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
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 Jason D. Lee Haochuan Li Liwei Wang Masayoshi Tomizuka ODL 44 1,126 0 09 Nov 2018
Characterizing Well-Behaved vs. Pathological Deep Neural Networks Mitchell Stern 21 0 0 07 Nov 2018
A Batched Scalable Multi-Objective Bayesian Optimization Algorithm Xi Lin Hui-Ling Zhen Zhenhua Li Qingfu Zhang Sam Kwong 22 11 0 04 Nov 2018
On the Convergence Rate of Training Recurrent Neural Networks Zeyuan Allen-Zhu Yuanzhi Li Zhao Song 34 191 0 29 Oct 2018
A Gaussian Process perspective on Convolutional Neural Networks Anastasia Borovykh 22 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 37 167 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 31 245 0 12 Oct 2018
Understanding Priors in Bayesian Neural Networks at the Unit Level M. Vladimirova Jakob Verbeek Pablo Mesejo Julyan Arbel BDL UQCV 19 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 34 16 0 09 Oct 2018
Information Geometry of Orthogonal Initializations and Training Piotr A. Sokól Il-Su Park AI4CE 85 16 0 09 Oct 2018
Deep convolutional Gaussian processes Kenneth Blomqvist Samuel Kaski Markus Heinonen BDL 33 60 0 06 Oct 2018
Generalization Properties of hyper-RKHS and its Applications Fanghui Liu Lei Shi Xiaolin Huang Jie Yang Johan A. K. Suykens 29 4 0 26 Sep 2018
Robustness Guarantees for Bayesian Inference with Gaussian Processes L. Cardelli Marta Kwiatkowska Luca Laurenti A. Patané AAML 24 52 0 17 Sep 2018
Deep Convolutional Networks as shallow Gaussian Processes Adrià Garriga-Alonso C. Rasmussen Laurence Aitchison BDL UQCV 35 268 0 16 Aug 2018
Noise Contrastive Priors for Functional Uncertainty Danijar Hafner Dustin Tran Timothy Lillicrap A. Irpan James Davidson AAML BDL UQCV 35 74 0 24 Jul 2018
EnsembleDAgger: A Bayesian Approach to Safe Imitation Learning Kunal Menda Katherine Driggs-Campbell Mykel J. Kochenderfer 19 117 0 22 Jul 2018
Phase Retrieval Under a Generative Prior Paul Hand Oscar Leong V. Voroninski 11 188 0 11 Jul 2018
Bayesian Deep Learning on a Quantum Computer Zhikuan Zhao Alejandro Pozas-Kerstjens Patrick Rebentrost P. Wittek UQCV BDL 28 69 0 29 Jun 2018
Adversarial Reprogramming of Neural Networks Gamaleldin F. Elsayed Ian Goodfellow Jascha Narain Sohl-Dickstein OOD AAML 16 178 0 28 Jun 2018
Neural-net-induced Gaussian process regression for function approximation and PDE solution G. Pang Liu Yang George Karniadakis 14 73 0 22 Jun 2018
Neural Tangent Kernel: Convergence and Generalization in Neural Networks Arthur Jacot Franck Gabriel Clément Hongler 72 3,126 0 20 Jun 2018
Insights on representational similarity in neural networks with canonical correlation Ari S. Morcos M. Raghu Samy Bengio DRL 32 434 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 24 115 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 26 2 0 06 Jun 2018
Universal Statistics of Fisher Information in Deep Neural Networks: Mean Field Approach Ryo Karakida S. Akaho S. Amari FedML 47 140 0 04 Jun 2018
Interpreting Deep Learning: The Machine Learning Rorschach Test? Adam S. Charles AAML HAI AI4CE 27 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 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 24 75 0 21 May 2018
Real-time regression analysis with deep convolutional neural networks Eliu A. Huerta D. George Zhizhen Zhao Gabrielle Allen 32 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 88 550 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 11 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 27 437 0 23 Feb 2018
Algorithmic Linearly Constrained Gaussian Processes Markus Lange-Hegermann 31 34 0 28 Jan 2018
Invariance of Weight Distributions in Rectified MLPs Russell Tsuchida Farbod Roosta-Khorasani M. Gallagher MLT 32 35 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 25 9 0 17 Nov 2017
Spatial Mapping with Gaussian Processes and Nonstationary Fourier Features Jean-François Ton Seth Flaxman Dino Sejdinovic Samir Bhatt GP 38 52 0 15 Nov 2017
Deep Learning: A Bayesian Perspective Nicholas G. Polson Vadim Sokolov BDL 54 115 0 01 Jun 2017
Quantifying the probable approximation error of probabilistic inference programs Marco F. Cusumano-Towner Vikash K. Mansinghka 33 7 0 31 May 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 289 9,167 0 06 Jun 2015