberg)
Backdrop: Stochastic Backpropagation Siavash Golkar (NYU)*; Kyle Cranmer (New York University)
Explain pathology in Deep Gaussian Process using Chaos Theory Anh Tong (UNIST)*; Jaesik Choi (Ulsan National Institute of Science and Technology)
Towards a Definition of Disentangled Representations Irina Higgins (DeepMind)*; David Amos (DeepMind); Sebastien Racaniere (DeepMind); David Pfau (DeepMind); Loic Matthey (DeepMind); Danilo Jimenez Rezende (DeepMind)
Towards Understanding Regularization in Batch Normalization Ping Luo (The Chinese University of Hong Kong); Xinjiang Wang (); Wenqi Shao (The Chinese University of HongKong)*; Zhanglin Peng (SenseTime)
Covariance in Physics and Convolutional Neural Networks Miranda Cheng (University of Amsterdam)*; Vassilis Anagiannis (University of Amsterdam); Maurice Weiler (University of Amsterdam); Pim de Haan (University of Amsterdam); Taco S. Cohen (Qualcomm AI Research); Max Welling (University of Amsterdam)
Meanfield theory of activation functions in Deep Neural Networks Mirco Milletari (Microsoft)*; Thiparat Chotibut (SUTD) ; Paolo E. Trevisanutto (National University of Singapore)
Finite size corrections for neural network Gaussian processes Joseph M Antognini (Whisper AI)*
Analysing the dynamics of online learning in over-parameterised two-layer neural networks Sebastian Goldt (Institut de Physique théorique, Paris)*; Madhu Advani (Harvard University); Andrew Saxe (University of Oxford); Florent Krzakala (École Normale Supérieure); Lenka Zdeborova (CEA Saclay)
A Halo Merger Tree Generation and Evaluation Framework Sandra Robles (Universidad Autónoma de Madrid); Jonathan Gómez (Pontificia Universidad Católica de Chile); Adín Ramírez Rivera (University of Campinas)*; Jenny Gonzáles (Pontificia Universidad Católica de Chile); Nelson Padilla (Pontificia Universidad Católica de Chile); Diego Dujovne (Universidad Diego Portales)
Learning Symmetries of Classical Integrable Systems Roberto Bondesan (Qualcomm AI Research)*, Austen Lamacraft (Cavendish Laboratory, University of Cambridge, UK)
Pathological Spectrum of the Fisher Information Matrix in Deep Neural Networks Ryo Karakida (National Institute of Advanced Industrial Science and Technology)*; Shotaro Akaho (AIST); Shun-ichi Amari (RIKEN)
How Noise during Training Affects the Hessian Spectrum Mingwei Wei (Northwestern University); David Schwab (Facebook AI Research)*
A Quantum Field Theory of Representation Learning Robert Bamler (University of California at Irvine)*; Stephan Mandt (University of California, Irivine)
Convergence Properties of Neural Networks on Separable Data Remi Tachet des Combes (Microsoft Research Montreal)*; Mohammad Pezeshki (Mila & University of Montreal); Samira Shabanian (Microsoft, Canada); Aaron Courville (MILA, Université de Montréal); Yoshua Bengio (Mila)
Universality and Capacity Metrics in Deep Neural Networks Michael Mahoney (University of California, Berkeley)*; Charles Martin (Calculation Consulting)
Asymptotics of Wide Networks from Feynman Diagrams Guy Gur-Ari (Google)*; Ethan Dyer (Google)
Deep Learning on the 2-Dimensional Ising Model to Extract the Crossover Region Nicholas Walker (Louisiana State Univ - Baton Rouge)*
Large Scale Structure of the Loss Landscape of Neural Networks Stanislav Fort (Stanford University)*; Stanislaw Jastrzebski (New York University)
Momentum Enables Large Batch Training Samuel L Smith (DeepMind)*; Erich Elsen (Google); Soham De (DeepMind)
Learning the Arrow of Time Nasim Rahaman (University of Heidelberg)*; Steffen Wolf (Heidelberg University); Anirudh Goyal (University of Montreal); Roman Remme (Heidelberg University); Yoshua Bengio (Mila)
Scale Steerable Filters for Locally Scale-Invariant Convolutional Neural Networks Rohan Ghosh (National University of Singapore)*; Anupam Gupta (National University of Singapore)
A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-Off Yaniv Blumenfeld (Technion)*; Dar Gilboa (Columbia University); Daniel Soudry (Technion)
Rethinking Complexity in Deep Learning: A View from Function Space Aristide Baratin (Mila, Université de Montréal)*; Thomas George (MILA, Université de Montréal); César Laurent (Mila, Université de Montréal); Valentin Thomas (MILA); Guillaume Lajoie (Université de Montréal, Mila); Simon Lacoste-Julien (Mila, Université de Montréal)
The Deep Learning Limit: Negative Neural Network eigenvalues just noise? Diego Granziol (Oxford)*; Stefan Zohren (University of Oxford); Stephen Roberts (Oxford); Dmitry P Vetrov (Higher School of Economics); Andrew Gordon Wilson (Cornell University); Timur Garipov (Samsung AI Center in Moscow)
Gradient descent in Gaussian random fields as a toy model for high-dimensional optimisation Mariano Chouza (Tower Research Capital); Stephen Roberts (Oxford); Stefan Zohren (University of Oxford)*
Deep Learning for Inverse Problems Abhejit Rajagopal (University of California, Santa Barbara)*; Vincent R Radzicki (University of California, Santa Barbara)
Author Information
Roman Novak (Google Brain)
Maxime Gabella (MAGMA Learning)
Frederic Dreyer (University of Oxford)
Theoretical physics researcher working on quantum chromodynamics and applications of machine learning for the LHC.
Siavash Golkar (New York University)
Anh Tong (Ulsan National Institute of Science and Technology)
Irina Higgins (DeepMind)
Irina Higgins is a research scientist at DeepMind, where she works in the Froniers team. Her work aims to bring together insights from the fields of neuroscience and physics to advance general artificial intelligence through improved representation learning. Before joining DeepMind, Irina was a British Psychological Society Undergraduate Award winner for her achievements as an undergraduate student in Experimental Psychology at Westminster University, followed by a DPhil at the Oxford Centre for Computational Neuroscience and Artificial Intelligence, where she focused on understanding the computational principles underlying speech processing in the auditory brain. During her DPhil, Irina also worked on developing poker AI, applying machine learning in the finance sector, and working on speech recognition at Google Research.
Mirco Milletari (Microsoft)
Joe Antognini (Whisper AI)
Sebastian Goldt (Institut de physique théorique (IPhT))
I'm an assistant professor working on theories of learning in neural networks.
Adín Ramírez Rivera (University of Campinas)
Roberto Bondesan (Qualcomm AI Research)
Ryo Karakida (National Institute of AIST)
Remi Tachet des Combes (Microsoft Research Montreal)
Michael Mahoney (UC Berkeley)
Nicholas Walker (Louisiana State University)
I am a graduate research assistant in the Department of Physics and Astronomy at Louisiana State University studying applications of machine learning to determining critical points and crossover regions in physical systems relating to condensed matter physics.
Stanislav Fort (Google AI)
Samuel Smith (DeepMind)
Rohan Ghosh (National University of Singapore)
Aristide Baratin (MILA)
Diego Granziol (Oxford)
Stephen Roberts (University of Oxford)
Dmitry Vetrov (Higher School of Economics, Samsung AI Center Moscow)
Andrew Wilson (Cornell University)
Andrew Gordon Wilson is faculty in the Courant Institute and Center for Data Science at NYU. His interests include probabilistic modelling, Gaussian processes, Bayesian statistics, physics inspired machine learning, and loss surfaces and generalization in deep learning. His webpage is https://cims.nyu.edu/~andrewgw.
