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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
Drawing Multiple Augmentation Samples Per Image During Training Efficiently Decreases Test Error Stanislav Fort Andrew Brock Razvan Pascanu Soham De Samuel L. Smith 64 32 0 27 May 2021
Calibration and Uncertainty Quantification of Bayesian Convolutional Neural Networks for Geophysical Applications L. Mosser E. Naeini UQCV BDL 19 0 0 25 May 2021
Properties of the After Kernel Philip M. Long 66 29 0 21 May 2021
Scalable Bayesian Approach for the DINA Q-matrix Estimation Combining Stochastic Optimization and Variational Inference Motonori Oka Kensuke Okada 44 6 0 20 May 2021
On the Distributional Properties of Adaptive Gradients Z. Zhiyi Liu Ziyin 40 4 0 15 May 2021
An Effective Baseline for Robustness to Distributional Shift S. Thulasidasan Sushil Thapa S. Dhaubhadel Gopinath Chennupati Tanmoy Bhattacharya J. Bilmes OOD OODD 70 27 0 15 May 2021
What Are Bayesian Neural Network Posteriors Really Like? Pavel Izmailov Sharad Vikram Matthew D. Hoffman A. Wilson UQCV BDL 81 389 0 29 Apr 2021
Discriminative Bayesian filtering lends momentum to the stochastic Newton method for minimizing log-convex functions Michael C. Burkhart 48 0 0 27 Apr 2021
SGD Implicitly Regularizes Generalization Error Daniel A. Roberts MLT 62 15 0 10 Apr 2021
Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization Zeke Xie Li-xin Yuan Zhanxing Zhu Masashi Sugiyama 123 29 0 31 Mar 2021
LiBRe: A Practical Bayesian Approach to Adversarial Detection Zhijie Deng Xiao Yang Shizhen Xu Hang Su Jun Zhu BDL AAML 81 62 0 27 Mar 2021
Sampling-free Variational Inference for Neural Networks with Multiplicative Activation Noise Jannik Schmitt Stefan Roth UQCV 47 6 0 15 Mar 2021
On the Validity of Modeling SGD with Stochastic Differential Equations (SDEs) Zhiyuan Li Sadhika Malladi Sanjeev Arora 104 80 0 24 Feb 2021
Wirelessly Powered Federated Edge Learning: Optimal Tradeoffs Between Convergence and Power Transfer Qunsong Zeng Yuqing Du Kaibin Huang 92 37 0 24 Feb 2021
Scalable nonparametric Bayesian learning for heterogeneous and dynamic velocity fields Sunrit Chakraborty Aritra Guha Rayleigh Lei X. Nguyen 8 1 0 15 Feb 2021
Goal-oriented adaptive sampling under random field modelling of response probability distributions Athénais Gautier D. Ginsbourger G. Pirot 33 2 0 15 Feb 2021
Strength of Minibatch Noise in SGD Liu Ziyin Kangqiao Liu Takashi Mori Masakuni Ueda ODL MLT 64 35 0 10 Feb 2021
On the Origin of Implicit Regularization in Stochastic Gradient Descent Samuel L. Smith Benoit Dherin David Barrett Soham De MLT 62 204 0 28 Jan 2021
Estimating informativeness of samples with Smooth Unique Information Hrayr Harutyunyan Alessandro Achille Giovanni Paolini Orchid Majumder Avinash Ravichandran Rahul Bhotika Stefano Soatto 95 25 0 17 Jan 2021
A Bayesian neural network predicts the dissolution of compact planetary systems M. Cranmer Daniel Tamayo H. Rein Peter W. Battaglia S. Hadden P. Armitage S. Ho D. Spergel BDL 112 28 0 11 Jan 2021
The shifted ODE method for underdamped Langevin MCMC James Foster Terry Lyons Harald Oberhauser 93 16 0 10 Jan 2021
Robustness, Privacy, and Generalization of Adversarial Training Fengxiang He Shaopeng Fu Bohan Wang Dacheng Tao 119 10 0 25 Dec 2020
Recent advances in deep learning theory Fengxiang He Dacheng Tao AI4CE 130 51 0 20 Dec 2020
Improved Image Matting via Real-time User Clicks and Uncertainty Estimation Tianyi Wei Dongdong Chen Wenbo Zhou Jing Liao Hanqing Zhao Weiming Zhang Nenghai Yu 61 29 0 15 Dec 2020
Neural Mechanics: Symmetry and Broken Conservation Laws in Deep Learning Dynamics D. Kunin Javier Sagastuy-Breña Surya Ganguli Daniel L. K. Yamins Hidenori Tanaka 167 80 0 08 Dec 2020
