Gaussian Process Behaviour in Wide Deep Neural Networks

30 April 2018

Papers citing "Gaussian Process Behaviour in Wide Deep Neural Networks"

50 / 391 papers shown

Title
Scale Mixtures of Neural Network Gaussian Processes Hyungi Lee Eunggu Yun Hongseok Yang Juho Lee UQCV BDL 13 7 0 03 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
$α$ -Stable convergence of heavy-tailed infinitely-wide neural networks Paul Jung Hoileong Lee Jiho Lee Hongseok Yang 16 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
Locality defeats the curse of dimensionality in convolutional teacher-student scenarios Alessandro Favero Francesco Cagnetta M. Wyart 30 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 11 31 0 15 Jun 2021
Wide Mean-Field Variational Bayesian Neural Networks Ignore the Data Beau Coker Weiwei Pan Finale Doshi-Velez BDL 19 9 0 13 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 29 32 0 11 Jun 2021
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective Geoff Pleiss John P. Cunningham 28 24 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 14 11 0 11 Jun 2021
A self consistent theory of Gaussian Processes captures feature learning effects in finite CNNs Gadi Naveh Zohar Ringel SSL MLT 36 31 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 35 33 0 07 Jun 2021
Batch Normalization Orthogonalizes Representations in Deep Random Networks Hadi Daneshmand Amir Joudaki Francis R. Bach OOD 13 37 0 07 Jun 2021
Reverse Engineering the Neural Tangent Kernel James B. Simon Sajant Anand M. DeWeese 35 9 0 06 Jun 2021
Regularization in ResNet with Stochastic Depth Soufiane Hayou Fadhel Ayed 19 10 0 06 Jun 2021
Symmetry-via-Duality: Invariant Neural Network Densities from Parameter-Space Correlators Anindita Maiti Keegan Stoner James Halverson 23 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 21 31 0 01 Jun 2021
Sparse Uncertainty Representation in Deep Learning with Inducing Weights H. Ritter Martin Kukla Chen Zhang Yingzhen Li UQCV BDL 60 17 0 30 May 2021
Activation function design for deep networks: linearity and effective initialisation Michael Murray V. Abrol Jared Tanner ODL LLMSV 29 18 0 17 May 2021
Posterior contraction for deep Gaussian process priors G. Finocchio Johannes Schmidt-Hieber 35 11 0 16 May 2021
Priors in Bayesian Deep Learning: A Review Vincent Fortuin UQCV BDL 33 124 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 18 30 0 10 May 2021
Tensor Programs IIb: Architectural Universality of Neural Tangent Kernel Training Dynamics Greg Yang Etai Littwin 22 63 0 08 May 2021
Analyzing Monotonic Linear Interpolation in Neural Network Loss Landscapes James Lucas Juhan Bae Michael Ruogu Zhang Stanislav Fort R. Zemel Roger C. Grosse MoMe 172 28 0 22 Apr 2021
Adversarial Robustness Guarantees for Gaussian Processes A. Patané Arno Blaas Luca Laurenti L. Cardelli Stephen J. Roberts Marta Z. Kwiatkowska GP AAML 98 9 0 07 Apr 2021
Learning with Neural Tangent Kernels in Near Input Sparsity Time A. Zandieh 9 0 0 01 Apr 2021
Weighted Neural Tangent Kernel: A Generalized and Improved Network-Induced Kernel Lei Tan Shutong Wu Xiaolin Huang 26 1 0 22 Mar 2021
Why flatness does and does not correlate with generalization for deep neural networks Shuo Zhang Isaac Reid Guillermo Valle Pérez A. Louis 19 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 26 17 0 03 Mar 2021
Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeed Maria Refinetti Sebastian Goldt Florent Krzakala Lenka Zdeborová 22 72 0 23 Feb 2021
Large-width functional asymptotics for deep Gaussian neural networks Daniele Bracale Stefano Favaro S. Fortini Stefano Peluchetti 20 18 0 20 Feb 2021
Quantum field-theoretic machine learning Dimitrios Bachtis Gert Aarts B. Lucini AI4CE 19 28 0 18 Feb 2021
Non-asymptotic approximations of neural networks by Gaussian processes Ronen Eldan Dan Mikulincer T. Schramm 41 24 0 17 Feb 2021
Cross-modal Adversarial Reprogramming Paarth Neekhara Shehzeen Samarah Hussain Jinglong Du Shlomo Dubnov F. Koushanfar Julian McAuley 13 35 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 22 6 0 14 Feb 2021
Explaining Neural Scaling Laws Yasaman Bahri Ethan Dyer Jared Kaplan Jaehoon Lee Utkarsh Sharma 27 250 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 67 138 0 12 Feb 2021
Reducing the Amortization Gap in Variational Autoencoders: A Bayesian Random Function Approach Minyoung Kim Vladimir Pavlovic BDL 31 6 0 05 Feb 2021
Faster Kernel Interpolation for Gaussian Processes Mohit Yadav Daniel Sheldon Cameron Musco BDL 13 10 0 28 Jan 2021
Implicit Bias of Linear RNNs M Motavali Emami Mojtaba Sahraee-Ardakan Parthe Pandit S. Rangan A. Fletcher 20 11 0 19 Jan 2021
Correlated Weights in Infinite Limits of Deep Convolutional Neural Networks Adrià Garriga-Alonso Mark van der Wilk 14 4 0 11 Jan 2021
Infinitely Wide Tensor Networks as Gaussian Process Erdong Guo D. Draper 19 2 0 07 Jan 2021
Perspective: A Phase Diagram for Deep Learning unifying Jamming, Feature Learning and Lazy Training Mario Geiger Leonardo Petrini M. Wyart DRL 31 11 0 30 Dec 2020
Trace-class Gaussian priors for Bayesian learning of neural networks with MCMC Torben Sell Sumeetpal S. Singh BDL 23 5 0 20 Dec 2020
Analyzing Finite Neural Networks: Can We Trust Neural Tangent Kernel Theory? Mariia Seleznova Gitta Kutyniok AAML 24 29 0 08 Dec 2020
Generalization bounds for deep learning Guillermo Valle Pérez A. Louis BDL 13 44 0 07 Dec 2020
All You Need is a Good Functional Prior for Bayesian Deep Learning Ba-Hien Tran Simone Rossi Dimitrios Milios Maurizio Filippone OOD BDL 31 56 0 25 Nov 2020
Neural Network Gaussian Process Considering Input Uncertainty for Composite Structures Assembly Cheolhei Lee Jianguo Wu Wei Cao Xiaowei Yue 6 19 0 21 Nov 2020