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Opening Remarks
Jaehoon Lee · Jeffrey Pennington · Yasaman Bahri · Max Welling · Surya Ganguli · Joan Bruna
Author Information
Jaehoon Lee (Google Brain)
Jeffrey Pennington (Google Brain)
Yasaman Bahri (Google Brain)
Max Welling (University of Amsterdam & Qualcomm)
Surya Ganguli (Stanford)
Joan Bruna (New York University)
More from the Same Authors
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2022 : Path Integral Stochastic Optimal Control for Sampling Transition Paths »
Lars Holdijk · Yuanqi Du · Priyank Jaini · Ferry Hooft · Bernd Ensing · Max Welling -
2022 : Pre-Training on a Data Diet: Identifying Sufficient Examples for Early Training »
Mansheej Paul · Brett Larsen · Surya Ganguli · Jonathan Frankle · Gintare Karolina Dziugaite -
2023 Poster: Beyond the Edge of Stability via Two-step Gradient Updates »
Lei Chen · Joan Bruna -
2023 Poster: Neural Wave Machines: Learning Spatiotemporally Structured Representations with Locally Coupled Oscillatory Recurrent Neural Networks »
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2023 Poster: Conditionally Strongly Log-Concave Generative Models »
Florentin Guth · Etienne Lempereur · Joan Bruna · Stéphane Mallat -
2023 Poster: Geometric Clifford Algebra Networks »
David Ruhe · Jayesh K. Gupta · Steven De Keninck · Max Welling · Johannes Brandstetter -
2023 Poster: Second-order regression models exhibit progressive sharpening to the edge of stability »
Atish Agarwala · Fabian Pedregosa · Jeffrey Pennington -
2023 Poster: Latent Traversals in Generative Models as Potential Flows »
Yue Song · T. Anderson Keller · Nicu Sebe · Max Welling -
2023 Workshop: Structured Probabilistic Inference and Generative Modeling »
Dinghuai Zhang · Yuanqi Du · Chenlin Meng · Shawn Tan · Yingzhen Li · Max Welling · Yoshua Bengio -
2022 Poster: Lie Point Symmetry Data Augmentation for Neural PDE Solvers »
Johannes Brandstetter · Max Welling · Daniel Worrall -
2022 Spotlight: Lie Point Symmetry Data Augmentation for Neural PDE Solvers »
Johannes Brandstetter · Max Welling · Daniel Worrall -
2022 Poster: Synergy and Symmetry in Deep Learning: Interactions between the Data, Model, and Inference Algorithm »
Lechao Xiao · Jeffrey Pennington -
2022 Poster: Extended Unconstrained Features Model for Exploring Deep Neural Collapse »
Tom Tirer · Joan Bruna -
2022 Poster: Wide Bayesian neural networks have a simple weight posterior: theory and accelerated sampling »
Jiri Hron · Roman Novak · Jeffrey Pennington · Jascha Sohl-Dickstein -
2022 Spotlight: Synergy and Symmetry in Deep Learning: Interactions between the Data, Model, and Inference Algorithm »
Lechao Xiao · Jeffrey Pennington -
2022 Spotlight: Wide Bayesian neural networks have a simple weight posterior: theory and accelerated sampling »
Jiri Hron · Roman Novak · Jeffrey Pennington · Jascha Sohl-Dickstein -
2022 Spotlight: Extended Unconstrained Features Model for Exploring Deep Neural Collapse »
Tom Tirer · Joan Bruna -
2022 Poster: Equivariant Diffusion for Molecule Generation in 3D »
Emiel Hoogeboom · Victor Garcia Satorras · Clément Vignac · Max Welling -
2022 Oral: Equivariant Diffusion for Molecule Generation in 3D »
Emiel Hoogeboom · Victor Garcia Satorras · Clément Vignac · Max Welling -
2021 Workshop: Over-parameterization: Pitfalls and Opportunities »
Yasaman Bahri · Quanquan Gu · Amin Karbasi · Hanie Sedghi -
2021 Workshop: ICML Workshop on Representation Learning for Finance and E-Commerce Applications »
Senthil Kumar · Sameena Shah · Joan Bruna · Tom Goldstein · Erik Mueller · Oleg Rokhlenko · Hongxia Yang · Jianpeng Xu · Oluwatobi O Olabiyi · Charese Smiley · C. Bayan Bruss · Saurabh H Nagrecha · Svitlana Vyetrenko -
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 Poster: Understanding self-supervised learning dynamics without contrastive pairs »
Yuandong Tian · Xinlei Chen · Surya Ganguli -
2021 Poster: A theory of high dimensional regression with arbitrary correlations between input features and target functions: sample complexity, multiple descent curves and a hierarchy of phase transitions »
Gabriel Mel · Surya Ganguli -
2021 Spotlight: A theory of high dimensional regression with arbitrary correlations between input features and target functions: sample complexity, multiple descent curves and a hierarchy of phase transitions »
Gabriel Mel · Surya Ganguli -
2021 Oral: Understanding self-supervised learning dynamics without contrastive pairs »
Yuandong Tian · Xinlei Chen · Surya Ganguli -
2021 Poster: On Energy-Based Models with Overparametrized Shallow Neural Networks »
Carles Domingo-Enrich · Alberto Bietti · Eric Vanden-Eijnden · Joan Bruna -
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 Oral: On Energy-Based Models with Overparametrized Shallow Neural Networks »
Carles Domingo-Enrich · Alberto Bietti · Eric Vanden-Eijnden · Joan Bruna -
2021 Poster: A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups »
