14 citations · 21 across the 6 of their papers we have counts for
6 papers
Fast FixMatch: Faster Semi-Supervised Learning with Curriculum Batch Size
John Chen, Chen Dun, Anastasios Kyrillidis
Advances in Semi-Supervised Learning (SSL) have almost entirely closed the gap between SSL and Supervised Learning at a fraction of the number of labels. However, recent performanc…
LOFT: Finding Lottery Tickets through Filter-wise Training
Qihan Wang, Chen Dun, Fangshuo Liao +2
Recent work on the Lottery Ticket Hypothesis (LTH) shows that there exist ``\textit{winning tickets}'' in large neural networks. These tickets represent ``sparse'' versions of the…
Federated Multiple Label Hashing (FedMLH): Communication Efficient Federated Learning on Extreme Classification Tasks
Zhenwei Dai, Chen Dun, Yuxin Tang +2
Federated learning enables many local devices to train a deep learning model jointly without sharing the local data. Currently, most of federated training schemes learns a global m…
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…
Distributed Learning of Deep Neural Networks using Independent Subnet Training
Binhang Yuan, Cameron R. Wolfe, Chen Dun +3
Distributed machine learning (ML) can bring more computational resources to bear than single-machine learning, thus enabling reductions in training time. Distributed learning parti…