César Laurent (MILA)
Valentin Thomas (MILA)
Simon Lacoste-Julien (Mila, University of Montreal)
Simon Lacoste-Julien is an associate professor at Mila and DIRO from Université de Montréal, and Canada CIFAR AI Chair holder. He also heads part time the SAIT AI Lab Montreal from Samsung. His research interests are machine learning and applied math, with applications in related fields like computer vision and natural language processing. He obtained a B.Sc. in math., physics and computer science from McGill, a PhD in computer science from UC Berkeley and a post-doc from the University of Cambridge. He spent a few years as a research faculty at INRIA and École normale supérieure in Paris before coming back to his roots in Montreal in 2016 to answer the call from Yoshua Bengio in growing the Montreal AI ecosystem.
Dar Gilboa (Columbia University)
Daniel Soudry (Technion)
Anupam Gupta (NUS)
Anirudh Goyal (Université de Montréal)
Yoshua Bengio (Montreal Institute for Learning Algorithms)
Erich Elsen (Google)
Soham De (DeepMind)
Stanislaw Jastrzebski (New York University)
Charles H Martin (Calculation Consulting)
Samira Shabanian (Microsoft, Canada)
Aaron Courville (Université de Montréal)
Shorato Akaho (The National Institute of Advanced Industrial Science and Technology)
Lenka Zdeborova (CNRS)
Ethan Dyer (Google)
Maurice Weiler (University of Amsterdam)
Pim de Haan (University of Amsterdam, visiting at UC Berkeley)
Taco Cohen (Qualcomm AI Research)
Max Welling (University of Amsterdam & Qualcomm)
Ping Luo (The University of Hong Kong)
zhanglin peng (SenseTime)
Nasim Rahaman (MPI for Intelligent Systems Tübingen / Mila, Quebec / University of Heidelberg)
Loic Matthey (DeepMind)
Danilo J. Rezende (DeepMind)
Danilo is a Senior Staff Research Scientist at Google DeepMind, where he works on probabilistic machine reasoning and learning algorithms. He has a BA in Physics and MSc in Theoretical Physics from Ecole Polytechnique (Palaiseau – France) and from the Institute of Theoretical Physics (SP – Brazil) and a Ph.D. in Computational Neuroscience at Ecole Polytechnique Federale de Lausanne, EPFL (Lausanne – Switzerland). His research focuses on scalable inference methods, generative models of complex data (such as images and video), applied probability, causal reasoning and unsupervised learning for decision-making.
Jaesik Choi (Ulsan National Institute of Science and Technology)
Kyle Cranmer (NYU)
Professor of Physics and Data Science at NYU. Executive director of Moore-Sloan data science environment at NYU. Member of ATLAS collaboration at CERN’s Large Hadron Collider (LHC). NeurIPS2016 keynote. Organizer of Deep Learning for Physical Sciences workshop at NeurIPS 2017.
Lechao Xiao (Google Research)
Jaehoon Lee (Google Brain)
Yasaman Bahri (Google Brain)
Jeffrey Pennington (Google Brain)
Greg Yang (Microsoft Research)
Jiri Hron (University of Cambridge)
Jascha Sohl-Dickstein (Google Brain)
Guy Gur-Ari (Google)
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Lenka Zdeborova -
2021 Workshop: Over-parameterization: Pitfalls and Opportunities »
Yasaman Bahri · Quanquan Gu · Amin Karbasi · Hanie Sedghi -
2021 Workshop: Beyond first-order methods in machine learning systems »
Albert S Berahas · Anastasios Kyrillidis · Fred Roosta · Amir Gholaminejad · Michael Mahoney · Rachael Tappenden · Raghu Bollapragada · Rixon Crane · J. Lyle Kim -
2021 : Invited Speakers' Panel »
Neeraja J Yadwadkar · Shalmali Joshi · Roberto Bondesan · Engineer Bainomugisha · Stephen Roberts -
2021 : Deployment and monitoring on constrained hardware and devices »
Cecilia Mascolo · Maria Nyamukuru · Ivan Kiskin · Partha Maji · Yunpeng Li · Stephen Roberts -
2021 Workshop: Tackling Climate Change with Machine Learning »
Hari Prasanna Das · Katarzyna Tokarska · Maria João Sousa · Meareg Hailemariam · David Rolnick · Xiaoxiang Zhu · Yoshua Bengio -
2021 : Machine Learning for Chip Design »
Roberto Bondesan -
2021 Workshop: INNF+: Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models »
Chin-Wei Huang · David Krueger · Rianne Van den Berg · George Papamakarios · Ricky T. Q. Chen · Danilo J. Rezende -
2021 Workshop: Challenges in Deploying and monitoring Machine Learning Systems »
Alessandra Tosi · Nathan Korda · Michael A Osborne · Stephen Roberts · Andrei Paleyes · Fariba Yousefi -
2021 : Opening remarks »
Alessandra Tosi · Nathan Korda · Fariba Yousefi · Andrei Paleyes · Stephen Roberts -
2021 Poster: SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes »
Sanyam Kapoor · Marc Finzi · Ke Alexander Wang · Andrew Wilson -
2021 Poster: Decomposed Mutual Information Estimation for Contrastive Representation Learning »
Alessandro Sordoni · Nouha Dziri · Hannes Schulz · Geoff Gordon · Philip Bachman · Remi Tachet des Combes -
2021 Test Of Time: Bayesian Learning via Stochastic Gradient Langevin Dynamics »
Yee Teh · Max Welling -
2021 Test Of Time: Test of Time Award »
Max Welling · Max Welling -
2021 Oral: SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes »
Sanyam Kapoor · Marc Finzi · Ke Alexander Wang · Andrew Wilson -
2021 Spotlight: Decomposed Mutual Information Estimation for Contrastive Representation Learning »
Alessandro Sordoni · Nouha Dziri · Hannes Schulz · Geoff Gordon · Philip Bachman · Remi Tachet des Combes -
2021 Poster: On Disentangled Representations Learned from Correlated Data »
Frederik Träuble · Elliot Creager · Niki Kilbertus · Francesco Locatello · Andrea Dittadi · Anirudh Goyal · Bernhard Schölkopf · Stefan Bauer -
2021 Poster: Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition »
Shengyang Sun · Jiaxin Shi · Andrew Wilson · Roger Grosse -
2021 Poster: HAWQ-V3: Dyadic Neural Network Quantization »
Zhewei Yao · Zhen Dong · Zhangcheng Zheng · Amir Gholaminejad · Jiali Yu · Eric Tan · Leyuan Wang · Qijing Huang · Yida Wang · Michael Mahoney · EECS Kurt Keutzer -
2021 Poster: Robust Representation Learning via Perceptual Similarity Metrics »
Saeid A Taghanaki · Kristy Choi · Amir Hosein Khasahmadi · Anirudh Goyal -
2021 Oral: On Disentangled Representations Learned from Correlated Data »
Frederik Träuble · Elliot Creager · Niki Kilbertus · Francesco Locatello · Andrea Dittadi · Anirudh Goyal · Bernhard Schölkopf · Stefan Bauer -
2021 Spotlight: Robust Representation Learning via Perceptual Similarity Metrics »
Saeid A Taghanaki · Kristy Choi · Amir Hosein Khasahmadi · Anirudh Goyal -
2021 Spotlight: HAWQ-V3: Dyadic Neural Network Quantization »
Zhewei Yao · Zhen Dong · Zhangcheng Zheng · Amir Gholaminejad · Jiali Yu · Eric Tan · Leyuan Wang · Qijing Huang · Yida Wang · Michael Mahoney · EECS Kurt Keutzer -
