2 citations · 6 across the 12 of their papers we have counts for
4 papers · 1 filter
LLMEasyQuant: Scalable Quantization for Parallel and Distributed LLM Inference
Dong Liu, Yanxuan Yu
As large language models (LLMs) grow in size and deployment scale, quantization has become an essential technique for reducing memory footprint and improving inference efficiency.…
MT2ST: Adaptive Multi-Task to Single-Task Learning
Dong Liu, Yanxuan Yu
Efficient machine learning (ML) has become increasingly important as models grow larger and data volumes expand. In this work, we address the trade-off between generalization in mu…
GraphSnapShot: Caching Local Structure for Fast Graph Learning
Dong Liu, Roger Waleffe, Meng Jiang +1
In our recent research, we have developed a framework called GraphSnapShot, which has been proven an useful tool for graph learning acceleration. GraphSnapShot is a framework for f…
Efficient Graph Optimization via Distance-Aware Graph Representation Learning
Dong Liu, Yanxuan Yu
We propose an efficient framework that integrates distance-aware multi-hop message passing with dynamic topology refinement. Unlike standard GNNs that rely on shallow, fixed-hop ag…