Bayesian Compression for Deep Learning

24 May 2017

Papers citing "Bayesian Compression for Deep Learning"

22 / 72 papers shown

Title
Where Do Human Heuristics Come From? Marcel Binz Dominik M. Endres 16 0 0 20 Feb 2019
Proximal Mean-field for Neural Network Quantization Thalaiyasingam Ajanthan P. Dokania Richard I. Hartley Philip H. S. Torr MQ 30 20 0 11 Dec 2018
Physics-informed deep generative models Yibo Yang P. Perdikaris AI4CE PINN 19 57 0 09 Dec 2018
No Peek: A Survey of private distributed deep learning Praneeth Vepakomma Tristan Swedish Ramesh Raskar O. Gupta Abhimanyu Dubey SyDa FedML 22 99 0 08 Dec 2018
Split learning for health: Distributed deep learning without sharing raw patient data Praneeth Vepakomma O. Gupta Tristan Swedish Ramesh Raskar FedML 37 692 0 03 Dec 2018
Rate Distortion For Model Compression: From Theory To Practice Weihao Gao Yu-Han Liu Chong-Jun Wang Sewoong Oh 25 31 0 09 Oct 2018
Relaxed Quantization for Discretized Neural Networks Christos Louizos M. Reisser Tijmen Blankevoort E. Gavves Max Welling MQ 27 131 0 03 Oct 2018
Probabilistic Binary Neural Networks Jorn W. T. Peters Max Welling BDL UQCV MQ 17 50 0 10 Sep 2018
Selfless Sequential Learning Rahaf Aljundi Marcus Rohrbach Tinne Tuytelaars CLL 28 114 0 14 Jun 2018
Scalable Neural Network Compression and Pruning Using Hard Clustering and L1 Regularization Yibo Yang Nicholas Ruozzi Vibhav Gogate 16 2 0 14 Jun 2018
Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors S. Ghosh Jiayu Yao Finale Doshi-Velez BDL UQCV 15 77 0 13 Jun 2018
Energy-Constrained Compression for Deep Neural Networks via Weighted Sparse Projection and Layer Input Masking Haichuan Yang Yuhao Zhu Ji Liu CVBM 14 36 0 12 Jun 2018
Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers Marcel Hirt P. Dellaportas BDL 20 10 0 23 May 2018
Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation Manuel Haussmann Fred Hamprecht M. Kandemir BDL 18 6 0 19 May 2018
Nonparametric Bayesian Deep Networks with Local Competition Konstantinos P. Panousis S. Chatzis Sergios Theodoridis BDL 22 32 0 19 May 2018
Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches Yeming Wen Paul Vicol Jimmy Ba Dustin Tran Roger C. Grosse BDL 9 307 0 12 Mar 2018
Compressing Neural Networks using the Variational Information Bottleneck Bin Dai Chen Zhu David Wipf MLT 24 178 0 28 Feb 2018
The Description Length of Deep Learning Models Léonard Blier Yann Ollivier 24 95 0 20 Feb 2018
Learning Discrete Weights Using the Local Reparameterization Trick Oran Shayer Dan Levi Ethan Fetaya 13 88 0 21 Oct 2017
Learning Intrinsic Sparse Structures within Long Short-Term Memory W. Wen Yuxiong He Samyam Rajbhandari Minjia Zhang Wenhan Wang Fang Liu Bin Hu Yiran Chen H. Li MQ 27 140 0 15 Sep 2017
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,138 0 06 Jun 2015
Improving neural networks by preventing co-adaptation of feature detectors Geoffrey E. Hinton Nitish Srivastava A. Krizhevsky Ilya Sutskever Ruslan Salakhutdinov VLM 266 7,636 0 03 Jul 2012