137 citations · 884 across the 51 of their papers we have counts for
Showing 2016Show all
2 papers · 1 filter
cs.LG2016
Training Neural Networks Without Gradients: A Scalable ADMM Approach
Gavin Taylor, Ryan Burmeister, Zheng Xu +3
With the growing importance of large network models and enormous training datasets, GPUs have become increasingly necessary to train neural networks. This is largely because conven…
cs.CV2016
Estimating Sparse Signals with Smooth Support via Convex Programming and Block Sparsity
Sohil Shah, Tom Goldstein, Christoph Studer
Conventional algorithms for sparse signal recovery and sparse representation rely on -norm regularized variational methods. However, when applied to the reconstruction of $\te…