Rethinking Weight Decay For Efficient Neural Network Pruning

Rethinking Weight Decay For Efficient Neural Network Pruning

20 November 2020

Vincent Gripon

Mathieu Léonardon

Papers citing "Rethinking Weight Decay For Efficient Neural Network Pruning"

10 / 60 papers shown

Title
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding Song Han Huizi Mao W. Dally 3DGS 239 8,821 0 01 Oct 2015
Data-free parameter pruning for Deep Neural Networks Suraj Srinivas R. Venkatesh Babu 3DPC 70 547 0 22 Jul 2015
Learning both Weights and Connections for Efficient Neural Networks Song Han Jeff Pool J. Tran W. Dally CVBM 294 6,660 0 08 Jun 2015
Distilling the Knowledge in a Neural Network Geoffrey E. Hinton Oriol Vinyals J. Dean FedML 316 19,609 0 09 Mar 2015
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Sergey Ioffe Christian Szegedy OOD 428 43,234 0 11 Feb 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 1.5K 149,842 0 22 Dec 2014
Going Deeper with Convolutions Christian Szegedy Wei Liu Yangqing Jia P. Sermanet Scott E. Reed Dragomir Anguelov D. Erhan Vincent Vanhoucke Andrew Rabinovich 409 43,589 0 17 Sep 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan Andrew Zisserman FAtt MDE 1.4K 100,213 0 04 Sep 2014
Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation Emily L. Denton Wojciech Zaremba Joan Bruna Yann LeCun Rob Fergus FAtt 165 1,688 0 02 Apr 2014
Network In Network Min Lin Qiang Chen Shuicheng Yan 279 6,274 0 16 Dec 2013