4 citations · 9 across the 5 of their papers we have counts for
5 papers
Instant Soup: Cheap Pruning Ensembles in A Single Pass Can Draw Lottery Tickets from Large Models
Ajay Jaiswal, Shiwei Liu, Tianlong Chen +2
Large pre-trained transformers have been receiving explosive attention in the past few years, due to their wide adaptability for numerous downstream applications via fine-tuning, b…
Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate Communication
Ajay Jaiswal, Shiwei Liu, Tianlong Chen +2
Graphs are omnipresent and GNNs are a powerful family of neural networks for learning over graphs. Despite their popularity, scaling GNNs either by deepening or widening suffers fr…
Old can be Gold: Better Gradient Flow can Make Vanilla-GCNs Great Again
Ajay Jaiswal, Peihao Wang, Tianlong Chen +3
Despite the enormous success of Graph Convolutional Networks (GCNs) in modeling graph-structured data, most of the current GCNs are shallow due to the notoriously challenging probl…
Training Your Sparse Neural Network Better with Any Mask
Ajay Jaiswal, Haoyu Ma, Tianlong Chen +2
Pruning large neural networks to create high-quality, independently trainable sparse masks, which can maintain similar performance to their dense counterparts, is very desirable du…
Spending Your Winning Lottery Better After Drawing It
Ajay Kumar Jaiswal, Haoyu Ma, Tianlong Chen +2
Lottery Ticket Hypothesis (LTH) suggests that a dense neural network contains a sparse sub-network that can match the performance of the original dense network when trained in isol…