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The Bayesian Learning Rule

9 July 2021

Mohammad Emtiyaz Khan

Håvard Rue

BDL

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Papers citing "The Bayesian Learning Rule"

50 / 63 papers shown

Title
Spectral-factorized Positive-definite Curvature Learning for NN Training Wu Lin Felix Dangel Runa Eschenhagen Juhan Bae Richard E. Turner Roger B. Grosse 142 0 0 10 Feb 2025
Hellinger-Kantorovich Gradient Flows: Global Exponential Decay of Entropy Functionals Alexander Mielke Jia Jie Zhu 154 2 0 28 Jan 2025
Tractable structured natural gradient descent using local parameterizations Wu Lin Frank Nielsen Mohammad Emtiyaz Khan Mark Schmidt 69 30 0 15 Feb 2021
Homeomorphic-Invariance of EM: Non-Asymptotic Convergence in KL Divergence for Exponential Families via Mirror Descent Frederik Kunstner Raunak Kumar Mark Schmidt 75 22 0 02 Nov 2020
Fast Variational Learning in State-Space Gaussian Process Models Paul E. Chang William J. Wilkinson Mohammad Emtiyaz Khan Arno Solin BDL 44 24 0 09 Jul 2020
Training Binary Neural Networks using the Bayesian Learning Rule Xiangming Meng Roman Bachmann Mohammad Emtiyaz Khan BDL MQ 67 42 0 25 Feb 2020
Decision-Making with Auto-Encoding Variational Bayes Romain Lopez Pierre Boyeau Nir Yosef Michael I. Jordan Jeffrey Regier BDL 513 10,591 0 17 Feb 2020
Stein's Lemma for the Reparameterization Trick with Exponential Family Mixtures Wu Lin Mohammad Emtiyaz Khan Mark Schmidt 73 31 0 29 Oct 2019
Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations Wu Lin Mohammad Emtiyaz Khan Mark Schmidt BDL 49 71 0 07 Jun 2019
Practical Deep Learning with Bayesian Principles Kazuki Osawa S. Swaroop Anirudh Jain Runa Eschenhagen Richard Turner Rio Yokota Mohammad Emtiyaz Khan BDL UQCV 151 247 0 06 Jun 2019
Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization K. Helwegen James Widdicombe Lukas Geiger Zechun Liu K. Cheng Roeland Nusselder MQ 45 113 0 05 Jun 2019
Approximate Inference Turns Deep Networks into Gaussian Processes Mohammad Emtiyaz Khan Alexander Immer Ehsan Abedi M. Korzepa UQCV BDL 108 125 0 05 Jun 2019
Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets Penghang Yin J. Lyu Shuai Zhang Stanley Osher Y. Qi Jack Xin MQ LLMSV 106 319 0 13 Mar 2019
Divergence-Based Motivation for Online EM and Combining Hidden Variable Models Ehsan Amid Manfred K. Warmuth 45 4 0 11 Feb 2019
A Simple Baseline for Bayesian Uncertainty in Deep Learning Wesley J. Maddox T. Garipov Pavel Izmailov Dmitry Vetrov A. Wilson BDL UQCV 96 809 0 07 Feb 2019
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient Aaron Mishkin Frederik Kunstner Didrik Nielsen Mark Schmidt Mohammad Emtiyaz Khan BDL UQCV 75 60 0 11 Nov 2018
An Introduction to Probabilistic Programming Jan-Willem van de Meent Brooks Paige Hongseok Yang Frank Wood GP 73 200 0 27 Sep 2018
Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods Laurence Aitchison ODL 52 21 0 19 Jul 2018
Fast yet Simple Natural-Gradient Descent for Variational Inference in Complex Models Mohammad Emtiyaz Khan Didrik Nielsen BDL 85 63 0 12 Jul 2018
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam Mohammad Emtiyaz Khan Didrik Nielsen Voot Tangkaratt Wu Lin Y. Gal Akash Srivastava ODL 154 271 0 13 Jun 2018
Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models Hugh Salimbeni Stefanos Eleftheriadis J. Hensman BDL 73 86 0 24 Mar 2018
