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
FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing Feng Yin Zhidi Lin Yue Xu Qinglei Kong Deshi Li Sergios Theodoridis Shuguang Cui Cui FedML 21 4 0 08 Mar 2020
Neural Operator: Graph Kernel Network for Partial Differential Equations Zong-Yi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu K. Bhattacharya Andrew M. Stuart Anima Anandkumar 13 710 0 07 Mar 2020
Stable behaviour of infinitely wide deep neural networks Stefano Favaro S. Fortini Stefano Peluchetti BDL 17 28 0 01 Mar 2020
Infinitely Wide Graph Convolutional Networks: Semi-supervised Learning via Gaussian Processes Jilin Hu Jianbing Shen B. Yang Ling Shao BDL GNN 42 17 0 26 Feb 2020
Convex Geometry and Duality of Over-parameterized Neural Networks Tolga Ergen Mert Pilanci MLT 42 54 0 25 Feb 2020
Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite Networks Russell Tsuchida Tim Pearce Christopher van der Heide Fred Roosta M. Gallagher 8 8 0 20 Feb 2020
Robust Pruning at Initialization Soufiane Hayou Jean-François Ton Arnaud Doucet Yee Whye Teh 14 46 0 19 Feb 2020
Taylorized Training: Towards Better Approximation of Neural Network Training at Finite Width Yu Bai Ben Krause Huan Wang Caiming Xiong R. Socher 22 22 0 10 Feb 2020
Quasi-Equivalence of Width and Depth of Neural Networks Fenglei Fan Rongjie Lai Ge Wang 22 11 0 06 Feb 2020
On Random Kernels of Residual Architectures Etai Littwin Tomer Galanti Lior Wolf 14 4 0 28 Jan 2020
On the infinite width limit of neural networks with a standard parameterization Jascha Narain Sohl-Dickstein Roman Novak S. Schoenholz Jaehoon Lee 32 47 0 21 Jan 2020
On Random Matrices Arising in Deep Neural Networks. Gaussian Case L. Pastur 23 23 0 17 Jan 2020
Disentangling Trainability and Generalization in Deep Neural Networks Lechao Xiao Jeffrey Pennington S. Schoenholz 6 34 0 30 Dec 2019
Discriminative Clustering with Representation Learning with any Ratio of Labeled to Unlabeled Data Corinne Jones Vincent Roulet Zaïd Harchaoui 36 1 0 30 Dec 2019
Mean field theory for deep dropout networks: digging up gradient backpropagation deeply Wei Huang R. Xu Weitao Du Yutian Zeng Yunce Zhao 30 6 0 19 Dec 2019
Analytic expressions for the output evolution of a deep neural network Anastasia Borovykh 14 0 0 18 Dec 2019
Neural Tangents: Fast and Easy Infinite Neural Networks in Python Roman Novak Lechao Xiao Jiri Hron Jaehoon Lee Alexander A. Alemi Jascha Narain Sohl-Dickstein S. Schoenholz 38 225 0 05 Dec 2019
Implicit Priors for Knowledge Sharing in Bayesian Neural Networks Jack K. Fitzsimons Sebastian M. Schmon Stephen J. Roberts BDL FedML 16 0 0 02 Dec 2019
Richer priors for infinitely wide multi-layer perceptrons Russell Tsuchida Fred Roosta M. Gallagher 6 10 0 29 Nov 2019
Enhanced Convolutional Neural Tangent Kernels Zhiyuan Li Ruosong Wang Dingli Yu S. Du Wei Hu Ruslan Salakhutdinov Sanjeev Arora 21 131 0 03 Nov 2019
Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes Greg Yang 33 193 0 28 Oct 2019
Explicitly Bayesian Regularizations in Deep Learning Xinjie Lan Kenneth Barner UQCV BDL AI4CE 14 1 0 22 Oct 2019
Why bigger is not always better: on finite and infinite neural networks Laurence Aitchison 175 51 0 17 Oct 2019
The Renyi Gaussian Process: Towards Improved Generalization Xubo Yue Raed Al Kontar 107 3 0 15 Oct 2019
Pathological spectra of the Fisher information metric and its variants in deep neural networks Ryo Karakida S. Akaho S. Amari 33 28 0 14 Oct 2019
