Deep Neural Networks as Gaussian Processes

1 November 2017

Jascha Narain Sohl-Dickstein

UQCV

BDL

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Papers citing "Deep Neural Networks as Gaussian Processes"

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Title
Learning curves for Gaussian process regression with power-law priors and targets Hui Jin P. Banerjee Guido Montúfar 16 17 0 23 Oct 2021
Using scientific machine learning for experimental bifurcation analysis of dynamic systems S. Beregi David A.W. Barton D. Rezgui S. Neild AI4CE 50 19 0 22 Oct 2021
Feature Learning and Signal Propagation in Deep Neural Networks Yizhang Lou Chris Mingard Yoonsoo Nam Soufiane Hayou MDE 29 17 0 22 Oct 2021
Self-supervised denoising for massive noisy images Feng Wang Trond R. Henninen D. Keller R. Erni 13 0 0 18 Oct 2021
Centroid Approximation for Bootstrap: Improving Particle Quality at Inference Mao Ye Qiang Liu 27 1 0 17 Oct 2021
Training Neural Networks for Solving 1-D Optimal Piecewise Linear Approximation Hangcheng Dong Jing-Xiao Liao Yan Wang Yixin Chen Bingguo Liu Dong Ye Guodong Liu 152 0 0 14 Oct 2021
Implicit Bias of Linear Equivariant Networks Hannah Lawrence Kristian Georgiev A. Dienes B. Kiani AI4CE 45 14 0 12 Oct 2021
On out-of-distribution detection with Bayesian neural networks Francesco DÁngelo Christian Henning BDL UQCV 29 6 0 12 Oct 2021
Imitating Deep Learning Dynamics via Locally Elastic Stochastic Differential Equations Jiayao Zhang Hua Wang Weijie J. Su 35 8 0 11 Oct 2021
Kernel Interpolation as a Bayes Point Machine Jeremy Bernstein Alexander R. Farhang Yisong Yue BDL 34 4 0 08 Oct 2021
New Insights into Graph Convolutional Networks using Neural Tangent Kernels Mahalakshmi Sabanayagam P. Esser D. Ghoshdastidar 29 6 0 08 Oct 2021
The Eigenlearning Framework: A Conservation Law Perspective on Kernel Regression and Wide Neural Networks James B. Simon Madeline Dickens Dhruva Karkada M. DeWeese 52 27 0 08 Oct 2021
Bayesian neural network unit priors and generalized Weibull-tail property M. Vladimirova Julyan Arbel Stéphane Girard BDL 54 9 0 06 Oct 2021
On the Impact of Stable Ranks in Deep Nets B. Georgiev L. Franken Mayukh Mukherjee Georgios Arvanitidis 23 3 0 05 Oct 2021
On the Correspondence between Gaussian Processes and Geometric Harmonics Felix Dietrich J. M. Bello-Rivas Ioannis G. Kevrekidis 34 3 0 05 Oct 2021
Random matrices in service of ML footprint: ternary random features with no performance loss Hafiz Tiomoko Ali Zhenyu Liao Romain Couillet 49 7 0 05 Oct 2021
Learning through atypical "phase transitions" in overparameterized neural networks Carlo Baldassi Clarissa Lauditi Enrico M. Malatesta R. Pacelli Gabriele Perugini R. Zecchina 39 26 0 01 Oct 2021
The edge of chaos: quantum field theory and deep neural networks Kevin T. Grosvenor R. Jefferson 43 22 0 27 Sep 2021
Understanding neural networks with reproducing kernel Banach spaces Francesca Bartolucci Ernesto De Vito Lorenzo Rosasco Stefano Vigogna 52 50 0 20 Sep 2021
Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes Jongseo Lee Jianxiang Feng Matthias Humt M. Müller Rudolph Triebel UQCV 53 21 0 20 Sep 2021
Deformed semicircle law and concentration of nonlinear random matrices for ultra-wide neural networks Zhichao Wang Yizhe Zhu 40 18 0 20 Sep 2021
Uniform Generalization Bounds for Overparameterized Neural Networks Sattar Vakili Michael Bromberg Jezabel R. Garcia Da-Shan Shiu A. Bernacchia 35 19 0 13 Sep 2021
Large-Scale Learning with Fourier Features and Tensor Decompositions Frederiek Wesel Kim Batselier 25 11 0 03 Sep 2021
A theory of representation learning gives a deep generalisation of kernel methods Adam X. Yang Maxime Robeyns Edward Milsom Ben Anson Nandi Schoots Laurence Aitchison BDL 37 10 0 30 Aug 2021