The Many Faces of Exponential Weights in Online Learning Dirk van der Hoeven T. Erven W. Kotłowski OffRL 55 50 0 21 Feb 2018
Noisy Natural Gradient as Variational Inference Guodong Zhang Shengyang Sun David Duvenaud Roger C. Grosse ODL 72 212 0 06 Dec 2017
Advances in Variational Inference Cheng Zhang Judith Butepage Hedvig Kjellström Stephan Mandt BDL 182 694 0 15 Nov 2017
Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks Pratik Chaudhari Stefano Soatto MLT 70 304 0 30 Oct 2017
The Marginal Value of Adaptive Gradient Methods in Machine Learning Ashia Wilson Rebecca Roelofs Mitchell Stern Nathan Srebro Benjamin Recht ODL 73 1,032 0 23 May 2017
Concrete Dropout Y. Gal Jiri Hron Alex Kendall BDL UQCV 179 593 0 22 May 2017
Stochastic Gradient Descent as Approximate Bayesian Inference Stephan Mandt Matthew D. Hoffman David M. Blei BDL 67 599 0 13 Apr 2017
Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data Gintare Karolina Dziugaite Daniel M. Roy 117 819 0 31 Mar 2017
Conjugate-Computation Variational Inference : Converting Variational Inference in Non-Conjugate Models to Inferences in Conjugate Models Mohammad Emtiyaz Khan Wu Lin BDL 53 137 0 13 Mar 2017
Online Natural Gradient as a Kalman Filter Yann Ollivier 70 68 0 01 Mar 2017
Monte Carlo Structured SVI for Two-Level Non-Conjugate Models Rishit Sheth Roni Khardon BDL 41 7 0 12 Dec 2016
Categorical Reparameterization with Gumbel-Softmax Eric Jang S. Gu Ben Poole BDL 361 5,379 0 03 Nov 2016
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables Chris J. Maddison A. Mnih Yee Whye Teh BDL 200 2,537 0 02 Nov 2016
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima N. Keskar Dheevatsa Mudigere J. Nocedal M. Smelyanskiy P. T. P. Tang ODL 429 2,945 0 15 Sep 2016
Optimization Methods for Large-Scale Machine Learning Léon Bottou Frank E. Curtis J. Nocedal 249 3,225 0 15 Jun 2016
Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors Christos Louizos Max Welling BDL 78 257 0 15 Mar 2016
Variational Inference: A Review for Statisticians David M. Blei A. Kucukelbir Jon D. McAuliffe BDL 298 4,807 0 04 Jan 2016
BinaryConnect: Training Deep Neural Networks with binary weights during propagations Matthieu Courbariaux Yoshua Bengio J. David MQ 212 2,992 0 02 Nov 2015
Evolutionary Multimodal Optimization: A Short Survey Ka-Chun Wong OffRL 42 28 0 03 Aug 2015
Bayesian Dark Knowledge Masashi Sugiyama Vivek Rathod R. Garnett Max Welling BDL UQCV 95 259 0 14 Jun 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 854 9,346 0 06 Jun 2015
Weight Uncertainty in Neural Networks Charles Blundell Julien Cornebise Koray Kavukcuoglu Daan Wierstra UQCV BDL 192 1,892 0 20 May 2015
On Graduated Optimization for Stochastic Non-Convex Problems Elad Hazan Kfir Y. Levy Shai Shalev-Shwartz 79 117 0 12 Mar 2015
Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks José Miguel Hernández-Lobato Ryan P. Adams UQCV BDL 142 945 0 18 Feb 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 2.1K 150,312 0 22 Dec 2014
New insights and perspectives on the natural gradient method James Martens ODL 76 630 0 03 Dec 2014
The Information Geometry of Mirror Descent Garvesh Raskutti S. Mukherjee 131 125 0 29 Oct 2013
Gaussian Processes for Big Data J. Hensman Nicolò Fusi Neil D. Lawrence GP 110 1,235 0 26 Sep 2013
Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation Yoshua Bengio Nicholas Léonard Aaron Courville 396 3,154 0 15 Aug 2013