The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

9 March 2018

Papers citing "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks"

30 / 730 papers shown

Title
Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems Antonio A. Ginart Maxim Naumov Dheevatsa Mudigere Jiyan Yang James Zou 22 99 0 25 Sep 2019
RNN Architecture Learning with Sparse Regularization Jesse Dodge Roy Schwartz Hao Peng Noah A. Smith 20 10 0 06 Sep 2019
Image Captioning with Sparse Recurrent Neural Network J. Tan Chee Seng Chan Joon Huang Chuah VLM 29 6 0 28 Aug 2019
A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Meng Tang Yimin Liu L. Durlofsky AI4CE 32 257 0 16 Aug 2019
Convolutional Dictionary Learning in Hierarchical Networks Javier Zazo Bahareh Tolooshams Demba E. Ba BDL 37 5 0 23 Jul 2019
Padé Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks Alejandro Molina P. Schramowski Kristian Kersting ODL 23 79 0 15 Jul 2019
Bringing Giant Neural Networks Down to Earth with Unlabeled Data Yehui Tang Shan You Chang Xu Boxin Shi Chao Xu 24 11 0 13 Jul 2019
Sparse Networks from Scratch: Faster Training without Losing Performance Tim Dettmers Luke Zettlemoyer 20 334 0 10 Jul 2019
XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera Dushyant Mehta Oleksandr Sotnychenko Franziska Mueller Weipeng Xu Mohamed A. Elgharib Pascal Fua Hans-Peter Seidel Helge Rhodin Gerard Pons-Moll Christian Theobalt 3DH 18 168 0 01 Jul 2019
Selection via Proxy: Efficient Data Selection for Deep Learning Cody Coleman Christopher Yeh Stephen Mussmann Baharan Mirzasoleiman Peter Bailis Percy Liang J. Leskovec Matei A. Zaharia 33 330 0 26 Jun 2019
Weight Agnostic Neural Networks Adam Gaier David R Ha OOD 38 239 0 11 Jun 2019
One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers Ari S. Morcos Haonan Yu Michela Paganini Yuandong Tian 16 228 0 06 Jun 2019
SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers Igor Fedorov Ryan P. Adams Matthew Mattina P. Whatmough 13 165 0 28 May 2019
Self-supervised audio representation learning for mobile devices Marco Tagliasacchi Beat Gfeller Félix de Chaumont Quitry Dominik Roblek SSL AI4TS 6 46 0 24 May 2019
Structured Compression by Weight Encryption for Unstructured Pruning and Quantization S. Kwon Dongsoo Lee Byeongwook Kim Parichay Kapoor Baeseong Park Gu-Yeon Wei MQ 21 48 0 24 May 2019
How Can We Be So Dense? The Benefits of Using Highly Sparse Representations Subutai Ahmad Luiz Scheinkman 33 96 0 27 Mar 2019
Convolution with even-sized kernels and symmetric padding Shuang Wu Guanrui Wang Pei Tang F. Chen Luping Shi 22 68 0 20 Mar 2019
A Brain-inspired Algorithm for Training Highly Sparse Neural Networks Zahra Atashgahi Joost Pieterse Shiwei Liu Decebal Constantin Mocanu Raymond N. J. Veldhuis Mykola Pechenizkiy 35 15 0 17 Mar 2019
Regularity Normalization: Neuroscience-Inspired Unsupervised Attention across Neural Network Layers Baihan Lin 19 2 0 27 Feb 2019
Parameter Efficient Training of Deep Convolutional Neural Networks by Dynamic Sparse Reparameterization Hesham Mostafa Xin Wang 37 307 0 15 Feb 2019
Intrinsically Sparse Long Short-Term Memory Networks Shiwei Liu Decebal Constantin Mocanu Mykola Pechenizkiy 30 9 0 26 Jan 2019
A Theoretical Analysis of Deep Q-Learning Jianqing Fan Zhuoran Yang Yuchen Xie Zhaoran Wang 23 596 0 01 Jan 2019
Neural Rejuvenation: Improving Deep Network Training by Enhancing Computational Resource Utilization Siyuan Qiao Zhe-nan Lin Jianming Zhang Alan Yuille 13 23 0 02 Dec 2018
Structured Pruning of Neural Networks with Budget-Aware Regularization Carl Lemaire Andrew Achkar Pierre-Marc Jodoin 27 92 0 23 Nov 2018
Rethinking the Value of Network Pruning Zhuang Liu Mingjie Sun Tinghui Zhou Gao Huang Trevor Darrell 10 1,453 0 11 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 29 31 0 10 Oct 2018
To compress or not to compress: Understanding the Interactions between Adversarial Attacks and Neural Network Compression Yiren Zhao Ilia Shumailov Robert D. Mullins Ross J. Anderson AAML 11 43 0 29 Sep 2018
Learning Representations for Neural Network-Based Classification Using the Information Bottleneck Principle Rana Ali Amjad Bernhard C. Geiger 35 196 0 27 Feb 2018
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 287 9,156 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 7,639 0 03 Jul 2012