On the expected behaviour of noise regularised deep neural networks as Gaussian processes Arnu Pretorius Herman Kamper Steve Kroon 27 9 0 12 Oct 2019
Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks Sanjeev Arora S. Du Zhiyuan Li Ruslan Salakhutdinov Ruosong Wang Dingli Yu AAML 19 161 0 03 Oct 2019
Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks Yu Bai J. Lee 24 116 0 03 Oct 2019
Non-Gaussian processes and neural networks at finite widths Sho Yaida 39 87 0 30 Sep 2019
Wider Networks Learn Better Features D. Gilboa Guy Gur-Ari 15 7 0 25 Sep 2019
Neural networks are a priori biased towards Boolean functions with low entropy Chris Mingard Joar Skalse Guillermo Valle Pérez David Martínez-Rubio Vladimir Mikulik A. Louis FAtt AI4CE 27 37 0 25 Sep 2019
Asymptotics of Wide Networks from Feynman Diagrams Ethan Dyer Guy Gur-Ari 29 113 0 25 Sep 2019
Deep kernel learning for integral measurements Carl Jidling J. Hendriks Thomas B. Schon A. Wills 39 7 0 04 Sep 2019
On the Expressiveness of Approximate Inference in Bayesian Neural Networks Andrew Y. K. Foong David R. Burt Yingzhen Li Richard Turner UQCV BDL 17 20 0 02 Sep 2019
Deep Learning Theory Review: An Optimal Control and Dynamical Systems Perspective Guan-Horng Liu Evangelos A. Theodorou AI4CE 24 71 0 28 Aug 2019
Finite size corrections for neural network Gaussian processes J. Antognini BDL 30 29 0 27 Aug 2019
The generalization error of random features regression: Precise asymptotics and double descent curve Song Mei Andrea Montanari 62 626 0 14 Aug 2019
A Fine-Grained Spectral Perspective on Neural Networks Greg Yang Hadi Salman 35 111 0 24 Jul 2019
Disentangling feature and lazy training in deep neural networks Mario Geiger S. Spigler Arthur Jacot M. Wyart 17 17 0 19 Jun 2019
$A General $\mathcal{O}(n^2)$ Hyper-Parameter Optimization for Gaussian Process Regression with Cross-Validation and Non-linearly Constrained ADMM$ A General $\mathcal{O}(n^2)$ Hyper-Parameter Optimization for Gaussian Process Regression with Cross-Validation and Non-linearly Constrained ADMM Linning Xu Feng Yin Jiawei Zhang Zhi-Quan Luo Shuguang Cui GP 44 0 0 06 Jun 2019
Approximate Inference Turns Deep Networks into Gaussian Processes Mohammad Emtiyaz Khan Alexander Immer Ehsan Abedi M. Korzepa UQCV BDL 42 122 0 05 Jun 2019
Bayesian Evidential Deep Learning with PAC Regularization Manuel Haussmann S. Gerwinn M. Kandemir UQCV EDL BDL 6 1 0 03 Jun 2019
A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-Off Yaniv Blumenfeld D. Gilboa Daniel Soudry MQ 15 14 0 03 Jun 2019
Exact Convergence Rates of the Neural Tangent Kernel in the Large Depth Limit Soufiane Hayou Arnaud Doucet Judith Rousseau 16 4 0 31 May 2019
On the Inductive Bias of Neural Tangent Kernels A. Bietti Julien Mairal 28 253 0 29 May 2019
Walsh-Hadamard Variational Inference for Bayesian Deep Learning Simone Rossi Sébastien Marmin Maurizio Filippone BDL 32 14 0 27 May 2019
Infinitely deep neural networks as diffusion processes Stefano Peluchetti Stefano Favaro ODL 14 31 0 27 May 2019
Scalable Training of Inference Networks for Gaussian-Process Models Jiaxin Shi Mohammad Emtiyaz Khan Jun Zhu BDL 27 18 0 27 May 2019
Sub-Weibull distributions: generalizing sub-Gaussian and sub-Exponential properties to heavier-tailed distributions M. Vladimirova Stéphane Girard Hien Nguyen Julyan Arbel 25 86 0 13 May 2019
On Exact Computation with an Infinitely Wide Neural Net Sanjeev Arora S. Du Wei Hu Zhiyuan Li Ruslan Salakhutdinov Ruosong Wang 47 905 0 26 Apr 2019