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Stochastic Gradient Descent as Approximate Bayesian Inference

13 April 2017

Matthew D. Hoffman

Papers citing "Stochastic Gradient Descent as Approximate Bayesian Inference"

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Title
Dynamic of Stochastic Gradient Descent with State-Dependent Noise Qi Meng Shiqi Gong Wei Chen Zhi-Ming Ma Tie-Yan Liu 53 16 0 24 Jun 2020
Free-rider Attacks on Model Aggregation in Federated Learning Yann Fraboni Richard Vidal Marco Lorenzi FedML 85 131 0 21 Jun 2020
PAC-Bayesian Generalization Bounds for MultiLayer Perceptrons Xinjie Lan Xin Guo Kenneth Barner 47 3 0 16 Jun 2020
Deep Autoencoding Topic Model with Scalable Hybrid Bayesian Inference Hao Zhang Bo Chen Yulai Cong D. Guo Hongwei Liu Mingyuan Zhou BDL 64 28 0 15 Jun 2020
Mean-Field Approximation to Gaussian-Softmax Integral with Application to Uncertainty Estimation Zhiyun Lu Eugene Ie Fei Sha UQCV BDL 68 14 0 13 Jun 2020
Isotropic SGD: a Practical Approach to Bayesian Posterior Sampling Giulio Franzese Rosa Candela Dimitrios Milios Maurizio Filippone Pietro Michiardi 10 1 0 09 Jun 2020
Predictive Coding Approximates Backprop along Arbitrary Computation Graphs Beren Millidge Alexander Tschantz Christopher L. Buckley 123 124 0 07 Jun 2020
Auto-decoding Graphs Sohil Shah V. Koltun GNN 55 4 0 04 Jun 2020
Learning Rate Annealing Can Provably Help Generalization, Even for Convex Problems Preetum Nakkiran MLT 64 21 0 15 May 2020
Ensembled sparse-input hierarchical networks for high-dimensional datasets Jean Feng N. Simon 26 4 0 11 May 2020
The Impact of the Mini-batch Size on the Variance of Gradients in Stochastic Gradient Descent Xin-Yao Qian Diego Klabjan ODL 72 36 0 27 Apr 2020
Analysis of Stochastic Gradient Descent in Continuous Time J. Latz 81 41 0 15 Apr 2020
On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration Wenlong Mou C. J. Li Martin J. Wainwright Peter L. Bartlett Michael I. Jordan 85 76 0 09 Apr 2020
Predicting the outputs of finite deep neural networks trained with noisy gradients Gadi Naveh Oded Ben-David H. Sompolinsky Zohar Ringel 112 23 0 02 Apr 2020
Robust and On-the-fly Dataset Denoising for Image Classification Jiaming Song Lunjia Hu Michael Auli Yann N. Dauphin Tengyu Ma NoLa OOD 90 13 0 24 Mar 2020
Online stochastic gradient descent on non-convex losses from high-dimensional inference Gerard Ben Arous Reza Gheissari Aukosh Jagannath 108 91 0 23 Mar 2020
The Implicit Regularization of Stochastic Gradient Flow for Least Squares Alnur Ali Yan Sun Robert Tibshirani 103 77 0 17 Mar 2020
A comprehensive study on the prediction reliability of graph neural networks for virtual screening Soojung Yang K. Lee Seongok Ryu 61 7 0 17 Mar 2020
Bayesian optimization of variable-size design space problems J. Pelamatti Loïc Brevault M. Balesdent El-Ghazali Talbi Yannick Guerin 80 29 0 06 Mar 2020
The large learning rate phase of deep learning: the catapult mechanism Aitor Lewkowycz Yasaman Bahri Ethan Dyer Jascha Narain Sohl-Dickstein Guy Gur-Ari ODL 221 241 0 04 Mar 2020
A Free-Energy Principle for Representation Learning Yansong Gao Pratik Chaudhari DRL 57 9 0 27 Feb 2020
Batch Normalization Biases Residual Blocks Towards the Identity Function in Deep Networks Soham De Samuel L. Smith ODL 106 20 0 24 Feb 2020
The Two Regimes of Deep Network Training Guillaume Leclerc Aleksander Madry 94 45 0 24 Feb 2020
Improving Sampling Accuracy of Stochastic Gradient MCMC Methods via Non-uniform Subsampling of Gradients Ruilin Li Xin Wang H. Zha Molei Tao 34 4 0 20 Feb 2020
