v1v2v3 (latest)

Deep Neural Networks as Gaussian Processes

1 November 2017

Jascha Narain Sohl-Dickstein

UQCV

BDL

ArXiv (abs)PDF HTML

Papers citing "Deep Neural Networks as Gaussian Processes"

50 / 696 papers shown

Title
Wide stochastic networks: Gaussian limit and PAC-Bayesian training Eugenio Clerico George Deligiannidis Arnaud Doucet 113 12 0 17 Jun 2021
Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation Haoxiang Wang Han Zhao Yue Liu 112 90 0 16 Jun 2021
Locality defeats the curse of dimensionality in convolutional teacher-student scenarios Alessandro Favero Francesco Cagnetta Matthieu Wyart 108 31 0 16 Jun 2021
How to Train Your Wide Neural Network Without Backprop: An Input-Weight Alignment Perspective Akhilan Boopathy Ila Fiete 108 10 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 84 32 0 15 Jun 2021
Precise characterization of the prior predictive distribution of deep ReLU networks Lorenzo Noci Gregor Bachmann Kevin Roth Sebastian Nowozin Thomas Hofmann BDL UQCV 97 33 0 11 Jun 2021
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective Geoff Pleiss John P. Cunningham 79 27 0 11 Jun 2021
Measuring the robustness of Gaussian processes to kernel choice William T. Stephenson S. Ghosh Tin D. Nguyen Mikhail Yurochkin Sameer K. Deshpande Tamara Broderick GP 48 11 0 11 Jun 2021
Learning effective stochastic differential equations from microscopic simulations: linking stochastic numerics to deep learning Felix Dietrich Alexei Makeev George A. Kevrekidis N. Evangelou Tom S. Bertalan Sebastian Reich Ioannis G. Kevrekidis DiffM 98 38 0 10 Jun 2021
A Neural Tangent Kernel Perspective of GANs Jean-Yves Franceschi Emmanuel de Bézenac Ibrahim Ayed Mickaël Chen Sylvain Lamprier Patrick Gallinari 131 27 0 10 Jun 2021
Ghosts in Neural Networks: Existence, Structure and Role of Infinite-Dimensional Null Space Sho Sonoda Isao Ishikawa Masahiro Ikeda BDL 68 9 0 09 Jun 2021
A self consistent theory of Gaussian Processes captures feature learning effects in finite CNNs Gadi Naveh Zohar Ringel SSL MLT 99 33 0 08 Jun 2021
Learning Functional Priors and Posteriors from Data and Physics Xuhui Meng Liu Yang Zhiping Mao J. Ferrandis George Karniadakis AI4CE 180 61 0 08 Jun 2021
The Future is Log-Gaussian: ResNets and Their Infinite-Depth-and-Width Limit at Initialization Mufan Li Mihai Nica Daniel M. Roy 122 34 0 07 Jun 2021
Batch Normalization Orthogonalizes Representations in Deep Random Networks Hadi Daneshmand Amir Joudaki Francis R. Bach OOD 73 37 0 07 Jun 2021
Reverse Engineering the Neural Tangent Kernel James B. Simon Sajant Anand M. DeWeese 106 9 0 06 Jun 2021
Out-of-Distribution Generalization in Kernel Regression Abdulkadir Canatar Blake Bordelon Cengiz Pehlevan OODD OOD 90 13 0 04 Jun 2021
Symmetry-via-Duality: Invariant Neural Network Densities from Parameter-Space Correlators Anindita Maiti Keegan Stoner James Halverson 82 20 0 01 Jun 2021
Asymptotics of representation learning in finite Bayesian neural networks Jacob A. Zavatone-Veth Abdulkadir Canatar Benjamin S. Ruben Cengiz Pehlevan 117 29 0 01 Jun 2021
Generalization Error Rates in Kernel Regression: The Crossover from the Noiseless to Noisy Regime Hugo Cui Bruno Loureiro Florent Krzakala Lenka Zdeborová 101 85 0 31 May 2021
Sparse Uncertainty Representation in Deep Learning with Inducing Weights H. Ritter Martin Kukla Chen Zhang Yingzhen Li UQCV BDL 94 17 0 30 May 2021
Active Learning in Bayesian Neural Networks with Balanced Entropy Learning Principle J. Woo 136 11 0 30 May 2021
Activation function design for deep networks: linearity and effective initialisation Michael Murray V. Abrol Jared Tanner ODL LLMSV 66 18 0 17 May 2021
Priors in Bayesian Deep Learning: A Review Vincent Fortuin UQCV BDL 141 134 0 14 May 2021