2021 Spotlight: Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition »
Shengyang Sun · Jiaxin Shi · Andrew Wilson · Roger Grosse -
2021 Poster: Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization »
Stanislaw Jastrzebski · Devansh Arpit · Oliver Astrand · Giancarlo Kerg · Huan Wang · Caiming Xiong · Richard Socher · Kyunghyun Cho · Krzysztof J Geras -
2021 Poster: An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming »
Minkai Xu · Wujie Wang · Shitong Luo · Chence Shi · Yoshua Bengio · Rafael Gomez-Bombarelli · Jian Tang -
2021 Poster: Can Subnetwork Structure Be the Key to Out-of-Distribution Generalization? »
Dinghuai Zhang · Kartik Ahuja · Yilun Xu · Yisen Wang · Aaron Courville -
2021 Poster: Tensor Programs IV: Feature Learning in Infinite-Width Neural Networks »
Greg Yang · Edward Hu -
2021 Poster: Tensor Programs IIb: Architectural Universality Of Neural Tangent Kernel Training Dynamics »
Greg Yang · Etai Littwin -
2021 Spotlight: An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming »
Minkai Xu · Wujie Wang · Shitong Luo · Chence Shi · Yoshua Bengio · Rafael Gomez-Bombarelli · Jian Tang -
2021 Oral: Can Subnetwork Structure Be the Key to Out-of-Distribution Generalization? »
Dinghuai Zhang · Kartik Ahuja · Yilun Xu · Yisen Wang · Aaron Courville -
2021 Spotlight: Tensor Programs IIb: Architectural Universality Of Neural Tangent Kernel Training Dynamics »
Greg Yang · Etai Littwin -
2021 Spotlight: Tensor Programs IV: Feature Learning in Infinite-Width Neural Networks »
Greg Yang · Edward Hu -
2021 Spotlight: Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization »
Stanislaw Jastrzebski · Devansh Arpit · Oliver Astrand · Giancarlo Kerg · Huan Wang · Caiming Xiong · Richard Socher · Kyunghyun Cho · Krzysztof J Geras -
2021 Poster: Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeed »
Maria Refinetti · Sebastian Goldt · FLORENT KRZAKALA · Lenka Zdeborova -
2021 Poster: Align, then memorise: the dynamics of learning with feedback alignment »
Maria Refinetti · Stéphane d'Ascoli · Ruben Ohana · Sebastian Goldt -
2021 Poster: Continuous Coordination As a Realistic Scenario for Lifelong Learning »
Hadi Nekoei · Akilesh Badrinaaraayanan · Aaron Courville · Sarath Chandar -
2021 Poster: On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent »
Shahar Azulay · Edward Moroshko · Mor Shpigel Nacson · Blake Woodworth · Nati Srebro · Amir Globerson · Daniel Soudry -
2021 Poster: The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning »
Roberto Bondesan · Max Welling -
2021 Spotlight: The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning »
Roberto Bondesan · Max Welling -
2021 Spotlight: Continuous Coordination As a Realistic Scenario for Lifelong Learning »
Hadi Nekoei · Akilesh Badrinaaraayanan · Aaron Courville · Sarath Chandar -
2021 Oral: On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent »
Shahar Azulay · Edward Moroshko · Mor Shpigel Nacson · Blake Woodworth · Nati Srebro · Amir Globerson · Daniel Soudry -
2021 Spotlight: Align, then memorise: the dynamics of learning with feedback alignment »
Maria Refinetti · Stéphane d'Ascoli · Ruben Ohana · Sebastian Goldt -
2021 Spotlight: Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeed »
Maria Refinetti · Sebastian Goldt · FLORENT KRZAKALA · Lenka Zdeborova -
2021 Poster: Whitening and Second Order Optimization Both Make Information in the Dataset Unusable During Training, and Can Reduce or Prevent Generalization »
Neha Wadia · Daniel Duckworth · Samuel Schoenholz · Ethan Dyer · Jascha Sohl-Dickstein -
2021 Poster: Structured Convolutional Kernel Networks for Airline Crew Scheduling »
Yassine Yaakoubi · Francois Soumis · Simon Lacoste-Julien -
2021 Poster: Out-of-Distribution Generalization via Risk Extrapolation (REx) »
David Krueger · Ethan Caballero · Joern-Henrik Jacobsen · Amy Zhang · Jonathan Binas · Dinghuai Zhang · Remi Le Priol · Aaron Courville -
2021 Poster: ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training »
Jianfei Chen · Lianmin Zheng · Zhewei Yao · Dequan Wang · Ion Stoica · Michael Mahoney · Joseph E Gonzalez -
2021 Poster: A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups »
Marc Finzi · Max Welling · Andrew Wilson -
2021 Poster: Beyond Variance Reduction: Understanding the True Impact of Baselines on Policy Optimization »
Wesley Chung · Valentin Thomas · Marlos C. Machado · Nicolas Le Roux -
2021 Poster: Affine Invariant Analysis of Frank-Wolfe on Strongly Convex Sets »
Thomas Kerdreux · Lewis Liu · Simon Lacoste-Julien · Damien Scieur -
2021 Poster: High-Performance Large-Scale Image Recognition Without Normalization »
Andy Brock · Soham De · Samuel Smith · Karen Simonyan -
2021 Poster: Accurate Post Training Quantization With Small Calibration Sets »
Itay Hubara · Yury Nahshan · Yair Hanani · Ron Banner · Daniel Soudry -
2021 Poster: Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies »
Paul Vicol · Luke Metz · Jascha Sohl-Dickstein -
2021 Poster: Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment »
Philip Ball · Cong Lu · Jack Parker-Holder · Stephen Roberts -
2021 Spotlight: Whitening and Second Order Optimization Both Make Information in the Dataset Unusable During Training, and Can Reduce or Prevent Generalization »
Neha Wadia · Daniel Duckworth · Samuel Schoenholz · Ethan Dyer · Jascha Sohl-Dickstein -
2021 Spotlight: Beyond Variance Reduction: Understanding the True Impact of Baselines on Policy Optimization »
Wesley Chung · Valentin Thomas · Marlos C. Machado · Nicolas Le Roux -
2021 Spotlight: Affine Invariant Analysis of Frank-Wolfe on Strongly Convex Sets »
Thomas Kerdreux · Lewis Liu · Simon Lacoste-Julien · Damien Scieur -
2021 Spotlight: High-Performance Large-Scale Image Recognition Without Normalization »
Andy Brock · Soham De · Samuel Smith · Karen Simonyan -
2021 Oral: Out-of-Distribution Generalization via Risk Extrapolation (REx) »
David Krueger · Ethan Caballero · Joern-Henrik Jacobsen · Amy Zhang · Jonathan Binas · Dinghuai Zhang · Remi Le Priol · Aaron Courville -
2021 Oral: ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training »
Jianfei Chen · Lianmin Zheng · Zhewei Yao · Dequan Wang · Ion Stoica · Michael Mahoney · Joseph E Gonzalez -
2021 Spotlight: Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment »
Philip Ball · Cong Lu · Jack Parker-Holder · Stephen Roberts -
2021 Spotlight: Accurate Post Training Quantization With Small Calibration Sets »
Itay Hubara · Yury Nahshan · Yair Hanani · Ron Banner · Daniel Soudry -
2021 Oral: Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies »
Paul Vicol · Luke Metz · Jascha Sohl-Dickstein -