Being Bayesian about Categorical Probability Taejong Joo U. Chung Minji Seo UQCV BDL 97 61 0 19 Feb 2020
Stochasticity of Deterministic Gradient Descent: Large Learning Rate for Multiscale Objective Function Lingkai Kong Molei Tao 57 23 0 14 Feb 2020
Deep Learning for Source Code Modeling and Generation: Models, Applications and Challenges T. H. Le Hao Chen Muhammad Ali Babar VLM 147 155 0 13 Feb 2020
A Diffusion Theory For Deep Learning Dynamics: Stochastic Gradient Descent Exponentially Favors Flat Minima Zeke Xie Issei Sato Masashi Sugiyama ODL 127 17 0 10 Feb 2020
On Last-Layer Algorithms for Classification: Decoupling Representation from Uncertainty Estimation N. Brosse C. Riquelme Alice Martin Sylvain Gelly Eric Moulines BDL OOD UQCV 97 34 0 22 Jan 2020
Reinforcement Learning with Probabilistically Complete Exploration Philippe Morere Gilad Francis Tom Blau Fabio Ramos OffRL 31 7 0 20 Jan 2020
Stochastic Weight Averaging in Parallel: Large-Batch Training that Generalizes Well Vipul Gupta S. Serrano D. DeCoste MoMe 83 60 0 07 Jan 2020
Analytic expressions for the output evolution of a deep neural network Anastasia Borovykh 29 0 0 18 Dec 2019
Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection Erik A. Daxberger José Miguel Hernández-Lobato UQCV 103 63 0 11 Dec 2019
Deep Ensembles: A Loss Landscape Perspective Stanislav Fort Huiyi Hu Balaji Lakshminarayanan OOD UQCV 157 631 0 05 Dec 2019
On the Validity of Bayesian Neural Networks for Uncertainty Estimation John Mitros Brian Mac Namee UQCV BDL 98 30 0 03 Dec 2019
Measuring Uncertainty through Bayesian Learning of Deep Neural Network Structure Zhijie Deng Yucen Luo Jun Zhu Bo Zhang UQCV BDL 43 2 0 22 Nov 2019
Bayesian interpretation of SGD as Ito process Soma Yokoi Issei Sato 40 5 0 20 Nov 2019
Understanding the Role of Momentum in Stochastic Gradient Methods Igor Gitman Hunter Lang Pengchuan Zhang Lin Xiao 77 95 0 30 Oct 2019
Online Stochastic Gradient Descent with Arbitrary Initialization Solves Non-smooth, Non-convex Phase Retrieval Yan Shuo Tan Roman Vershynin 79 35 0 28 Oct 2019
Sparsification as a Remedy for Staleness in Distributed Asynchronous SGD Rosa Candela Giulio Franzese Maurizio Filippone Pietro Michiardi 91 1 0 21 Oct 2019
Challenges in Markov chain Monte Carlo for Bayesian neural networks Theodore Papamarkou Jacob D. Hinkle M. T. Young D. Womble BDL 131 51 0 15 Oct 2019
Evaluating Scalable Uncertainty Estimation Methods for DNN-Based Molecular Property Prediction Gabriele Scalia Colin A. Grambow Barbara Pernici Yi‐Pei Li W. Green BDL 89 8 0 07 Oct 2019
Student Specialization in Deep ReLU Networks With Finite Width and Input Dimension Yuandong Tian MLT 57 8 0 30 Sep 2019
On Model Stability as a Function of Random Seed Pranava Madhyastha Dhruv Batra 102 63 0 23 Sep 2019
Using Statistics to Automate Stochastic Optimization Hunter Lang Pengchuan Zhang Lin Xiao 86 22 0 21 Sep 2019
Variationally Inferred Sampling Through a Refined Bound for Probabilistic Programs Víctor Gallego D. Insua BDL 44 1 0 26 Aug 2019
Bayesian Inference for Large Scale Image Classification Jonathan Heek Nal Kalchbrenner UQCV BDL 146 35 0 09 Aug 2019
Representation Degeneration Problem in Training Natural Language Generation Models Jun Gao Di He Xu Tan Tao Qin Liwei Wang Tie-Yan Liu 76 271 0 28 Jul 2019
Probabilistic Approximate Logic and its Implementation in the Logical Imagination Engine Mark-Oliver Stehr Minyoung Kim C. Talcott M. Knapp A. Vertes 47 2 0 25 Jul 2019
Hessian based analysis of SGD for Deep Nets: Dynamics and Generalization Xinyan Li Qilong Gu Yingxue Zhou Tiancong Chen A. Banerjee ODL 88 52 0 24 Jul 2019