Noise and Fluctuation of Finite Learning Rate Stochastic Gradient Descent Kangqiao Liu Liu Ziyin Masakuni Ueda MLT 149 39 0 07 Dec 2020
A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving Di Feng Ali Harakeh Steven Waslander Klaus C. J. Dietmayer AAML UQCV EDL 126 227 0 20 Nov 2020
Variational Laplace for Bayesian neural networks Ali Unlu Laurence Aitchison UQCV BDL 37 0 0 20 Nov 2020
Efficient and Transferable Adversarial Examples from Bayesian Neural Networks Martin Gubri Maxime Cordy Mike Papadakis Yves Le Traon Koushik Sen AAML 151 11 0 10 Nov 2020
On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method Ye He Krishnakumar Balasubramanian Murat A. Erdogdu 66 35 0 06 Nov 2020
A Bayesian Perspective on Training Speed and Model Selection Clare Lyle Lisa Schut Binxin Ru Y. Gal Mark van der Wilk 99 24 0 27 Oct 2020
Deep Learning is Singular, and That's Good Daniel Murfet Susan Wei Biwei Huang Hui Li Jesse Gell-Redman T. Quella UQCV 79 29 0 22 Oct 2020
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms Maruan Al-Shedivat Jennifer Gillenwater Eric Xing Afshin Rostamizadeh FedML 126 112 0 11 Oct 2020
Small Data, Big Decisions: Model Selection in the Small-Data Regime J. Bornschein Francesco Visin Simon Osindero 63 40 0 26 Sep 2020
Implicit Gradient Regularization David Barrett Benoit Dherin 101 152 0 23 Sep 2020
Extending Label Smoothing Regularization with Self-Knowledge Distillation Jiyue Wang Pei Zhang Wenjie Pang Jie Li 16 0 0 11 Sep 2020
Ramifications of Approximate Posterior Inference for Bayesian Deep Learning in Adversarial and Out-of-Distribution Settings John Mitros A. Pakrashi Brian Mac Namee UQCV 98 2 0 03 Sep 2020
Robust, Accurate Stochastic Optimization for Variational Inference Akash Kumar Dhaka Alejandro Catalina Michael Riis Andersen Maans Magnusson Jonathan H. Huggins Aki Vehtari 71 34 0 01 Sep 2020
Learning explanations that are hard to vary Giambattista Parascandolo Alexander Neitz Antonio Orvieto Luigi Gresele Bernhard Schölkopf FAtt 86 187 0 01 Sep 2020
Noise-induced degeneration in online learning Yuzuru Sato Daiji Tsutsui A. Fujiwara 58 2 0 24 Aug 2020
Intelligence plays dice: Stochasticity is essential for machine learning M. Sabuncu 127 6 0 17 Aug 2020
Efficient hyperparameter optimization by way of PAC-Bayes bound minimization John J. Cherian Andrew G. Taube R. McGibbon Panagiotis Angelikopoulos Guy Blanc M. Snarski D. D. Richman J. L. Klepeis D. Shaw 33 6 0 14 Aug 2020
Neural networks with late-phase weights J. Oswald Seijin Kobayashi Alexander Meulemans Christian Henning Benjamin Grewe João Sacramento 94 35 0 25 Jul 2020
Fast Learning for Renewal Optimization in Online Task Scheduling M. Neely 102 13 0 18 Jul 2020
Tighter Generalization Bounds for Iterative Differentially Private Learning Algorithms Fengxiang He Bohan Wang Dacheng Tao FedML 55 18 0 18 Jul 2020
On stochastic mirror descent with interacting particles: convergence properties and variance reduction Anastasia Borovykh N. Kantas P. Parpas G. Pavliotis 55 12 0 15 Jul 2020
Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users Laurent Valentin Jospin Wray Buntine F. Boussaïd Hamid Laga Bennamoun OOD BDL UQCV 93 634 0 14 Jul 2020
Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and Momentum Zeke Xie Xinrui Wang Huishuai Zhang Issei Sato Masashi Sugiyama ODL 101 47 0 29 Jun 2020
Is SGD a Bayesian sampler? Well, almost Chris Mingard Guillermo Valle Pérez Joar Skalse A. Louis BDL 77 53 0 26 Jun 2020
On the Generalization Benefit of Noise in Stochastic Gradient Descent Samuel L. Smith Erich Elsen Soham De MLT 62 100 0 26 Jun 2020