2021 Spotlight: Structured Convolutional Kernel Networks for Airline Crew Scheduling »
Yassine Yaakoubi · Francois Soumis · Simon Lacoste-Julien -
2021 Oral: NeRF-VAE: A Geometry Aware 3D Scene Generative Model »
Adam Kosiorek · Heiko Strathmann · Daniel Zoran · Pol Moreno · Rosalia Schneider · Sona Mokra · Danilo J. Rezende -
2021 Oral: A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups »
Marc Finzi · Max Welling · Andrew Wilson -
2021 Poster: Federated Learning of User Verification Models Without Sharing Embeddings »
Hossein Hosseini · Hyunsin Park · Sungrack Yun · Christos Louizos · Joseph B Soriaga · Max Welling -
2021 Poster: NeRF-VAE: A Geometry Aware 3D Scene Generative Model »
Adam Kosiorek · Heiko Strathmann · Daniel Zoran · Pol Moreno · Rosalia Schneider · Sona Mokra · Danilo J. Rezende -
2021 Poster: Multiplicative Noise and Heavy Tails in Stochastic Optimization »
Liam Hodgkinson · Michael Mahoney -
2021 Poster: E(n) Equivariant Graph Neural Networks »
Victor Garcia Satorras · Emiel Hoogeboom · Max Welling -
2021 Poster: Continual Learning in the Teacher-Student Setup: Impact of Task Similarity »
Sebastian Lee · Sebastian Goldt · Andrew Saxe -
2021 Poster: What Are Bayesian Neural Network Posteriors Really Like? »
Pavel Izmailov · Sharad Vikram · Matthew Hoffman · Andrew Wilson -
2021 Poster: On Monotonic Linear Interpolation of Neural Network Parameters »
James Lucas · Juhan Bae · Michael Zhang · Stanislav Fort · Richard Zemel · Roger Grosse -
2021 Poster: Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling »
Gregory Benton · Wesley Maddox · Sanae Lotfi · Andrew Wilson -
2021 Poster: Self Normalizing Flows »
T. Anderson Keller · Jorn Peters · Priyank Jaini · Emiel Hoogeboom · Patrick Forré · Max Welling -
2021 Social: The ICML Debate: Should AI Research and Development Be Controlled by a Regulatory Body or Government Oversight? »
Yunpeng Li · Olga Isupova · Nika Haghtalab · Adam White · Diego Granziol -
2021 Spotlight: Multiplicative Noise and Heavy Tails in Stochastic Optimization »
Liam Hodgkinson · Michael Mahoney -
2021 Spotlight: E(n) Equivariant Graph Neural Networks »
Victor Garcia Satorras · Emiel Hoogeboom · Max Welling -
2021 Spotlight: Federated Learning of User Verification Models Without Sharing Embeddings »
Hossein Hosseini · Hyunsin Park · Sungrack Yun · Christos Louizos · Joseph B Soriaga · Max Welling -
2021 Spotlight: Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling »
Gregory Benton · Wesley Maddox · Sanae Lotfi · Andrew Wilson -
2021 Spotlight: Self Normalizing Flows »
T. Anderson Keller · Jorn Peters · Priyank Jaini · Emiel Hoogeboom · Patrick Forré · Max Welling -
2021 Spotlight: Continual Learning in the Teacher-Student Setup: Impact of Task Similarity »
Sebastian Lee · Sebastian Goldt · Andrew Saxe -
2021 Spotlight: On Monotonic Linear Interpolation of Neural Network Parameters »
James Lucas · Juhan Bae · Michael Zhang · Stanislav Fort · Richard Zemel · Roger Grosse -
2021 Oral: What Are Bayesian Neural Network Posteriors Really Like? »
Pavel Izmailov · Sharad Vikram · Matthew Hoffman · Andrew Wilson -
2021 : The Mystery of Generalization: Why Does Deep Learning Work? »
Jeffrey Pennington -
2021 Tutorial: Random Matrix Theory and ML (RMT+ML) »
Fabian Pedregosa · Courtney Paquette · Thomas Trogdon · Jeffrey Pennington -
2020 : Determinantal Point Processes in Randomized Numerical Linear Algebra »
Michael Mahoney -
2020 : QA for invited talk 4 Bengio »
Yoshua Bengio -
2020 : Invited talk 6: Likelihood Models for Science »
Kyle Cranmer -
2020 : Invited talk 4 Bengio »
Yoshua Bengio -
2020 : Contributed Talk 1: Provably Efficient Online Hyperparameter Optimization with Population-Based Bandits »
Jack Parker-Holder · Vu Nguyen · Stephen Roberts -
2020 : Keynote: Yoshua Bengio (Q&A) »
Yoshua Bengio -
2020 : Keynote: Yoshua Bengio »
Yoshua Bengio -
2020 Workshop: Inductive Biases, Invariances and Generalization in Reinforcement Learning »
Anirudh Goyal · Rosemary Nan Ke · Jane Wang · Stefan Bauer · Theophane Weber · Fabio Viola · Bernhard Schölkopf · Stefan Bauer -
2020 : Invited talk 1: Unifying VAEs and Flows »
Max Welling -
2020 Workshop: INNF+: Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models »
Chin-Wei Huang · David Krueger · Rianne Van den Berg · George Papamakarios · Chris Cremer · Ricky T. Q. Chen · Danilo J. Rezende -
2020 Workshop: Beyond first order methods in machine learning systems »
Albert S Berahas · Amir Gholaminejad · Anastasios Kyrillidis · Michael Mahoney · Fred Roosta -
2020 Workshop: Object-Oriented Learning: Perception, Representation, and Reasoning »
Sungjin Ahn · Adam Kosiorek · Jessica Hamrick · Sjoerd van Steenkiste · Yoshua Bengio -
2020 Workshop: MLRetrospectives: A Venue for Self-Reflection in ML Research »
Jessica Forde · Jesse Dodge · Mayoore Jaiswal · Rosanne Liu · Ryan Lowe · Rosanne Liu · Joelle Pineau · Yoshua Bengio -
2020 : Invited Talk: Kyle Cranmer »
Kyle Cranmer -
2020 Poster: Forecasting Sequential Data Using Consistent Koopman Autoencoders »
Omri Azencot · N. Benjamin Erichson · Vanessa Lin · Michael Mahoney -
2020 Poster: Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules »
Sarthak Mittal · Alex Lamb · Anirudh Goyal · Vikram Voleti · Murray Shanahan · Guillaume Lajoie · Michael Mozer · Yoshua Bengio -
2020 Poster: PowerNorm: Rethinking Batch Normalization in Transformers »
Sheng Shen · Zhewei Yao · Amir Gholaminejad · Michael Mahoney · Kurt Keutzer -
2020 Poster: Learning to Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning »
Sai Krishna Gottipati · Boris Sattarov · Sufeng Niu · Yashaswi Pathak · Haoran Wei · Shengchao Liu · Shengchao Liu · Simon Blackburn · Karam Thomas · Connor Coley · Jian Tang · Sarath Chandar · Yoshua Bengio -
2020 Poster: AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation »
Jae Hyun Lim · Aaron Courville · Christopher Pal · Chin-Wei Huang -
2020 Poster: Perceptual Generative Autoencoders »
Zijun Zhang · Ruixiang ZHANG · Zongpeng Li · Yoshua Bengio · Liam Paull -
2020 Poster: Semi-Supervised Learning with Normalizing Flows »
Pavel Izmailov · Polina Kirichenko · Marc Finzi · Andrew Wilson -
2020 Poster: Stochastic Hamiltonian Gradient Methods for Smooth Games »
Nicolas Loizou · Hugo Berard · Alexia Jolicoeur-Martineau · Pascal Vincent · Simon Lacoste-Julien · Ioannis Mitliagkas -
2020 Poster: Rigging the Lottery: Making All Tickets Winners »
Utku Evci · Trevor Gale · Jacob Menick · Pablo Samuel Castro · Erich Elsen -
2020 Poster: Countering Language Drift with Seeded Iterated Learning »
Yuchen Lu · Soumye Singhal · Florian Strub · Aaron Courville · Olivier Pietquin -
