60 citations · 97 across the 6 of their papers we have counts for
7 papers
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…
A Deployment-Friendly Foundational Framework for Efficient Computational Pathology
Yu Cai, Cheng Jin, Zhengyu Zhang +25
Pathology foundation models (PFMs) generalize well across computational pathology tasks but remain costly for gigapixel whole-slide image analysis. Here, we present LitePath, a dep…
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…
Learning to See Through a Baby's Eyes: Early Visual Diets Enable Robust Visual Intelligence in Humans and Machines
Yusen Cai, Qing Lin, Bhargava Satya Nunna +1
Newborns perceive the world with low-acuity, color-degraded, and temporally continuous vision, which gradually sharpens as infants develop. To explore the ecological advantages of…
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/…
Visual Mamba: A Survey and New Outlooks
Rui Xu, Shu Yang, Yihui Wang +3
Mamba, a recent selective structured state space model, excels in long sequence modeling, which is vital in the large model era. Long sequence modeling poses significant challenges…
Dual-distribution discrepancy with self-supervised refinement for anomaly detection in medical images
Yu Cai, Hao Chen, Xin Yang +2
Medical anomaly detection is a crucial yet challenging task aimed at recognizing abnormal images to assist in diagnosis. Due to the high-cost annotations of abnormal images, most m…