297 citations · 308 across the 8 of their papers we have counts for
3 papers · 1 filter
MF-QAT: Multi-Format Quantization-Aware Training for Elastic Inference
Zifei Xu, Sayeh Sharify, Hesham Mostafa
Quantization-aware training (QAT) is typically performed for a single target numeric format, while practical deployments often need to choose numerical precision at inference time…
Distributed Training of Large Graph Neural Networks with Variable Communication Rates
Juan Cervino, Md Asadullah Turja, Hesham Mostafa +2
Training Graph Neural Networks (GNNs) on large graphs presents unique challenges due to the large memory and computing requirements. Distributed GNN training, where the graph is pa…
Fully-inductive Node Classification on Arbitrary Graphs
Jianan Zhao, Zhaocheng Zhu, Mikhail Galkin +3
One fundamental challenge in graph machine learning is generalizing to new graphs. Many existing methods following the inductive setup can generalize to test graphs with new struct…