2020 Poster: On the Generalization Benefit of Noise in Stochastic Gradient Descent »
Samuel Smith · Erich Elsen · Soham De -
2020 Poster: Generalisation error in learning with random features and the hidden manifold model »
Federica Gerace · Bruno Loureiro · Florent Krzakala · Marc Mezard · Lenka Zdeborova -
2020 Poster: Involutive MCMC: a Unifying Framework »
Kirill Neklyudov · Max Welling · Evgenii Egorov · Dmitry Vetrov -
2020 Poster: Revisiting Fundamentals of Experience Replay »
William Fedus · Prajit Ramachandran · Rishabh Agarwal · Yoshua Bengio · Hugo Larochelle · Mark Rowland · Will Dabney -
2020 Poster: Small-GAN: Speeding up GAN Training using Core-Sets »
Samrath Sinha · Han Zhang · Anirudh Goyal · Yoshua Bengio · Hugo Larochelle · Augustus Odena -
2020 Poster: The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent »
Karthik Abinav Sankararaman · Soham De · Zheng Xu · W. Ronny Huang · Tom Goldstein -
2020 Poster: Randomly Projected Additive Gaussian Processes for Regression »
Ian Delbridge · David S Bindel · Andrew Wilson -
2020 Poster: Channel Equilibrium Networks for Learning Deep Representation »
Wenqi Shao · Shitao Tang · Xingang Pan · Ping Tan · Xiaogang Wang · Ping Luo -
2020 Poster: The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization »
Ben Adlam · Jeffrey Pennington -
2020 Poster: Infinite attention: NNGP and NTK for deep attention networks »
Jiri Hron · Yasaman Bahri · Jascha Sohl-Dickstein · Roman Novak -
2020 Poster: Beyond Signal Propagation: Is Feature Diversity Necessary in Deep Neural Network Initialization? »
Yaniv Blumenfeld · Dar Gilboa · Daniel Soudry -
2020 Poster: The Role of Regularization in Classification of High-dimensional Noisy Gaussian Mixture »
Francesca Mignacco · Florent Krzakala · Yue Lu · Pierfrancesco Urbani · Lenka Zdeborova -
2020 Poster: Ready Policy One: World Building Through Active Learning »
Philip Ball · Jack Parker-Holder · Aldo Pacchiano · Krzysztof Choromanski · Stephen Roberts -
2020 Poster: Normalizing Flows on Tori and Spheres »
Danilo J. Rezende · George Papamakarios · Sebastien Racaniere · Michael Albergo · Gurtej Kanwar · Phiala Shanahan · Kyle Cranmer -
2020 Poster: Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics »
Arsenii Kuznetsov · Pavel Shvechikov · Alexander Grishin · Dmitry Vetrov -
2020 Poster: Error Estimation for Sketched SVD via the Bootstrap »
Miles Lopes · N. Benjamin Erichson · Michael Mahoney -
2020 Poster: Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data »
Marc Finzi · Samuel Stanton · Pavel Izmailov · Andrew Wilson -
2020 Poster: Disentangling Trainability and Generalization in Deep Neural Networks »
Lechao Xiao · Jeffrey Pennington · Samuel Schoenholz -
2020 Poster: Bayesian Optimisation over Multiple Continuous and Categorical Inputs »
Binxin Ru · Ahsan Alvi · Vu Nguyen · Michael A Osborne · Stephen Roberts -
2020 Poster: Randomized Smoothing of All Shapes and Sizes »
Greg Yang · Tony Duan · J. Edward Hu · Hadi Salman · Ilya Razenshteyn · Jerry Li -
2020 Tutorial: Bayesian Deep Learning and a Probabilistic Perspective of Model Construction »
Andrew Wilson -
2020 Tutorial: Representation Learning Without Labels »
S. M. Ali Eslami · Irina Higgins · Danilo J. Rezende -
2019 : poster session I »
Nicholas Rhinehart · Yunhao Tang · Vinay Prabhu · Dian Ang Yap · Alexander Wang · Marc Finzi · Manoj Kumar · You Lu · Abhishek Kumar · Qi Lei · Michael Przystupa · Nicola De Cao · Polina Kirichenko · Pavel Izmailov · Andrew Wilson · Jakob Kruse · Diego Mesquita · Mario Lezcano Casado · Thomas Müller · Keir Simmons · Andrei Atanov -
2019 : AI Commons »
Yoshua Bengio -
2019 : Poster spotlights #1 »
Siheng Chen · Vedran Hadziosmanovic · Adín Ramírez Rivera -
2019 : Opening remarks »
Yoshua Bengio -
2019 Workshop: AI For Social Good (AISG) »
Margaux Luck · Kris Sankaran · Tristan Sylvain · Sean McGregor · Jonnie Penn · Girmaw Abebe Tadesse · Virgile Sylvain · Myriam Côté · Lester Mackey · Rayid Ghani · Yoshua Bengio -
2019 Workshop: Learning and Reasoning with Graph-Structured Representations »
Ethan Fetaya · Zhiting Hu · Thomas Kipf · Yujia Li · Xiaodan Liang · Renjie Liao · Raquel Urtasun · Hao Wang · Max Welling · Eric Xing · Richard Zemel -
2019 Workshop: Invertible Neural Networks and Normalizing Flows »
Chin-Wei Huang · David Krueger · Rianne Van den Berg · George Papamakarios · Aidan Gomez · Chris Cremer · Aaron Courville · Ricky T. Q. Chen · Danilo J. Rezende -
2019 : Panel Discussion »
Yoshua Bengio · Andrew Ng · Raia Hadsell · John Platt · Claire Monteleoni · Jennifer Chayes -
2019 : Learning the Arrow of Time »
Nasim Rahaman -
2019 : Deep Learning on the 2-Dimensional Ising Model to Extract the Crossover Region »
Nicholas Walker -
2019 : A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-Off »
Dar Gilboa -
2019 : Panel Discussion (moderator: Tom Dietterich) »
Max Welling · Kilian Weinberger · Terrance Boult · Dawn Song · Thomas Dietterich -
2019 : Asymptotics of Wide Networks from Feynman Diagrams »
Guy Gur-Ari -
2019 : Understanding overparameterized neural networks »
Jascha Sohl-Dickstein -
2019 : Subspace Inference for Bayesian Deep Learning »
Polina Kirichenko · Pavel Izmailov · Andrew Wilson -
2019 : Personalized Visualization of the Impact of Climate Change »
Yoshua Bengio -
2019 : Convergence Properties of Neural Networks on Separable Data »
Remi Tachet des Combes -
2019 : Networking Lunch (provided) + Poster Session »
Abraham Stanway · Alex Robson · Aneesh Rangnekar · Ashesh Chattopadhyay · Ashley Pilipiszyn · Benjamin LeRoy · Bolong Cheng · Ce Zhang · Chaopeng Shen · Christian Schroeder · Christian Clough · Clement DUHART · Clement Fung · Cozmin Ududec · Dali Wang · David Dao · di wu · Dimitrios Giannakis · Dino Sejdinovic · Doina Precup · Duncan Watson-Parris · Gege Wen · George Chen · Gopal Erinjippurath · Haifeng Li · Han Zou · Herke van Hoof · Hillary A Scannell · Hiroshi Mamitsuka · Hongbao Zhang · Jaegul Choo · James Wang · James Requeima · Jessica Hwang · Jinfan Xu · Johan Mathe · Jonathan Binas · Joonseok Lee · Kalai Ramea · Kate Duffy · Kevin McCloskey · Kris Sankaran · Lester Mackey · Letif Mones · Loubna Benabbou · Lynn Kaack · Matthew Hoffman · Mayur Mudigonda · Mehrdad Mahdavi · Michael McCourt · Mingchao Jiang · Mohammad Mahdi Kamani · Neel Guha · Niccolo Dalmasso · Nick Pawlowski · Nikola Milojevic-Dupont · Paulo Orenstein · Pedram Hassanzadeh · Pekka Marttinen · Ramesh Nair · Sadegh Farhang · Samuel Kaski · Sandeep Manjanna · Sasha Luccioni · Shuby Deshpande · Soo Kim · Soukayna Mouatadid · Sunghyun Park · Tao Lin · Telmo Felgueira · Thomas Hornigold · Tianle Yuan · Tom Beucler · Tracy Cui · Volodymyr Kuleshov · Wei Yu · yang song · Ydo Wexler · Yoshua Bengio · Zhecheng Wang · Zhuangfang Yi · Zouheir Malki -
