Principled Pruning of Bayesian Neural Networks through Variational Free Energy Minimization

17 October 2022

Papers citing "Principled Pruning of Bayesian Neural Networks through Variational Free Energy Minimization"

24 / 24 papers shown

Title
Robust Speech Recognition via Large-Scale Weak Supervision Alec Radford Jong Wook Kim Tao Xu Greg Brockman C. McLeavey Ilya Sutskever OffRL 104 3,515 0 06 Dec 2022
Restoring speech intelligibility for hearing aid users with deep learning P. U. Diehl Y. Singer Hannes Zilly U. Schonfeld Paul Meyer-Rachner Mark Berry Henning Sprekeler Elias Sprengel A. Pudszuhn V. Hofmann 19 19 0 23 Jun 2022
Hierarchical Text-Conditional Image Generation with CLIP Latents Aditya A. Ramesh Prafulla Dhariwal Alex Nichol Casey Chu Mark Chen VLM DiffM 259 6,768 0 13 Apr 2022
Priors in Bayesian Deep Learning: A Review Vincent Fortuin UQCV BDL 67 127 0 14 May 2021
Compacting Deep Neural Networks for Internet of Things: Methods and Applications Ke Zhang Hanbo Ying Hongning Dai Lin Li Yuangyuang Peng Keyi Guo Hongfang Yu 47 38 0 20 Mar 2021
Bayesian Neural Network Priors Revisited Vincent Fortuin Adrià Garriga-Alonso Sebastian W. Ober F. Wenzel Gunnar Rätsch Richard Turner Mark van der Wilk Laurence Aitchison BDL UQCV 79 140 0 12 Feb 2021
A Survey on Deep Neural Network Compression: Challenges, Overview, and Solutions Rahul Mishra Hari Prabhat Gupta Tanima Dutta 28 90 0 05 Oct 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 56 620 0 14 Jul 2020
TinyLSTMs: Efficient Neural Speech Enhancement for Hearing Aids Igor Fedorov Marko Stamenovic Carl R. Jensen Li-Chia Yang Ari Mandell Yiming Gan Matthew Mattina P. Whatmough 26 97 0 20 May 2020
Decision-Making with Auto-Encoding Variational Bayes Romain Lopez Pierre Boyeau Nir Yosef Michael I. Jordan Jeffrey Regier BDL 138 10,591 0 17 Feb 2020
Bayesian Compression for Natural Language Processing Nadezhda Chirkova E. Lobacheva Dmitry Vetrov BDL 27 15 0 25 Oct 2018
Variational Bayesian dropout: pitfalls and fixes Jiri Hron A. G. Matthews Zoubin Ghahramani BDL 58 67 0 05 Jul 2018
A Survey of Model Compression and Acceleration for Deep Neural Networks Yu Cheng Duo Wang Pan Zhou Zhang Tao 53 1,091 0 23 Oct 2017
Bayesian Compression for Deep Learning Christos Louizos Karen Ullrich Max Welling UQCV BDL 84 479 0 24 May 2017
Variational Dropout Sparsifies Deep Neural Networks Dmitry Molchanov Arsenii Ashukha Dmitry Vetrov BDL 70 825 0 19 Jan 2017
Variational Inference: A Review for Statisticians David M. Blei A. Kucukelbir Jon D. McAuliffe BDL 159 4,748 0 04 Jan 2016
Variational Dropout and the Local Reparameterization Trick Diederik P. Kingma Tim Salimans Max Welling BDL 135 1,500 0 08 Jun 2015
Weight Uncertainty in Neural Networks Charles Blundell Julien Cornebise Koray Kavukcuoglu Daan Wierstra UQCV BDL 99 1,878 0 20 May 2015
Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks José Miguel Hernández-Lobato Ryan P. Adams UQCV BDL 57 940 0 18 Feb 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 577 149,474 0 22 Dec 2014
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation Kyunghyun Cho B. V. Merrienboer Çağlar Gülçehre Dzmitry Bahdanau Fethi Bougares Holger Schwenk Yoshua Bengio AIMat 563 23,235 0 03 Jun 2014
Stochastic Variational Inference Matt Hoffman David M. Blei Chong-Jun Wang John Paisley BDL 163 2,605 0 29 Jun 2012
MCMC using Hamiltonian dynamics Radford M. Neal 280 3,278 0 09 Jun 2012
The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo Matthew D. Hoffman Andrew Gelman 123 4,275 0 18 Nov 2011