21 citations · 63 across the 22 of their papers we have counts for
7 papers · 1 filter
Distill on a Diet: Efficient Knowledge Distillation via Learnable Data Pruning
Yifan Wu, Yiqi Wang, Xichen Ye +5
Knowledge Distillation (KD) is widely used to obtain compact models for efficient inference in resource-constrained environments. Yet the computational overhead of the distillation…
Beyond Parameter Finetuning: Test-Time Representation Refinement for Node Classification
Jiaxin Zhang, Yiqi Wang, Siwei Wang +4
Graph Neural Networks frequently exhibit significant performance degradation in the out-of-distribution test scenario. While test-time training (TTT) offers a promising solution, e…
Investigating Data Pruning for Pretraining Biological Foundation Models at Scale
Yifan Wu, Jiyue Jiang, Xichen Ye +9
Biological foundation models (BioFMs), pretrained on large-scale biological sequences, have recently shown strong potential in providing meaningful representations for diverse down…
Test-Time Training on Graphs with Large Language Models (LLMs)
Jiaxin Zhang, Yiqi Wang, Xihong Yang +6
Graph Neural Networks have demonstrated great success in various fields of multimedia. However, the distribution shift between the training and test data challenges the effectivene…
Cumulative Distribution Function based General Temporal Point Processes
Maolin Wang, Yu Pan, Zenglin Xu +6
Temporal Point Processes (TPPs) hold a pivotal role in modeling event sequences across diverse domains, including social networking and e-commerce, and have significantly contribut…
Mixed Graph Contrastive Network for Semi-Supervised Node Classification
Xihong Yang, Yiqi Wang, Yue Liu +5
Graph Neural Networks (GNNs) have achieved promising performance in semi-supervised node classification in recent years. However, the problem of insufficient supervision, together…
Gophormer: Ego-Graph Transformer for Node Classification
Jianan Zhao, Chaozhuo Li, Qianlong Wen +5
Transformers have achieved remarkable performance in a myriad of fields including natural language processing and computer vision. However, when it comes to the graph mining area,…