2019 : Analyzing the dynamics of online learning in over-parameterized two-layer neural networks »
Sebastian Goldt -
2019 : Why Deep Learning Works: Traditional and Heavy-Tailed Implicit Self-Regularization in Deep Neural Networks »
Michael Mahoney -
2019 : On the Interplay between Physics and Deep Learning »
Kyle Cranmer -
2019 : Poster Session 1 (all papers) »
Matilde Gargiani · Yochai Zur · Chaim Baskin · Evgenii Zheltonozhskii · Liam Li · Ameet Talwalkar · Xuedong Shang · Harkirat Singh Behl · Atilim Gunes Baydin · Ivo Couckuyt · Tom Dhaene · Chieh Lin · Wei Wei · Min Sun · Orchid Majumder · Michele Donini · Yoshihiko Ozaki · Ryan P. Adams · Christian Geißler · Ping Luo · zhanglin peng · · Ruimao Zhang · John Langford · Rich Caruana · Debadeepta Dey · Charles Weill · Xavi Gonzalvo · Scott Yang · Scott Yak · Eugen Hotaj · Vladimir Macko · Mehryar Mohri · Corinna Cortes · Stefan Webb · Jonathan Chen · Martin Jankowiak · Noah Goodman · Aaron Klein · Frank Hutter · Mojan Javaheripi · Mohammad Samragh · Sungbin Lim · Taesup Kim · SUNGWOONG KIM · Michael Volpp · Iddo Drori · Yamuna Krishnamurthy · Kyunghyun Cho · Stanislaw Jastrzebski · Quentin de Laroussilhe · Mingxing Tan · Xiao Ma · Neil Houlsby · Andrea Gesmundo · Zalán Borsos · Krzysztof Maziarz · Felipe Petroski Such · Joel Lehman · Kenneth Stanley · Jeff Clune · Pieter Gijsbers · Joaquin Vanschoren · Felix Mohr · Eyke Hüllermeier · Zheng Xiong · Wenpeng Zhang · Wenwu Zhu · Weijia Shao · Aleksandra Faust · Michal Valko · Michael Y Li · Hugo Jair Escalante · Marcel Wever · Andrey Khorlin · Tara Javidi · Anthony Francis · Saurajit Mukherjee · Jungtaek Kim · Michael McCourt · Saehoon Kim · Tackgeun You · Seungjin Choi · Nicolas Knudde · Alexander Tornede · Ghassen Jerfel -
2019 : Poster spotlights »
Roman Novak · Frederic Dreyer · Siavash Golkar · Irina Higgins · Joe Antognini · Ryo Karakida · Rohan Ghosh -
2019 : Keynote by Max Welling: A Nonparametric Bayesian Approach to Deep Learning (without GPs) »
Max Welling -
2019 : Loss landscape and behaviour of algorithms in the spiked matrix-tensor model »
Lenka Zdeborova -
2019 Workshop: Climate Change: How Can AI Help? »
David Rolnick · Alexandre Lacoste · Tegan Maharaj · Jennifer Chayes · Yoshua Bengio -
2019 Workshop: Joint Workshop on On-Device Machine Learning & Compact Deep Neural Network Representations (ODML-CDNNR) »
Sujith Ravi · Zornitsa Kozareva · Lixin Fan · Max Welling · Yurong Chen · Werner Bailer · Brian Kulis · Haoji Hu · Jonathan Dekhtiar · Yingyan Lin · Diana Marculescu -
2019 Workshop: Theoretical Physics for Deep Learning »
Jaehoon Lee · Jeffrey Pennington · Yasaman Bahri · Max Welling · Surya Ganguli · Joan Bruna -
2019 : Opening Remarks »
Jaehoon Lee · Jeffrey Pennington · Yasaman Bahri · Max Welling · Surya Ganguli · Joan Bruna -
2019 Poster: Understanding and correcting pathologies in the training of learned optimizers »
Luke Metz · Niru Maheswaranathan · Jeremy Nixon · Daniel Freeman · Jascha Sohl-Dickstein -
2019 Poster: Stochastic Beams and Where To Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement »
Wouter Kool · Herke van Hoof · Max Welling -
2019 Poster: State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations »
Alex Lamb · Jonathan Binas · Anirudh Goyal · Sandeep Subramanian · Ioannis Mitliagkas · Yoshua Bengio · Michael Mozer -
2019 Poster: Guided evolutionary strategies: augmenting random search with surrogate gradients »
Niru Maheswaranathan · Luke Metz · George Tucker · Dami Choi · Jascha Sohl-Dickstein -
2019 Poster: Parameter-Efficient Transfer Learning for NLP »
Neil Houlsby · Andrei Giurgiu · Stanislaw Jastrzebski · Bruna Morrone · Quentin de Laroussilhe · Andrea Gesmundo · Mona Attariyan · Sylvain Gelly -
2019 Poster: Unreproducible Research is Reproducible »
Xavier Bouthillier · César Laurent · Pascal Vincent -
2019 Poster: On the Spectral Bias of Neural Networks »
Nasim Rahaman · Aristide Baratin · Devansh Arpit · Felix Draxler · Min Lin · Fred Hamprecht · Yoshua Bengio · Aaron Courville -
2019 Poster: Hierarchical Importance Weighted Autoencoders »
Chin-Wei Huang · Kris Sankaran · Eeshan Dhekane · Alexandre Lacoste · Aaron Courville -
2019 Poster: Multi-Object Representation Learning with Iterative Variational Inference »
Klaus Greff · Raphael Lopez Kaufman · Rishabh Kabra · Nicholas Watters · Christopher Burgess · Daniel Zoran · Loic Matthey · Matthew Botvinick · Alexander Lerchner -
2019 Oral: Hierarchical Importance Weighted Autoencoders »
Chin-Wei Huang · Kris Sankaran · Eeshan Dhekane · Alexandre Lacoste · Aaron Courville -
2019 Oral: Guided evolutionary strategies: augmenting random search with surrogate gradients »
Niru Maheswaranathan · Luke Metz · George Tucker · Dami Choi · Jascha Sohl-Dickstein -
2019 Oral: Multi-Object Representation Learning with Iterative Variational Inference »
Klaus Greff · Raphael Lopez Kaufman · Rishabh Kabra · Nicholas Watters · Christopher Burgess · Daniel Zoran · Loic Matthey · Matthew Botvinick · Alexander Lerchner -
2019 Oral: Unreproducible Research is Reproducible »
Xavier Bouthillier · César Laurent · Pascal Vincent -
2019 Oral: On the Spectral Bias of Neural Networks »
Nasim Rahaman · Aristide Baratin · Devansh Arpit · Felix Draxler · Min Lin · Fred Hamprecht · Yoshua Bengio · Aaron Courville -
2019 Oral: Parameter-Efficient Transfer Learning for NLP »
Neil Houlsby · Andrei Giurgiu · Stanislaw Jastrzebski · Bruna Morrone · Quentin de Laroussilhe · Andrea Gesmundo · Mona Attariyan · Sylvain Gelly -
2019 Oral: Understanding and correcting pathologies in the training of learned optimizers »
Luke Metz · Niru Maheswaranathan · Jeremy Nixon · Daniel Freeman · Jascha Sohl-Dickstein -
2019 Oral: Stochastic Beams and Where To Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement »
Wouter Kool · Herke van Hoof · Max Welling -
2019 Oral: State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations »
Alex Lamb · Jonathan Binas · Anirudh Goyal · Sandeep Subramanian · Ioannis Mitliagkas · Yoshua Bengio · Michael Mozer -
2019 Poster: Differentiable Dynamic Normalization for Learning Deep Representation »
Ping Luo · Peng Zhanglin · Shao Wenqi · Zhang ruimao · Ren jiamin · Wu lingyun -
2019 Poster: Simple Black-box Adversarial Attacks »
Chuan Guo · Jacob Gardner · Yurong You · Andrew Wilson · Kilian Weinberger -
2019 Poster: Efficient Dictionary Learning with Gradient Descent »
Dar Gilboa · Sam Buchanan · John Wright -
