9 citations · 11 across the 7 of their papers we have counts for
8 papers
Enhancing Noise Robustness of Parkinson's Disease Telemonitoring via Contrastive Feature Augmentation
Ziming Tang, Chengbin Hou, Tianyu Zhang +3
Parkinson's disease (PD) is one of the most common neurodegenerative disorder. PD telemonitoring emerges as a novel assessment modality enabling self-administered at-home tests of…
MoESD: Unveil Speculative Decoding's Potential for Accelerating Sparse MoE
Zongle Huang, Lei Zhu, Zongyuan Zhan +5
Large Language Models (LLMs) have achieved remarkable success across many applications, with Mixture of Experts (MoE) models demonstrating great potential. Compared to traditional…
Faster and Better LLMs via Latency-Aware Test-Time Scaling
Zili Wang, Tianyu Zhang, Haoli Bai +5
Test-Time Scaling (TTS) has proven effective in improving the performance of Large Language Models (LLMs) during inference. However, existing research has overlooked the efficiency…
Node Importance Estimation Leveraging LLMs for Semantic Augmentation in Knowledge Graphs
Xinyu Lin, Tianyu Zhang, Chengbin Hou +3
Node Importance Estimation (NIE) is a task that quantifies the importance of node in a graph. Recent research has investigated to exploit various information from Knowledge Graphs…
Parse Trees Guided LLM Prompt Compression
Wenhao Mao, Chengbin Hou, Tianyu Zhang +3
Offering rich contexts to Large Language Models (LLMs) has shown to boost the performance in various tasks, but the resulting longer prompt would increase the computational cost an…
Molecular Graph Representation Learning Integrating Large Language Models with Domain-specific Small Models
Tianyu Zhang, Yuxiang Ren, Chengbin Hou +2
Molecular property prediction is a crucial foundation for drug discovery. In recent years, pre-trained deep learning models have been widely applied to this task. Some approaches t…
Label Informed Contrastive Pretraining for Node Importance Estimation on Knowledge Graphs
Tianyu Zhang, Chengbin Hou, Rui Jiang +4
Node Importance Estimation (NIE) is a task of inferring importance scores of the nodes in a graph. Due to the availability of richer data and knowledge, recent research interests o…
CGCL: Collaborative Graph Contrastive Learning without Handcrafted Graph Data Augmentations
Tianyu Zhang, Yuxiang Ren, Wenzheng Feng +2
Unsupervised graph representation learning is a non-trivial topic. The success of contrastive methods in the unsupervised representation learning on structured data inspires simila…