A Bregman Learning Framework for Sparse Neural Networks

A Bregman Learning Framework for Sparse Neural Networks

10 May 2021

Daniel Tenbrinck

Papers citing "A Bregman Learning Framework for Sparse Neural Networks"

10 / 10 papers shown

Title
Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks Torsten Hoefler Dan Alistarh Tal Ben-Nun Nikoli Dryden Alexandra Peste MQ 243 703 0 31 Jan 2021
Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models Lasse F. Wolff Anthony Benjamin Kanding Raghavendra Selvan HAI 38 305 0 06 Jul 2020
Rigging the Lottery: Making All Tickets Winners Utku Evci Trevor Gale Jacob Menick Pablo Samuel Castro Erich Elsen 129 592 0 25 Nov 2019
Sparse Networks from Scratch: Faster Training without Losing Performance Tim Dettmers Luke Zettlemoyer 84 337 0 10 Jul 2019
Stochastic Gradient Descent for Nonconvex Learning without Bounded Gradient Assumptions Yunwen Lei Ting Hu Guiying Li K. Tang MLT 51 116 0 03 Feb 2019
To prune, or not to prune: exploring the efficacy of pruning for model compression Michael Zhu Suyog Gupta 130 1,262 0 05 Oct 2017
The Expressive Power of Neural Networks: A View from the Width Zhou Lu Hongming Pu Feicheng Wang Zhiqiang Hu Liwei Wang 65 886 0 08 Sep 2017
Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science Decebal Constantin Mocanu Elena Mocanu Peter Stone Phuong H. Nguyen M. Gibescu A. Liotta 105 619 0 15 Jul 2017
Learning both Weights and Connections for Efficient Neural Networks Song Han Jeff Pool J. Tran W. Dally CVBM 224 6,628 0 08 Jun 2015
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification Kaiming He Xinming Zhang Shaoqing Ren Jian Sun VLM 166 18,534 0 06 Feb 2015