2019 Poster: Traditional and Heavy Tailed Self Regularization in Neural Network Models »
Michael Mahoney · Charles H Martin -
2019 Poster: Discovering Latent Covariance Structures for Multiple Time Series »
Anh Tong · Jaesik Choi -
2019 Poster: Emerging Convolutions for Generative Normalizing Flows »
Emiel Hoogeboom · Rianne Van den Berg · Max Welling -
2019 Poster: Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation »
Ahsan Alvi · Binxin Ru · Jan-Peter Calliess · Stephen Roberts · Michael A Osborne -
2019 Oral: Differentiable Dynamic Normalization for Learning Deep Representation »
Ping Luo · Peng Zhanglin · Shao Wenqi · Zhang ruimao · Ren jiamin · Wu lingyun -
2019 Oral: Discovering Latent Covariance Structures for Multiple Time Series »
Anh Tong · Jaesik Choi -
2019 Oral: Efficient Dictionary Learning with Gradient Descent »
Dar Gilboa · Sam Buchanan · John Wright -
2019 Oral: Traditional and Heavy Tailed Self Regularization in Neural Network Models »
Michael Mahoney · Charles H Martin -
2019 Oral: Simple Black-box Adversarial Attacks »
Chuan Guo · Jacob Gardner · Yurong You · Andrew Wilson · Kilian Weinberger -
2019 Oral: Emerging Convolutions for Generative Normalizing Flows »
Emiel Hoogeboom · Rianne Van den Berg · Max Welling -
2019 Oral: Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation »
Ahsan Alvi · Binxin Ru · Jan-Peter Calliess · Stephen Roberts · Michael A Osborne -
2019 Poster: SWALP : Stochastic Weight Averaging in Low Precision Training »
Guandao Yang · Tianyi Zhang · Polina Kirichenko · Junwen Bai · Andrew Wilson · Christopher De Sa -
2019 Poster: Safe Policy Improvement with Baseline Bootstrapping »
Romain Laroche · Paul TRICHELAIR · Remi Tachet des Combes -
2019 Poster: Passed & Spurious: Descent Algorithms and Local Minima in Spiked Matrix-Tensor Models »
Stefano Sarao Mannelli · Florent Krzakala · Pierfrancesco Urbani · Lenka Zdeborova -
2019 Poster: Manifold Mixup: Better Representations by Interpolating Hidden States »
Vikas Verma · Alex Lamb · Christopher Beckham · Amir Najafi · Ioannis Mitliagkas · David Lopez-Paz · Yoshua Bengio -
2019 Poster: Gauge Equivariant Convolutional Networks and the Icosahedral CNN »
Taco Cohen · Maurice Weiler · Berkay Kicanaoglu · Max Welling -
2019 Poster: The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study »
Daniel Park · Jascha Sohl-Dickstein · Quoc Le · Samuel Smith -
2019 Poster: GMNN: Graph Markov Neural Networks »
Meng Qu · Yoshua Bengio · Jian Tang -
2019 Poster: Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models »
Mor Shpigel Nacson · Suriya Gunasekar · Jason Lee · Nati Srebro · Daniel Soudry -
2019 Oral: SWALP : Stochastic Weight Averaging in Low Precision Training »
Guandao Yang · Tianyi Zhang · Polina Kirichenko · Junwen Bai · Andrew Wilson · Christopher De Sa -
2019 Oral: The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study »
Daniel Park · Jascha Sohl-Dickstein · Quoc Le · Samuel Smith -
2019 Oral: Gauge Equivariant Convolutional Networks and the Icosahedral CNN »
Taco Cohen · Maurice Weiler · Berkay Kicanaoglu · Max Welling -
2019 Oral: Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models »
Mor Shpigel Nacson · Suriya Gunasekar · Jason Lee · Nati Srebro · Daniel Soudry -
2019 Oral: Safe Policy Improvement with Baseline Bootstrapping »
Romain Laroche · Paul TRICHELAIR · Remi Tachet des Combes -
2019 Oral: GMNN: Graph Markov Neural Networks »
Meng Qu · Yoshua Bengio · Jian Tang -
2019 Oral: Passed & Spurious: Descent Algorithms and Local Minima in Spiked Matrix-Tensor Models »
Stefano Sarao Mannelli · Florent Krzakala · Pierfrancesco Urbani · Lenka Zdeborova -
2019 Oral: Manifold Mixup: Better Representations by Interpolating Hidden States »
Vikas Verma · Alex Lamb · Christopher Beckham · Amir Najafi · Ioannis Mitliagkas · David Lopez-Paz · Yoshua Bengio -
2018 Poster: Out-of-sample extension of graph adjacency spectral embedding »
Keith Levin · Fred Roosta · Michael Mahoney · Carey Priebe -
2018 Poster: Attention-based Deep Multiple Instance Learning »
Maximilian Ilse · Jakub Tomczak · Max Welling -
2018 Poster: Constant-Time Predictive Distributions for Gaussian Processes »
Geoff Pleiss · Jacob Gardner · Kilian Weinberger · Andrew Wilson -
2018 Poster: Efficient Neural Audio Synthesis »
Nal Kalchbrenner · Erich Elsen · Karen Simonyan · Seb Noury · Norman Casagrande · Edward Lockhart · Florian Stimberg · Aäron van den Oord · Sander Dieleman · Koray Kavukcuoglu -
2018 Poster: Mutual Information Neural Estimation »
Mohamed Belghazi · Aristide Baratin · Sai Rajeswar · Sherjil Ozair · Yoshua Bengio · R Devon Hjelm · Aaron Courville -
2018 Oral: Attention-based Deep Multiple Instance Learning »
Maximilian Ilse · Jakub Tomczak · Max Welling -
2018 Oral: Constant-Time Predictive Distributions for Gaussian Processes »
Geoff Pleiss · Jacob Gardner · Kilian Weinberger · Andrew Wilson -
2018 Oral: Efficient Neural Audio Synthesis »
Nal Kalchbrenner · Erich Elsen · Karen Simonyan · Seb Noury · Norman Casagrande · Edward Lockhart · Florian Stimberg · Aäron van den Oord · Sander Dieleman · Koray Kavukcuoglu -
2018 Oral: Mutual Information Neural Estimation »
Mohamed Belghazi · Aristide Baratin · Sai Rajeswar · Sherjil Ozair · Yoshua Bengio · R Devon Hjelm · Aaron Courville -
2018 Oral: Out-of-sample extension of graph adjacency spectral embedding »
Keith Levin · Fred Roosta · Michael Mahoney · Carey Priebe -
2018 Poster: Fast Information-theoretic Bayesian Optimisation »
Binxin Ru · Michael A Osborne · Mark Mcleod · Diego Granziol -
2018 Poster: Optimization, fast and slow: optimally switching between local and Bayesian optimization »
Mark McLeod · Stephen Roberts · Michael A Osborne -
2018 Poster: Focused Hierarchical RNNs for Conditional Sequence Processing »
Rosemary Nan Ke · Konrad Zolna · Alessandro Sordoni · Zhouhan Lin · Adam Trischler · Yoshua Bengio · Joelle Pineau · Laurent Charlin · Christopher Pal -
2018 Poster: Variational Bayesian dropout: pitfalls and fixes »
Jiri Hron · Alexander Matthews · Zoubin Ghahramani -
2018 Poster: Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap »
Miles Lopes · Shusen Wang · Michael Mahoney -
2018 Poster: Characterizing Implicit Bias in Terms of Optimization Geometry »
Suriya Gunasekar · Jason Lee · Daniel Soudry · Nati Srebro -
2018 Poster: Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks »
Minmin Chen · Jeffrey Pennington · Samuel Schoenholz -
2018 Oral: Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks »
Minmin Chen · Jeffrey Pennington · Samuel Schoenholz -
2018 Oral: Variational Bayesian dropout: pitfalls and fixes »
