2 citations · 5 across the 16 of their papers we have counts for
4 papers · 1 filter
Towards Dynamic Message Passing on Graphs
Junshu Sun, Chenxue Yang, Xiangyang Ji +2
Message passing plays a vital role in graph neural networks (GNNs) for effective feature learning. However, the over-reliance on input topology diminishes the efficacy of message p…
Expanding Sparse Tuning for Low Memory Usage
Shufan Shen, Junshu Sun, Xiangyang Ji +2
Parameter-efficient fine-tuning (PEFT) is an effective method for adapting pre-trained vision models to downstream tasks by tuning a small subset of parameters. Among PEFT methods,…
Scalable Graph Compressed Convolutions
Junshu Sun, Shuhui Wang, Chenxue Yang +1
Designing effective graph neural networks (GNNs) with message passing has two fundamental challenges, i.e., determining optimal message-passing pathways and designing local aggrega…
Pick-and-Draw: Training-free Semantic Guidance for Text-to-Image Personalization
Henglei Lv, Jiayu Xiao, Liang Li +1
Diffusion-based text-to-image personalization have achieved great success in generating subjects specified by users among various contexts. Even though, existing finetuning-based m…