1k citations · 1.9k across the 5 of their papers we have counts for
5 papers
AVO: Agentic Variation Operators for Autonomous Evolutionary Search
Terry Chen, Zhifan Ye, Bing Xu +20
Agentic Variation Operators (AVO) are a new family of evolutionary variation operators that replace the fixed mutation, crossover, and hand-designed heuristics of classical evoluti…
DSD: Dense-Sparse-Dense Training for Deep Neural Networks
Song Han, Jeff Pool, Sharan Narang +9
Modern deep neural networks have a large number of parameters, making them very hard to train. We propose DSD, a dense-sparse-dense training flow, for regularizing deep neural netw…
Learning both Weights and Connections for Efficient Neural Networks
Song Han, Jeff Pool, John Tran +1
Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems. Also, conventional networks fix the architecture befor…
cuDNN: Efficient Primitives for Deep Learning
Sharan Chetlur, Cliff Woolley, Philippe Vandermersch +4
We present a library of efficient implementations of deep learning primitives. Deep learning workloads are computationally intensive, and optimizing their kernels is difficult and…
Parallel Support Vector Machines in Practice
Stephen Tyree, Jacob R. Gardner, Kilian Q. Weinberger +2
In this paper, we evaluate the performance of various parallel optimization methods for Kernel Support Vector Machines on multicore CPUs and GPUs. In particular, we provide the fir…