Jiri Hron · Alexander Matthews · Zoubin Ghahramani -
2018 Oral: Optimization, fast and slow: optimally switching between local and Bayesian optimization »
Mark McLeod · Stephen Roberts · Michael A Osborne -
2018 Oral: Characterizing Implicit Bias in Terms of Optimization Geometry »
Suriya Gunasekar · Jason Lee · Daniel Soudry · Nati Srebro -
2018 Oral: Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap »
Miles Lopes · Shusen Wang · Michael Mahoney -
2018 Oral: Fast Information-theoretic Bayesian Optimisation »
Binxin Ru · Michael A Osborne · Mark Mcleod · Diego Granziol -
2018 Oral: Focused Hierarchical RNNs for Conditional Sequence Processing »
Rosemary Nan Ke · Konrad Zolna · Alessandro Sordoni · Zhouhan Lin · Adam Trischler · Yoshua Bengio · Joelle Pineau · Laurent Charlin · Christopher Pal -
2018 Invited Talk: Intelligence per Kilowatthour »
Max Welling -
2018 Poster: Neural Relational Inference for Interacting Systems »
Thomas Kipf · Ethan Fetaya · Kuan-Chieh Wang · Max Welling · Richard Zemel -
2018 Poster: Generative Temporal Models with Spatial Memory for Partially Observed Environments »
Marco Fraccaro · Danilo J. Rezende · Yori Zwols · Alexander Pritzel · S. M. Ali Eslami · Fabio Viola -
2018 Poster: BOCK : Bayesian Optimization with Cylindrical Kernels »
ChangYong Oh · Efstratios Gavves · Max Welling -
2018 Poster: Conditional Neural Processes »
Marta Garnelo · Dan Rosenbaum · Chris Maddison · Tiago Ramalho · David Saxton · Murray Shanahan · Yee Teh · Danilo J. Rezende · S. M. Ali Eslami -
2018 Poster: Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks »
Lechao Xiao · Yasaman Bahri · Jascha Sohl-Dickstein · Samuel Schoenholz · Jeffrey Pennington -
2018 Poster: Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling »
kyowoon Lee · Sol-A Kim · Jaesik Choi · Seong-Whan Lee -
2018 Oral: Neural Relational Inference for Interacting Systems »
Thomas Kipf · Ethan Fetaya · Kuan-Chieh Wang · Max Welling · Richard Zemel -
2018 Oral: Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling »
kyowoon Lee · Sol-A Kim · Jaesik Choi · Seong-Whan Lee -
2018 Oral: Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks »
Lechao Xiao · Yasaman Bahri · Jascha Sohl-Dickstein · Samuel Schoenholz · Jeffrey Pennington -
2018 Oral: Generative Temporal Models with Spatial Memory for Partially Observed Environments »
Marco Fraccaro · Danilo J. Rezende · Yori Zwols · Alexander Pritzel · S. M. Ali Eslami · Fabio Viola -
2018 Oral: Conditional Neural Processes »
Marta Garnelo · Dan Rosenbaum · Chris Maddison · Tiago Ramalho · David Saxton · Murray Shanahan · Yee Teh · Danilo J. Rezende · S. M. Ali Eslami -
2018 Oral: BOCK : Bayesian Optimization with Cylindrical Kernels »
ChangYong Oh · Efstratios Gavves · Max Welling -
2017 Workshop: Reproducibility in Machine Learning Research »
Rosemary Nan Ke · Anirudh Goyal · Alex Lamb · Joelle Pineau · Samy Bengio · Yoshua Bengio -
2017 Poster: Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging »
Shusen Wang · Alex Gittens · Michael Mahoney -
2017 Poster: Multiplicative Normalizing Flows for Variational Bayesian Neural Networks »
Christos Louizos · Max Welling -
2017 Poster: Variational Dropout Sparsifies Deep Neural Networks »
Dmitry Molchanov · Arsenii Ashukha · Dmitry Vetrov -
2017 Talk: Variational Dropout Sparsifies Deep Neural Networks »
Dmitry Molchanov · Arsenii Ashukha · Dmitry Vetrov -
2017 Talk: Multiplicative Normalizing Flows for Variational Bayesian Neural Networks »
Christos Louizos · Max Welling -
2017 Poster: Learning Deep Architectures via Generalized Whitened Neural Networks »
Ping Luo -
2017 Poster: Sharp Minima Can Generalize For Deep Nets »
Laurent Dinh · Razvan Pascanu · Samy Bengio · Yoshua Bengio -
2017 Poster: Geometry of Neural Network Loss Surfaces via Random Matrix Theory »
Jeffrey Pennington · Yasaman Bahri -
2017 Poster: Input Switched Affine Networks: An RNN Architecture Designed for Interpretability »
Jakob Foerster · Justin Gilmer · Jan Chorowski · Jascha Sohl-Dickstein · David Sussillo -
2017 Poster: Learned Optimizers that Scale and Generalize »
Olga Wichrowska · Niru Maheswaranathan · Matthew Hoffman · Sergio Gómez Colmenarejo · Misha Denil · Nando de Freitas · Jascha Sohl-Dickstein -
2017 Poster: Capacity Releasing Diffusion for Speed and Locality. »
Di Wang · Kimon Fountoulakis · Monika Henzinger · Michael Mahoney · Satish Rao -
2017 Poster: A Closer Look at Memorization in Deep Networks »
David Krueger · Yoshua Bengio · Stanislaw Jastrzebski · Maxinder S. Kanwal · Nicolas Ballas · Asja Fischer · Emmanuel Bengio · Devansh Arpit · Tegan Maharaj · Aaron Courville · Simon Lacoste-Julien -
2017 Talk: A Closer Look at Memorization in Deep Networks »
David Krueger · Yoshua Bengio · Stanislaw Jastrzebski · Maxinder S. Kanwal · Nicolas Ballas · Asja Fischer · Emmanuel Bengio · Devansh Arpit · Tegan Maharaj · Aaron Courville · Simon Lacoste-Julien -
2017 Talk: Input Switched Affine Networks: An RNN Architecture Designed for Interpretability »
Jakob Foerster · Justin Gilmer · Jan Chorowski · Jascha Sohl-Dickstein · David Sussillo -
2017 Talk: Capacity Releasing Diffusion for Speed and Locality. »
Di Wang · Kimon Fountoulakis · Monika Henzinger · Michael Mahoney · Satish Rao -
2017 Talk: Learning Deep Architectures via Generalized Whitened Neural Networks »
Ping Luo -
2017 Talk: Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging »
Shusen Wang · Alex Gittens · Michael Mahoney -
2017 Poster: DARLA: Improving Zero-Shot Transfer in Reinforcement Learning »
Irina Higgins · Arka Pal · Andrei A Rusu · Loic Matthey · Christopher Burgess · Alexander Pritzel · Matthew Botvinick · Charles Blundell · Alexander Lerchner -
2017 Poster: On the Expressive Power of Deep Neural Networks »
Maithra Raghu · Ben Poole · Surya Ganguli · Jon Kleinberg · Jascha Sohl-Dickstein -
2017 Talk: Learned Optimizers that Scale and Generalize »
Olga Wichrowska · Niru Maheswaranathan · Matthew Hoffman · Sergio Gómez Colmenarejo · Misha Denil · Nando de Freitas · Jascha Sohl-Dickstein -
2017 Talk: DARLA: Improving Zero-Shot Transfer in Reinforcement Learning »
Irina Higgins · Arka Pal · Andrei A Rusu · Loic Matthey · Christopher Burgess · Alexander Pritzel · Matthew Botvinick · Charles Blundell · Alexander Lerchner -
2017 Talk: On the Expressive Power of Deep Neural Networks »
Maithra Raghu · Ben Poole · Surya Ganguli · Jon Kleinberg · Jascha Sohl-Dickstein -
2017 Talk: Geometry of Neural Network Loss Surfaces via Random Matrix Theory »
Jeffrey Pennington · Yasaman Bahri -
2017 Talk: Sharp Minima Can Generalize For Deep Nets »
Laurent Dinh · Razvan Pascanu · Samy Bengio · Yoshua Bengio