14 citations · 44 across the 14 of their papers we have counts for
7 papers · 1 filter
i-SpaSP: Structured Neural Pruning via Sparse Signal Recovery
Cameron R. Wolfe, Anastasios Kyrillidis
We propose a novel, structured pruning algorithm for neural networks -- the iterative, Sparse Structured Pruning algorithm, dubbed as i-SpaSP. Inspired by ideas from sparse signal…
How much pre-training is enough to discover a good subnetwork?
Cameron R. Wolfe, Fangshuo Liao, Qihan Wang +2
Neural network pruning is useful for discovering efficient, high-performing subnetworks within pre-trained, dense network architectures. More often than not, it involves a three-st…
Exceeding the Limits of Visual-Linguistic Multi-Task Learning
Cameron R. Wolfe, Keld T. Lundgaard
By leveraging large amounts of product data collected across hundreds of live e-commerce websites, we construct 1000 unique classification tasks that share similarly-structured inp…
REX: Revisiting Budgeted Training with an Improved Schedule
John Chen, Cameron Wolfe, Anastasios Kyrillidis
Deep learning practitioners often operate on a computational and monetary budget. Thus, it is critical to design optimization algorithms that perform well under any budget. The lin…
Momentum-inspired Low-Rank Coordinate Descent for Diagonally Constrained SDPs
Junhyung Lyle Kim, JA Lara Benitez, Mohammad Taha Toghani +3
We present a novel, practical, and provable approach for solving diagonally constrained semi-definite programming (SDP) problems at scale using accelerated non-convex programming.…
ResIST: Layer-Wise Decomposition of ResNets for Distributed Training
Chen Dun, Cameron R. Wolfe, Christopher M. Jermaine +1
We propose ResIST, a novel distributed training protocol for Residual Networks (ResNets). ResIST randomly decomposes a global ResNet into several shallow sub-ResNets that are train…
GIST: Distributed Training for Large-Scale Graph Convolutional Networks
Cameron R. Wolfe, Jingkang Yang, Arindam Chowdhury +4
The graph convolutional network (GCN) is a go-to solution for machine learning on graphs, but its training is notoriously difficult to scale both in terms of graph size and the num…