59 citations · 185 across the 20 of their papers we have counts for
5 papers · 1 filter
SparseLoRA: Accelerating LLM Fine-Tuning with Contextual Sparsity
Samir Khaki, Xiuyu Li, Junxian Guo +7
Fine-tuning LLMs is both computationally and memory-intensive. While parameter-efficient fine-tuning methods, such as QLoRA and DoRA, reduce the number of trainable parameters and…
TorchSparse: Efficient Point Cloud Inference Engine
Haotian Tang, Zhijian Liu, Xiuyu Li +2
Deep learning on point clouds has received increased attention thanks to its wide applications in AR/VR and autonomous driving. These applications require low latency and high accu…
GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networks
Chenhui Deng, Xiuyu Li, Zhuo Feng +1
Graph neural networks (GNNs) have been increasingly deployed in various applications that involve learning on non-Euclidean data. However, recent studies show that GNNs are vulnera…
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods
Derek Lim, Felix Hohne, Xiuyu Li +4
Many widely used datasets for graph machine learning tasks have generally been homophilous, where nodes with similar labels connect to each other. Recently, new Graph Neural Networ…
New Benchmarks for Learning on Non-Homophilous Graphs
Derek Lim, Xiuyu Li, Felix Hohne +1
Much data with graph structures satisfy the principle of homophily, meaning that connected nodes tend to be similar with respect to a specific attribute. As such, ubiquitous datase…