Sparsity-Probe: Analysis tool for Deep Learning Models Ido Ben-Shaul S. Dekel 46 4 0 14 May 2021
Deep Neural Networks as Point Estimates for Deep Gaussian Processes Vincent Dutordoir J. Hensman Mark van der Wilk Carl Henrik Ek Zoubin Ghahramani N. Durrande BDL UQCV 124 31 0 10 May 2021
Tensor Programs IIb: Architectural Universality of Neural Tangent Kernel Training Dynamics Greg Yang Etai Littwin 85 67 0 08 May 2021
Adaptive Latent Space Tuning for Non-Stationary Distributions A. Scheinker F. Cropp S. Paiagua D. Filippetto OOD 121 3 0 08 May 2021
Uniform Convergence, Adversarial Spheres and a Simple Remedy Gregor Bachmann Seyed-Mohsen Moosavi-Dezfooli Thomas Hofmann AAML 47 8 0 07 May 2021
Uncertainty-Aware Boosted Ensembling in Multi-Modal Settings U. Sarawgi Rishab Khincha W. Zulfikar Satrajit S. Ghosh Pattie Maes UQCV 60 7 0 21 Apr 2021
How rotational invariance of common kernels prevents generalization in high dimensions Konstantin Donhauser Mingqi Wu Fanny Yang 92 24 0 09 Apr 2021
Artificial Neural Network Modeling for Airline Disruption Management Kolawole E. Ogunsina Wendy A. Okolo 24 5 0 05 Apr 2021
Learning with Neural Tangent Kernels in Near Input Sparsity Time A. Zandieh 78 0 0 01 Apr 2021
Initializing ReLU networks in an expressive subspace of weights Dayal Singh J. SreejithG 47 4 0 23 Mar 2021
Weighted Neural Tangent Kernel: A Generalized and Improved Network-Induced Kernel Lei Tan Shutong Wu Xiaolin Huang 42 2 0 22 Mar 2021
Data-driven Aerodynamic Analysis of Structures using Gaussian Processes I. Kavrakov A. McRobie Guido Morgenthal 48 13 0 20 Mar 2021
Why flatness does and does not correlate with generalization for deep neural networks Shuo Zhang Isaac Reid Guillermo Valle Pérez A. Louis 79 8 0 10 Mar 2021
Towards Deepening Graph Neural Networks: A GNTK-based Optimization Perspective Wei Huang Yayong Li Weitao Du Jie Yin R. Xu Ling-Hao Chen Miao Zhang 87 17 0 03 Mar 2021
Computing the Information Content of Trained Neural Networks Jeremy Bernstein Yisong Yue 75 4 0 01 Mar 2021
Multi-fidelity regression using artificial neural networks: efficient approximation of parameter-dependent output quantities Mengwu Guo Andrea Manzoni Maurice Amendt Paolo Conti J. Hesthaven 184 98 0 26 Feb 2021
Neural Generalization of Multiple Kernel Learning Ahamad Navid Ghanizadeh Kamaledin Ghiasi-Shirazi R. Monsefi Mohammadreza Qaraei 39 2 0 26 Feb 2021
Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeed Maria Refinetti Sebastian Goldt Florent Krzakala Lenka Zdeborová 103 74 0 23 Feb 2021
Large-width functional asymptotics for deep Gaussian neural networks Daniele Bracale Stefano Favaro S. Fortini Stefano Peluchetti 70 17 0 20 Feb 2021
Approximation and Learning with Deep Convolutional Models: a Kernel Perspective A. Bietti 98 30 0 19 Feb 2021
WGAN with an Infinitely Wide Generator Has No Spurious Stationary Points Albert No Taeho Yoon Sehyun Kwon Ernest K. Ryu GAN 58 2 0 15 Feb 2021
Cross-modal Adversarial Reprogramming Paarth Neekhara Shehzeen Samarah Hussain Jinglong Du Shlomo Dubnov F. Koushanfar Julian McAuley 125 36 0 15 Feb 2021
Double-descent curves in neural networks: a new perspective using Gaussian processes Ouns El Harzli Bernardo Cuenca Grau Guillermo Valle Pérez A. Louis 108 7 0 14 Feb 2021
Explaining Neural Scaling Laws Yasaman Bahri Ethan Dyer Jared Kaplan Jaehoon Lee Utkarsh Sharma 126 272 0 12 Feb 2021
Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction Dominik Narnhofer Alexander Effland Erich Kobler Kerstin Hammernik Florian Knoll Thomas Pock UQCV BDL 77 51 0 12 Feb 2021
Bayesian Neural Network Priors Revisited Vincent Fortuin Adrià Garriga-Alonso Sebastian W. Ober F. Wenzel Gunnar Rätsch Richard Turner Mark van der Wilk Laurence Aitchison BDL UQCV 139 141 0 12 Feb 2021