Marc Finzi · Max Welling · Andrew Wilson -
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: E(n) Equivariant Graph Neural Networks »
Victor Garcia Satorras · Emiel Hoogeboom · Max Welling -
2021 Poster: Offline Contextual Bandits with Overparameterized Models »
David Brandfonbrener · William Whitney · Rajesh Ranganath · Joan Bruna -
2021 Poster: A Functional Perspective on Learning Symmetric Functions with Neural Networks »
Aaron Zweig · Joan Bruna -
2021 Poster: Self Normalizing Flows »
T. Anderson Keller · Jorn Peters · Priyank Jaini · Emiel Hoogeboom · Patrick Forré · Max Welling -
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: Self Normalizing Flows »
T. Anderson Keller · Jorn Peters · Priyank Jaini · Emiel Hoogeboom · Patrick Forré · Max Welling -
2021 Spotlight: A Functional Perspective on Learning Symmetric Functions with Neural Networks »
Aaron Zweig · Joan Bruna -
2021 Spotlight: Offline Contextual Bandits with Overparameterized Models »
David Brandfonbrener · William Whitney · Rajesh Ranganath · Joan Bruna -
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 : Invited talk 1: Unifying VAEs and Flows »
Max Welling -
2020 Poster: Extra-gradient with player sampling for faster convergence in n-player games »
Samy Jelassi · Carles Domingo-Enrich · Damien Scieur · Arthur Mensch · Joan Bruna -
2020 Poster: Involutive MCMC: a Unifying Framework »
Kirill Neklyudov · Max Welling · Evgenii Egorov · Dmitry Vetrov -
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: Disentangling Trainability and Generalization in Deep Neural Networks »
Lechao Xiao · Jeffrey Pennington · Samuel Schoenholz -
2020 Poster: Two Routes to Scalable Credit Assignment without Weight Symmetry »
Daniel Kunin · Aran Nayebi · Javier Sagastuy-Brena · Surya Ganguli · Jonathan Bloom · Daniel Yamins -
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 : 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 -
2019 : Panel Discussion (moderator: Tom Dietterich) »
Max Welling · Kilian Weinberger · Terrance Boult · Dawn Song · Thomas Dietterich -
2019 : Keynote by Max Welling: A Nonparametric Bayesian Approach to Deep Learning (without GPs) »
Max Welling -
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 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: Neuron birth-death dynamics accelerates gradient descent and converges asymptotically »
Grant Rotskoff · Samy Jelassi · Joan Bruna · Eric Vanden-Eijnden -
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: Neuron birth-death dynamics accelerates gradient descent and converges asymptotically »
Grant Rotskoff · Samy Jelassi · Joan Bruna · Eric Vanden-Eijnden -
2019 Poster: Approximating Orthogonal Matrices with Effective Givens Factorization »
Thomas Frerix · Joan Bruna -
2019 Poster: Emerging Convolutions for Generative Normalizing Flows »
Emiel Hoogeboom · Rianne Van den Berg · Max Welling -
2019 Oral: Approximating Orthogonal Matrices with Effective Givens Factorization »
Thomas Frerix · Joan Bruna -
2019 Oral: Emerging Convolutions for Generative Normalizing Flows »
Emiel Hoogeboom · Rianne Van den Berg · Max Welling -
2019 Poster: Gauge Equivariant Convolutional Networks and the Icosahedral CNN »
Taco Cohen · Maurice Weiler · Berkay Kicanaoglu · Max Welling -
2019 Oral: Gauge Equivariant Convolutional Networks and the Icosahedral CNN »
Taco Cohen · Maurice Weiler · Berkay Kicanaoglu · Max Welling -
2018 Poster: Attention-based Deep Multiple Instance Learning »
Maximilian Ilse · Jakub Tomczak · Max Welling -
2018 Oral: Attention-based Deep Multiple Instance Learning »
Maximilian Ilse · Jakub Tomczak · Max Welling -
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 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: BOCK : Bayesian Optimization with Cylindrical Kernels »
ChangYong Oh · Efstratios Gavves · Max Welling -
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 Oral: Neural Relational Inference for Interacting Systems »
Thomas Kipf · Ethan Fetaya · Kuan-Chieh Wang · Max Welling · Richard Zemel -
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: BOCK : Bayesian Optimization with Cylindrical Kernels »
ChangYong Oh · Efstratios Gavves · Max Welling -
2017 Poster: Multiplicative Normalizing Flows for Variational Bayesian Neural Networks »
Christos Louizos · Max Welling -
2017 Talk: Multiplicative Normalizing Flows for Variational Bayesian Neural Networks »
Christos Louizos · Max Welling -
2017 Poster: Continual Learning Through Synaptic Intelligence »
Friedemann Zenke · Ben Poole · Surya Ganguli -
2017 Poster: Geometry of Neural Network Loss Surfaces via Random Matrix Theory »
Jeffrey Pennington · Yasaman Bahri -
2017 Talk: Continual Learning Through Synaptic Intelligence »
Friedemann Zenke · Ben Poole · Surya Ganguli -
2017 Poster: On the Expressive Power of Deep Neural Networks »
Maithra Raghu · Ben Poole · Surya Ganguli · Jon Kleinberg · Jascha Sohl-Dickstein -
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