DSD: Dense-Sparse-Dense Training for Deep Neural Networks

2 code implementations • 15 Jul 2016 • Song Han, Jeff Pool, Sharan Narang, Huizi Mao, Enhao Gong, Shijian Tang, Erich Elsen, Peter Vajda, Manohar Paluri, John Tran, Bryan Catanzaro, William J. Dally

We propose DSD, a dense-sparse-dense training flow, for regularizing deep neural networks and achieving better optimization performance.

8k Caption Generation +3

Learning both Weights and Connections for Efficient Neural Network

no code implementations • NeurIPS 2015 • Song Han, Jeff Pool, John Tran, William Dally

On the ImageNet dataset, our method reduced the number of parameters of AlexNet by a factor of 9×, from 61 million to 6. 7 million, without incurring accuracy loss.

Efficient Neural Network

Learning both Weights and Connections for Efficient Neural Networks

7 code implementations • NeurIPS 2015 • Song Han, Jeff Pool, John Tran, William J. Dally

On the ImageNet dataset, our method reduced the number of parameters of AlexNet by a factor of 9x, from 61 million to 6. 7 million, without incurring accuracy loss.

cuDNN: Efficient Primitives for Deep Learning

3 code implementations • 3 Oct 2014 • Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John Tran, Bryan Catanzaro, Evan Shelhamer

To address this problem, we have created a library similar in intent to BLAS, with optimized routines for deep learning workloads.

Parallel Support Vector Machines in Practice

no code implementations • 3 Apr 2014 • Stephen Tyree, Jacob R. Gardner, Kilian Q. Weinberger, Kunal Agrawal, John Tran

In particular, we provide the first comparison of algorithms with explicit and implicit parallelization.