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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"

46 / 696 papers shown

Title
The Benefits of Over-parameterization at Initialization in Deep ReLU Networks Devansh Arpit Yoshua Bengio 76 22 0 11 Jan 2019
On the effect of the activation function on the distribution of hidden nodes in a deep network Philip M. Long Hanie Sedghi 88 5 0 07 Jan 2019
Scaling description of generalization with number of parameters in deep learning Mario Geiger Arthur Jacot S. Spigler Franck Gabriel Levent Sagun Stéphane dÁscoli Giulio Biroli Clément Hongler Matthieu Wyart 127 196 0 06 Jan 2019
On Lazy Training in Differentiable Programming Lénaïc Chizat Edouard Oyallon Francis R. Bach 255 840 0 19 Dec 2018
Efficient Model-Free Reinforcement Learning Using Gaussian Process Ying Fan Letian Chen Yizhou Wang GP 67 6 0 11 Dec 2018
The Limitations of Model Uncertainty in Adversarial Settings Kathrin Grosse David Pfaff M. Smith Michael Backes AAML 70 34 0 06 Dec 2018
Gradient Descent Finds Global Minima of Deep Neural Networks S. Du Jason D. Lee Haochuan Li Liwei Wang Masayoshi Tomizuka ODL 412 1,137 0 09 Nov 2018
Characterizing Well-Behaved vs. Pathological Deep Neural Networks Mitchell Stern 64 0 0 07 Nov 2018
A Batched Scalable Multi-Objective Bayesian Optimization Algorithm Xi Lin Hui-Ling Zhen Zhenhua Li Qingfu Zhang Sam Kwong 56 11 0 04 Nov 2018
On the Convergence Rate of Training Recurrent Neural Networks Zeyuan Allen-Zhu Yuanzhi Li Zhao Song 268 193 0 29 Oct 2018
A Gaussian Process perspective on Convolutional Neural Networks Anastasia Borovykh 88 19 0 25 Oct 2018
A Modern Take on the Bias-Variance Tradeoff in Neural Networks Brady Neal Sarthak Mittal A. Baratin Vinayak Tantia Matthew Scicluna Simon Lacoste-Julien Ioannis Mitliagkas 100 168 0 19 Oct 2018
Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel Colin Wei Jason D. Lee Qiang Liu Tengyu Ma 291 245 0 12 Oct 2018
Understanding Priors in Bayesian Neural Networks at the Unit Level M. Vladimirova Jakob Verbeek Pablo Mesejo Julyan Arbel BDL UQCV 97 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 165 310 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 107 16 0 09 Oct 2018
Information Geometry of Orthogonal Initializations and Training Piotr A. Sokól Il-Su Park AI4CE 136 17 0 09 Oct 2018
Deep convolutional Gaussian processes Kenneth Blomqvist Samuel Kaski Markus Heinonen BDL 93 61 0 06 Oct 2018
Generalization Properties of hyper-RKHS and its Applications Fanghui Liu Lei Shi Xiaolin Huang Jie Yang Johan A. K. Suykens 70 4 0 26 Sep 2018
Robustness Guarantees for Bayesian Inference with Gaussian Processes L. Cardelli Marta Kwiatkowska Luca Laurenti A. Patané AAML 79 52 0 17 Sep 2018
Deep Convolutional Networks as shallow Gaussian Processes Adrià Garriga-Alonso C. Rasmussen Laurence Aitchison BDL UQCV 137 271 0 16 Aug 2018
Noise Contrastive Priors for Functional Uncertainty Danijar Hafner Dustin Tran Timothy Lillicrap A. Irpan James Davidson AAML BDL UQCV 152 74 0 24 Jul 2018
EnsembleDAgger: A Bayesian Approach to Safe Imitation Learning Kunal Menda Katherine Driggs-Campbell Mykel J. Kochenderfer 197 119 0 22 Jul 2018
Phase Retrieval Under a Generative Prior Paul Hand Oscar Leong V. Voroninski 106 189 0 11 Jul 2018
Bayesian Deep Learning on a Quantum Computer Zhikuan Zhao Alejandro Pozas-Kerstjens Patrick Rebentrost P. Wittek UQCV BDL 92 72 0 29 Jun 2018
Adversarial Reprogramming of Neural Networks Gamaleldin F. Elsayed Ian Goodfellow Jascha Narain Sohl-Dickstein OOD AAML 57 184 0 28 Jun 2018
Neural-net-induced Gaussian process regression for function approximation and PDE solution G. Pang Liu Yang George Karniadakis 85 73 0 22 Jun 2018
Neural Tangent Kernel: Convergence and Generalization in Neural Networks Arthur Jacot Franck Gabriel Clément Hongler 416 3,226 0 20 Jun 2018
Insights on representational similarity in neural networks with canonical correlation Ari S. Morcos M. Raghu Samy Bengio DRL 155 447 0 14 Jun 2018
Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks Minmin Chen Jeffrey Pennington S. Schoenholz SyDa AI4CE 89 117 0 14 Jun 2018
Variational Implicit Processes Chao Ma Yingzhen Li José Miguel Hernández-Lobato BDL 130 70 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 69 2 0 06 Jun 2018
Universal Statistics of Fisher Information in Deep Neural Networks: Mean Field Approach Ryo Karakida S. Akaho S. Amari FedML 238 146 0 04 Jun 2018
Interpreting Deep Learning: The Machine Learning Rorschach Test? Adam S. Charles AAML HAI AI4CE 105 9 0 01 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 151 232 0 22 May 2018
On the Selection of Initialization and Activation Function for Deep Neural Networks Soufiane Hayou Arnaud Doucet Judith Rousseau ODL 67 76 0 21 May 2018
Real-time regression analysis with deep convolutional neural networks Eliu A. Huerta D. George Zhizhen Zhao Gabrielle Allen 90 4 0 07 May 2018
Gaussian Process Behaviour in Wide Deep Neural Networks A. G. Matthews Mark Rowland Jiri Hron Richard Turner Zoubin Ghahramani BDL 241 561 0 30 Apr 2018
Gradient descent in Gaussian random fields as a toy model for high-dimensional optimisation in deep learning Mariano Chouza Stephen J. Roberts S. Zohren 38 2 0 24 Mar 2018
Sensitivity and Generalization in Neural Networks: an Empirical Study Roman Novak Yasaman Bahri Daniel A. Abolafia Jeffrey Pennington Jascha Narain Sohl-Dickstein AAML 149 443 0 23 Feb 2018
Algorithmic Linearly Constrained Gaussian Processes Markus Lange-Hegermann 87 35 0 28 Jan 2018
Invariance of Weight Distributions in Rectified MLPs Russell Tsuchida Farbod Roosta-Khorasani M. Gallagher MLT 131 36 0 24 Nov 2017
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 82 9 0 17 Nov 2017
Spatial Mapping with Gaussian Processes and Nonstationary Fourier Features Jean-François Ton Seth Flaxman Dino Sejdinovic Samir Bhatt GP 80 55 0 15 Nov 2017
Deep Learning: A Bayesian Perspective Nicholas G. Polson Vadim Sokolov BDL 142 117 0 01 Jun 2017
Quantifying the probable approximation error of probabilistic inference programs Marco F. Cusumano-Towner Vikash K. Mansinghka 102 7 0 31 May 2016