Gaussian Process Behaviour in Wide Deep Neural Networks

30 April 2018

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

41 / 391 papers shown

Title
Linear Multiple Low-Rank Kernel Based Stationary Gaussian Processes Regression for Time Series Feng Yin Lishuo Pan Xinwei He Tianshi Chen Sergios Theodoridis Zhi-Quan Zhi-Quan Luo AI4TS 11 25 0 21 Apr 2019
A Bayesian Perspective on the Deep Image Prior Zezhou Cheng Matheus Gadelha Subhransu Maji Daniel Sheldon BDL UQCV 6 134 0 16 Apr 2019
Implicit Regularization in Over-parameterized Neural Networks M. Kubo Ryotaro Banno Hidetaka Manabe Masataka Minoji 25 23 0 05 Mar 2019
Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learning Evgenii Tsymbalov Sergei Makarychev Alexander Shapeev Maxim Panov BDL UQCV 6 20 0 27 Feb 2019
Function Space Particle Optimization for Bayesian Neural Networks Ziyu Wang Tongzheng Ren Jun Zhu Bo Zhang BDL 28 63 0 26 Feb 2019
A Mean Field Theory of Batch Normalization Greg Yang Jeffrey Pennington Vinay Rao Jascha Narain Sohl-Dickstein S. Schoenholz 19 178 0 21 Feb 2019
On the Impact of the Activation Function on Deep Neural Networks Training Soufiane Hayou Arnaud Doucet Judith Rousseau ODL 31 195 0 19 Feb 2019
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent Jaehoon Lee Lechao Xiao S. Schoenholz Yasaman Bahri Roman Novak Jascha Narain Sohl-Dickstein Jeffrey Pennington 57 1,077 0 18 Feb 2019
Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation Greg Yang 11 284 0 13 Feb 2019
Mean Field Limit of the Learning Dynamics of Multilayer Neural Networks Phan-Minh Nguyen AI4CE 24 72 0 07 Feb 2019
Dynamical Isometry and a Mean Field Theory of LSTMs and GRUs D. Gilboa B. Chang Minmin Chen Greg Yang S. Schoenholz Ed H. Chi Jeffrey Pennington 34 40 0 25 Jan 2019
On the effect of the activation function on the distribution of hidden nodes in a deep network Philip M. Long Hanie Sedghi 13 5 0 07 Jan 2019
On Lazy Training in Differentiable Programming Lénaïc Chizat Edouard Oyallon Francis R. Bach 52 807 0 19 Dec 2018
Efficient Model-Free Reinforcement Learning Using Gaussian Process Ying Fan Letian Chen Yizhou Wang GP 15 6 0 11 Dec 2018
The Limitations of Model Uncertainty in Adversarial Settings Kathrin Grosse David Pfaff M. Smith Michael Backes AAML 25 34 0 06 Dec 2018
Gradient Descent Finds Global Minima of Deep Neural Networks S. Du J. Lee Haochuan Li Liwei Wang Masayoshi Tomizuka ODL 44 1,125 0 09 Nov 2018
Characterizing Well-Behaved vs. Pathological Deep Neural Networks Mitchell Stern 21 0 0 07 Nov 2018
A Gaussian Process perspective on Convolutional Neural Networks Anastasia Borovykh 6 19 0 25 Oct 2018
Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel Colin Wei J. Lee Qiang Liu Tengyu Ma 26 245 0 12 Oct 2018
Understanding Priors in Bayesian Neural Networks at the Unit Level M. Vladimirova Jakob Verbeek Pablo Mesejo Julyan Arbel BDL UQCV 11 4 0 11 Oct 2018
Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes Roman Novak Lechao Xiao Jaehoon Lee Yasaman Bahri Greg Yang Jiri Hron Daniel A. Abolafia Jeffrey Pennington Jascha Narain Sohl-Dickstein UQCV BDL 25 307 0 11 Oct 2018
Deterministic Variational Inference for Robust Bayesian Neural Networks Anqi Wu Sebastian Nowozin Edward Meeds Richard Turner José Miguel Hernández-Lobato Alexander L. Gaunt UQCV AAML BDL 29 16 0 09 Oct 2018
Information Geometry of Orthogonal Initializations and Training Piotr A. Sokól Il-Su Park AI4CE 80 16 0 09 Oct 2018
Robustness Guarantees for Bayesian Inference with Gaussian Processes L. Cardelli Marta Kwiatkowska Luca Laurenti A. Patané AAML 14 52 0 17 Sep 2018
Deep Convolutional Networks as shallow Gaussian Processes Adrià Garriga-Alonso C. Rasmussen Laurence Aitchison BDL UQCV 13 269 0 16 Aug 2018
Noise Contrastive Priors for Functional Uncertainty Danijar Hafner Dustin Tran Timothy Lillicrap A. Irpan James Davidson AAML BDL UQCV 27 74 0 24 Jul 2018
Variational Bayesian dropout: pitfalls and fixes Jiri Hron A. G. Matthews Zoubin Ghahramani BDL 32 67 0 05 Jul 2018
When Gaussian Process Meets Big Data: A Review of Scalable GPs Haitao Liu Yew-Soon Ong Xiaobo Shen Jianfei Cai GP 24 681 0 03 Jul 2018
Bayesian Deep Learning on a Quantum Computer Zhikuan Zhao Alejandro Pozas-Kerstjens Patrick Rebentrost P. Wittek UQCV BDL 20 69 0 29 Jun 2018
Adversarial Reprogramming of Neural Networks Gamaleldin F. Elsayed Ian Goodfellow Jascha Narain Sohl-Dickstein OOD AAML 10 178 0 28 Jun 2018
Neural Tangent Kernel: Convergence and Generalization in Neural Networks Arthur Jacot Franck Gabriel Clément Hongler 60 3,117 0 20 Jun 2018
Insights on representational similarity in neural networks with canonical correlation Ari S. Morcos M. Raghu Samy Bengio DRL 32 432 0 14 Jun 2018
Variational Implicit Processes Chao Ma Yingzhen Li José Miguel Hernández-Lobato BDL 24 68 0 06 Jun 2018
Killing four birds with one Gaussian process: the relation between different test-time attacks Kathrin Grosse M. Smith Michael Backes AAML 18 2 0 06 Jun 2018
Sufficient Conditions for Idealised Models to Have No Adversarial Examples: a Theoretical and Empirical Study with Bayesian Neural Networks Y. Gal Lewis Smith AAML BDL 52 34 0 02 Jun 2018
Deep learning generalizes because the parameter-function map is biased towards simple functions Guillermo Valle Pérez Chico Q. Camargo A. Louis MLT AI4CE 18 226 0 22 May 2018
On the Selection of Initialization and Activation Function for Deep Neural Networks Soufiane Hayou Arnaud Doucet Judith Rousseau ODL 13 75 0 21 May 2018
The Gaussian Process Autoregressive Regression Model (GPAR) James Requeima Will Tebbutt W. Bruinsma Richard Turner 24 39 0 20 Feb 2018
How Wrong Am I? - Studying Adversarial Examples and their Impact on Uncertainty in Gaussian Process Machine Learning Models Kathrin Grosse David Pfaff M. Smith Michael Backes AAML 15 9 0 17 Nov 2017
Deep Neural Networks as Gaussian Processes Jaehoon Lee Yasaman Bahri Roman Novak S. Schoenholz Jeffrey Pennington Jascha Narain Sohl-Dickstein UQCV BDL 21 1,074 0 01 Nov 2017
Quantifying the probable approximation error of probabilistic inference programs Marco F. Cusumano-Towner Vikash K. Mansinghka 33 7 0 31 May 2016