Neural Network Gaussian Processes by Increasing Depth Shao-Qun Zhang Fei Wang Feng-lei Fan 11 7 0 29 Aug 2021
Shift-Curvature, SGD, and Generalization Arwen V. Bradley C. Gomez-Uribe Manish Reddy Vuyyuru 35 2 0 21 Aug 2021
Nonperturbative renormalization for the neural network-QFT correspondence Harold Erbin Vincent Lahoche D. O. Samary 46 30 0 03 Aug 2021
Deep Stable neural networks: large-width asymptotics and convergence rates Stefano Favaro S. Fortini Stefano Peluchetti BDL 35 14 0 02 Aug 2021
Dataset Distillation with Infinitely Wide Convolutional Networks Timothy Nguyen Roman Novak Lechao Xiao Jaehoon Lee DD 51 231 0 27 Jul 2021
Are Bayesian neural networks intrinsically good at out-of-distribution detection? Christian Henning Francesco DÁngelo Benjamin Grewe UQCV BDL 31 10 0 26 Jul 2021
A brief note on understanding neural networks as Gaussian processes Mengwu Guo BDL GP 22 2 0 25 Jul 2021
A variational approximate posterior for the deep Wishart process Sebastian W. Ober Laurence Aitchison BDL 27 11 0 21 Jul 2021
The Limiting Dynamics of SGD: Modified Loss, Phase Space Oscillations, and Anomalous Diffusion D. Kunin Javier Sagastuy-Breña Lauren Gillespie Eshed Margalit Hidenori Tanaka Surya Ganguli Daniel L. K. Yamins 36 16 0 19 Jul 2021
Epistemic Neural Networks Ian Osband Zheng Wen M. Asghari Vikranth Dwaracherla M. Ibrahimi Xiyuan Lu Benjamin Van Roy UQCV BDL 32 99 0 19 Jul 2021
Understanding the Distributions of Aggregation Layers in Deep Neural Networks Eng-Jon Ong S. Husain M. Bober FAtt FedML AI4CE 11 2 0 09 Jul 2021
Logit-based Uncertainty Measure in Classification Huiyue Wu Diego Klabjan EDL BDL UQCV 20 6 0 06 Jul 2021
Random Neural Networks in the Infinite Width Limit as Gaussian Processes Boris Hanin BDL 37 44 0 04 Jul 2021
Scale Mixtures of Neural Network Gaussian Processes Hyungi Lee Eunggu Yun Hongseok Yang Juho Lee UQCV BDL 21 7 0 03 Jul 2021
Subspace Clustering Based Analysis of Neural Networks Uday Singh Saini Pravallika Devineni Evangelos E. Papalexakis GNN 22 1 0 02 Jul 2021
Implicit Acceleration and Feature Learning in Infinitely Wide Neural Networks with Bottlenecks Etai Littwin Omid Saremi Shuangfei Zhai Vimal Thilak Hanlin Goh J. Susskind Greg Yang 33 3 0 01 Jul 2021
Saddle-to-Saddle Dynamics in Deep Linear Networks: Small Initialization Training, Symmetry, and Sparsity Arthur Jacot François Ged Berfin cSimcsek Clément Hongler Franck Gabriel 35 52 0 30 Jun 2021
Repulsive Deep Ensembles are Bayesian Francesco DÁngelo Vincent Fortuin UQCV BDL 64 95 0 22 Jun 2021
Deep Gaussian Processes: A Survey Kalvik Jakkala AI4CE GP BDL 29 19 0 21 Jun 2021
Scalable Safety-Critical Policy Evaluation with Accelerated Rare Event Sampling Mengdi Xu Peide Huang Fengpei Li Jiacheng Zhu Xuewei Qi K. Oguchi Zhiyuan Huang Henry Lam Ding Zhao 16 4 0 19 Jun 2021
$α$ -Stable convergence of heavy-tailed infinitely-wide neural networks Paul Jung Hoileong Lee Jiho Lee Hongseok Yang 21 5 0 18 Jun 2021
Wide stochastic networks: Gaussian limit and PAC-Bayesian training Eugenio Clerico George Deligiannidis Arnaud Doucet 25 12 0 17 Jun 2021
Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation Haoxiang Wang Han Zhao Bo Li 37 88 0 16 Jun 2021
Locality defeats the curse of dimensionality in convolutional teacher-student scenarios Alessandro Favero Francesco Cagnetta Matthieu Wyart 35 31 0 16 Jun 2021
How to Train Your Wide Neural Network Without Backprop: An Input-Weight Alignment Perspective Akhilan Boopathy Ila Fiete 44 9 0 15 Jun 2021
Scaling Neural Tangent Kernels via Sketching and Random Features A. Zandieh Insu Han H. Avron N. Shoham Chaewon Kim Jinwoo Shin 16 31 0 15 Jun 2021