150 citations · 382 across the 30 of their papers we have counts for
33 papers
CG-DMER: Hybrid Contrastive-Generative Framework for Disentangled Multimodal ECG Representation Learning
Ziwei Niu, Hao Sun, Shujun Bian +4
Accurate interpretation of electrocardiogram (ECG) signals is crucial for diagnosing cardiovascular diseases. Recent multimodal approaches that integrate ECGs with accompanying cli…
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…
Generalized Deep Multi-view Clustering via Causal Learning with Partially Aligned Cross-view Correspondence
Xihong Yang, Siwei Wang, Jiaqi Jin +6
Multi-view clustering (MVC) aims to explore the common clustering structure across multiple views. Many existing MVC methods heavily rely on the assumption of view consistency, whe…
CTR-Guided Generative Query Suggestion in Conversational Search
Erxue Min, Hsiu-Yuan Huang, Xihong Yang +7
Generating effective query suggestions in conversational search requires aligning model outputs with user preferences, which is challenging due to sparse and noisy click signals. W…
Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios
Xihong Yang, Siwei Wang, Fangdi Wang +6
Leveraging the powerful representation learning capabilities, deep multi-view clustering methods have demonstrated reliable performance by effectively integrating multi-source info…
Imputation-free and Alignment-free: Incomplete Multi-view Clustering Driven by Consensus Semantic Learning
Yuzhuo Dai, Jiaqi Jin, Zhibin Dong +6
In incomplete multi-view clustering (IMVC), missing data induce prototype shifts within views and semantic inconsistencies across views. A feasible solution is to explore cross-vie…
From Prompting to Alignment: A Generative Framework for Query Recommendation
Erxue Min, Hsiu-Yuan Huang, Xihong Yang +7
In modern search systems, search engines often suggest relevant queries to users through various panels or components, helping refine their information needs. Traditionally, these…
Deep Incomplete Multi-view Clustering with Distribution Dual-Consistency Recovery Guidance
Jiaqi Jin, Siwei Wang, Zhibin Dong +4
Multi-view clustering leverages complementary representations from diverse sources to enhance performance. However, real-world data often suffer incomplete cases due to factors lik…
MedAgent-Pro: Towards Evidence-based Multi-modal Medical Diagnosis via Reasoning Agentic Workflow
Ziyue Wang, Junde Wu, Linghan Cai +4
In modern medicine, clinical diagnosis relies on the comprehensive analysis of primarily textual and visual data, drawing on medical expertise to ensure systematic and rigorous rea…
Hgformer: Hyperbolic Graph Transformer for Recommendation
Xin Yang, Xingrun Li, Heng Chang +8
The cold start problem is a challenging problem faced by most modern recommender systems. By leveraging knowledge from other domains, cross-domain recommendation can be an effectiv…
DaRec: A Disentangled Alignment Framework for Large Language Model and Recommender System
Xihong Yang, Heming Jing, Zixing Zhang +10
Benefiting from the strong reasoning capabilities, Large language models (LLMs) have demonstrated remarkable performance in recommender systems. Various efforts have been made to d…
Dual Test-time Training for Out-of-distribution Recommender System
Xihong Yang, Yiqi Wang, Jin Chen +5
Deep learning has been widely applied in recommender systems, which has achieved revolutionary progress recently. However, most existing learning-based methods assume that the user…
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…
Boosting the Power of Small Multimodal Reasoning Models to Match Larger Models with Self-Consistency Training
Cheng Tan, Jingxuan Wei, Zhangyang Gao +5
Multimodal reasoning is a challenging task that requires models to reason across multiple modalities to answer questions. Existing approaches have made progress by incorporating la…
Asymmetric double-winged multi-view clustering network for exploring Diverse and Consistent Information
Qun Zheng, Xihong Yang, Siwei Wang +2
In unsupervised scenarios, deep contrastive multi-view clustering (DCMVC) is becoming a hot research spot, which aims to mine the potential relationships between different views. M…
Efficient Multi-View Graph Clustering with Local and Global Structure Preservation
Yi Wen, Suyuan Liu, Xinhang Wan +5
Anchor-based multi-view graph clustering (AMVGC) has received abundant attention owing to its high efficiency and the capability to capture complementary structural information acr…
Reinforcement Graph Clustering with Unknown Cluster Number
Yue Liu, Ke Liang, Jun Xia +5
Deep graph clustering, which aims to group nodes into disjoint clusters by neural networks in an unsupervised manner, has attracted great attention in recent years. Although the pe…
DealMVC: Dual Contrastive Calibration for Multi-view Clustering
Xihong Yang, Jiaqi Jin, Siwei Wang +7
Benefiting from the strong view-consistent information mining capacity, multi-view contrastive clustering has attracted plenty of attention in recent years. However, we observe the…
CONVERT:Contrastive Graph Clustering with Reliable Augmentation
Xihong Yang, Cheng Tan, Yue Liu +7
Contrastive graph node clustering via learnable data augmentation is a hot research spot in the field of unsupervised graph learning. The existing methods learn the sampling distri…
Dink-Net: Neural Clustering on Large Graphs
Yue Liu, Ke Liang, Jun Xia +4
Deep graph clustering, which aims to group the nodes of a graph into disjoint clusters with deep neural networks, has achieved promising progress in recent years. However, the exis…
SARF: Aliasing Relation Assisted Self-Supervised Learning for Few-shot Relation Reasoning
Lingyuan Meng, Ke Liang, Bin Xiao +5
Few-shot relation reasoning on knowledge graphs (FS-KGR) aims to infer long-tail data-poor relations, which has drawn increasing attention these years due to its practicalities. Th…
Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignment
Meng Liu, Ke Liang, Yawei Zhao +5
Temporal graph learning aims to generate high-quality representations for graph-based tasks with dynamic information, which has recently garnered increasing attention. In contrast…
Hard Sample Aware Network for Contrastive Deep Graph Clustering
Yue Liu, Xihong Yang, Sihang Zhou +7
Contrastive deep graph clustering, which aims to divide nodes into disjoint groups via contrastive mechanisms, is a challenging research spot. Among the recent works, hard sample m…
Cluster-guided Contrastive Graph Clustering Network
Xihong Yang, Yue Liu, Sihang Zhou +6
Benefiting from the intrinsic supervision information exploitation capability, contrastive learning has achieved promising performance in the field of deep graph clustering recentl…