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Bayesian Compression for Deep Learning

24 May 2017

Papers citing "Bayesian Compression for Deep Learning"

50 / 269 papers shown

Title
Bayesian Sparsification of Gated Recurrent Neural Networks E. Lobacheva Nadezhda Chirkova Dmitry Vetrov BDL 35 2 0 12 Dec 2018
Proximal Mean-field for Neural Network Quantization Thalaiyasingam Ajanthan P. Dokania Leonid Sigal Philip Torr MQ 89 20 0 11 Dec 2018
Accelerating Convolutional Neural Networks via Activation Map Compression Georgios Georgiadis 86 76 0 10 Dec 2018
Physics-informed deep generative models Yibo Yang P. Perdikaris AI4CE PINN 89 59 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 86 100 0 08 Dec 2018
Efficient and Robust Machine Learning for Real-World Systems Franz Pernkopf Wolfgang Roth Matthias Zöhrer Lukas Pfeifenberger Günther Schindler Holger Froening Sebastian Tschiatschek Robert Peharz Matthew Mattina Zoubin Ghahramani OOD 34 1 0 05 Dec 2018
ECC: Platform-Independent Energy-Constrained Deep Neural Network Compression via a Bilinear Regression Model Haichuan Yang Yuhao Zhu Ji Liu 111 40 0 05 Dec 2018
Split learning for health: Distributed deep learning without sharing raw patient data Praneeth Vepakomma O. Gupta Tristan Swedish Ramesh Raskar FedML 125 714 0 03 Dec 2018
Accelerate CNN via Recursive Bayesian Pruning Yuefu Zhou Ya Zhang Yanfeng Wang Qi Tian BDL 79 58 0 02 Dec 2018
Leveraging Filter Correlations for Deep Model Compression Pravendra Singh Vinay Kumar Verma Piyush Rai Vinay P. Namboodiri 95 65 0 26 Nov 2018
Structured Pruning for Efficient ConvNets via Incremental Regularization Huan Wang Qiming Zhang Yuehai Wang Haoji Hu 3DPC 105 45 0 20 Nov 2018
Stability Based Filter Pruning for Accelerating Deep CNNs Pravendra Singh Vinay Sameer Raja Kadi N. Verma Vinay P. Namboodiri CVBM 74 26 0 20 Nov 2018
Variational Bayesian Dropout with a Hierarchical Prior Yuhang Liu Wenyong Dong Lei Zhang Dong Gong Javen Qinfeng Shi BDL 52 18 0 19 Nov 2018
Iteratively Training Look-Up Tables for Network Quantization Fabien Cardinaux Stefan Uhlich K. Yoshiyama J. A. García Stephen Tiedemann Thomas Kemp Akira Nakamura MQ 56 1 0 13 Nov 2018
Generalized Ternary Connect: End-to-End Learning and Compression of Multiplication-Free Deep Neural Networks Samyak Parajuli Aswin Raghavan S. Chai 52 7 0 12 Nov 2018
FLOPs as a Direct Optimization Objective for Learning Sparse Neural Networks Gautam Bhattacharya Ashutosh Adhikari Md. Jahangir Alam 85 33 0 07 Nov 2018
Variational Dropout via Empirical Bayes V. Kharitonov Dmitry Molchanov Dmitry Vetrov BDL 65 9 0 01 Nov 2018
Bayesian Compression for Natural Language Processing Nadezhda Chirkova E. Lobacheva Dmitry Vetrov BDL 65 15 0 25 Oct 2018
The Deep Weight Prior Andrei Atanov Arsenii Ashukha Kirill Struminsky Dmitry Vetrov Max Welling BDL 125 37 0 16 Oct 2018
A Closer Look at Structured Pruning for Neural Network Compression Elliot J. Crowley Jack Turner Amos Storkey Michael F. P. O'Boyle 3DPC 80 31 0 10 Oct 2018
Rate Distortion For Model Compression: From Theory To Practice Weihao Gao Yu-Han Liu Chong-Jun Wang Sewoong Oh 93 31 0 09 Oct 2018
The Outer Product Structure of Neural Network Derivatives Craig Bakker Michael J. Henry Nathan Oken Hodas 27 3 0 09 Oct 2018
Doubly Semi-Implicit Variational Inference Dmitry Molchanov V. Kharitonov Artem Sobolev Dmitry Vetrov BDL 115 40 0 05 Oct 2018
Relaxed Quantization for Discretized Neural Networks Christos Louizos M. Reisser Tijmen Blankevoort E. Gavves Max Welling MQ 110 132 0 03 Oct 2018
