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cs.LG2026
FAST: A Holistic Framework for Optimizing Memory-I/O, Computation, and Sampling in Temporal GNN Training
Yushu Cai, Qingrui Zhu, Lei Liu +3
Temporal Graph Neural Networks (TGNNs) are widely used for learning from dynamic graphs in applications such as recommendation, social network analysis, and traffic forecasting. Ho…
cs.LG2025
Alada: Alternating Adaptation of Momentum Method for Memory-Efficient Matrix Optimization
Xiaoyu He, Yu Cai, Jin Jia +3
This work proposes Alada, an adaptive momentum method for stochastic optimization over large-scale matrices. Alada employs a rank-one factorization approach to estimate the second…
cs.LG2025
FlexQ: Efficient Post-training INT6 Quantization for LLM Serving via Algorithm-System Co-Design
Hao Zhang, Aining Jia, Weifeng Bu +4
Large Language Models (LLMs) demonstrate exceptional performance but entail significant memory and computational costs, restricting their practical deployment. While existing INT4/…