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)