Layer-compensated Pruning for Resource-constrained Convolutional Neural Networks Ting-Wu Chin Cha Zhang Diana Marculescu 86 46 0 01 Oct 2018
Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters Marton Havasi Robert Peharz José Miguel Hernández-Lobato 80 82 0 30 Sep 2018
Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks Jack Turner José Cano Valentin Radu Elliot J. Crowley Michael F. P. O'Boyle Amos Storkey 43 40 0 19 Sep 2018
Discretely Relaxing Continuous Variables for tractable Variational Inference Trefor W. Evans P. Nair BDL 59 0 0 12 Sep 2018
Probabilistic Binary Neural Networks Jorn W. T. Peters Max Welling BDL UQCV MQ 86 52 0 10 Sep 2018
Learning Sparse Low-Precision Neural Networks With Learnable Regularization Yoojin Choi Mostafa El-Khamy Jungwon Lee MQ 73 31 0 01 Sep 2018
Learning to Quantize Deep Networks by Optimizing Quantization Intervals with Task Loss S. Jung Changyong Son Seohyung Lee JinWoo Son Youngjun Kwak Jae-Joon Han Sung Ju Hwang Changkyu Choi MQ 100 376 0 17 Aug 2018
Noise Contrastive Priors for Functional Uncertainty Danijar Hafner Dustin Tran Timothy Lillicrap A. Irpan James Davidson AAML BDL UQCV 150 74 0 24 Jul 2018
Variational Bayesian dropout: pitfalls and fixes Jiri Hron A. G. Matthews Zoubin Ghahramani BDL 104 67 0 05 Jul 2018
Neural Processes M. Garnelo Jonathan Richard Schwarz Dan Rosenbaum Fabio Viola Danilo Jimenez Rezende S. M. Ali Eslami Yee Whye Teh BDL UQCV GP 138 517 0 04 Jul 2018
Conditional Neural Processes M. Garnelo Dan Rosenbaum Chris J. Maddison Tiago Ramalho D. Saxton Murray Shanahan Yee Whye Teh Danilo Jimenez Rezende S. M. Ali Eslami UQCV BDL 94 708 0 04 Jul 2018
Selfless Sequential Learning Rahaf Aljundi Marcus Rohrbach Tinne Tuytelaars CLL 72 114 0 14 Jun 2018
Scalable Neural Network Compression and Pruning Using Hard Clustering and L1 Regularization Yibo Yang Nicholas Ruozzi Vibhav Gogate 33 2 0 14 Jun 2018
Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors S. Ghosh Jiayu Yao Finale Doshi-Velez BDL UQCV 70 78 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 116 36 0 12 Jun 2018
Adaptive Network Sparsification with Dependent Variational Beta-Bernoulli Dropout Juho Lee Saehoon Kim Jaehong Yoon Haebeom Lee Eunho Yang Sung Ju Hwang 55 12 0 28 May 2018
Compact and Computationally Efficient Representation of Deep Neural Networks Simon Wiedemann K. Müller Wojciech Samek MQ 96 71 0 27 May 2018
Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers Marcel Hirt P. Dellaportas BDL 81 10 0 23 May 2018
AutoPruner: An End-to-End Trainable Filter Pruning Method for Efficient Deep Model Inference Jian-Hao Luo Jianxin Wu 98 211 0 23 May 2018
Compression of Deep Convolutional Neural Networks under Joint Sparsity Constraints Yoojin Choi Mostafa El-Khamy Jungwon Lee 40 6 0 21 May 2018
Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation Manuel Haussmann Fred Hamprecht M. Kandemir BDL 72 6 0 19 May 2018
Nonparametric Bayesian Deep Networks with Local Competition Konstantinos P. Panousis S. Chatzis Sergios Theodoridis BDL 102 32 0 19 May 2018
UNIQ: Uniform Noise Injection for Non-Uniform Quantization of Neural Networks Chaim Baskin Eli Schwartz Evgenii Zheltonozhskii Natan Liss Raja Giryes A. Bronstein A. Mendelson MQ 104 45 0 29 Apr 2018
Low-memory convolutional neural networks through incremental depth-first processing Jonathan Binas Yoshua Bengio SupR 51 3 0 28 Apr 2018
Measuring the Intrinsic Dimension of Objective Landscapes Chunyuan Li Heerad Farkhoor Rosanne Liu J. Yosinski 95 418 0 24 Apr 2018
Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches Yeming Wen Paul Vicol Jimmy Ba Dustin Tran Roger C. Grosse BDL 101 315 0 12 Mar 2018