150 citations · 745 across the 95 of their papers we have counts for
17 papers · 1 filter
Learning Propagation Geometry from Message-Passing Feedback
Yingxu Wang, Kunyu Zhang, Xinwang Liu +4
Learning local geometry enables graph neural networks (GNNs) to adapt how they compare and integrate neighborhood information. However, estimating geometry from aggregated represen…
SeOPD: Self-Evolving LLMs via Online Policy Distillation from Self-Generated Chain-of-Thought
Xiaoshu Chen, Xiangyu Wong, Sihang Zhou +2
Recent advances in online policy self-distillation (OPSD) have demonstrated that large language models (LLMs) can improve their capabilities by leveraging external privileged infor…
MODAL: Multi-Modal Object Re-ID via Model-Driven Sparse Decoupling and Text-Image Differential Filtering
Chengbo Huang, Jun-Jie Huang, Long Lan +5
Multi-modal object re-identification (Re-ID) aims to facilitate cross-camera object retrieval in complex environments by leveraging complementary information from visual (e.g., RGB…
SpaCellAgent: A Self-Evolving LLM-Based Multi-Agent Framework for Trajectory Analysis
Songhan Wang, Haoang Chi, He Li +6
Spatial and Single-cell transcriptomics are transformative in deciphering cellular dynamics. As the fundamental paradigm for reconstructing cell developmental paths, trajectory inf…
JuZhou 1.0 Technical Report: The First Edge-Native Text-to-Image Foundation Model Trained Entirely on China-Developed AI Accelerators
Ce Chen, Congrui Wang, Yonglin Li +23
Text-to-image (T2I) diffusion models typically require substantial computational resources and cloud infrastructure, posing significant challenges for edge deployment in terms of l…
Beyond Independent Manipulation: Individual Fairness-aware Strategic Classification with Peer Imitation
Xinpeng Lv, Chunyuan Zheng, Yunxin Mao +9
Strategic classification (SC) investigates scenarios where agents manipulate their features to obtain favorable decisions from predictive models. Existing fairness-aware SC approac…
Safe-Subspace Pseudo-Label Refinement for Source-Free Graph Domain Adaptation
Yingxu Wang, Xinwang Liu, Siyang Gao +1
Source-free graph domain adaptation (SF-GDA) aims to adapt source-trained graph models to unlabeled target graphs when source graphs are no longer accessible. A central obstacle is…
Learning Disentangled Representations for Generalized Multi-view Clustering
Xin Zou, Ruimeng Liu, Chang Tang +4
Multi-View Clustering (MVC) has gained significant attention for its ability to leverage complementary information across diverse views. However, existing deep MVC methods often st…
Combined Dictionary Unfolding Network with Gradient-Adaptive Fidelity for Transferable Multi-Source Fusion
Ge Luo, Jun-Jie Huang, Qi Yu +6
Deep Unfolding Network-based methods have emerged as effective solutions for multi-source image fusion by combining model-driven iterative optimization with data-driven deep learni…
Decoupled Travel Planning with Behavior Forest
Duanyang Yuan, Sihang Zhou, Yanning Hou +7
Behavior sequences, composed of executable steps, serve as the operational foundation for multi-constraint planning problems such as travel planning. In such tasks, each planning s…
Neural Network Optimization Reimagined: Decoupled Techniques for Scratch and Fine-Tuning
Xin Ning, Qiankun Li, Xiaolong Huang +5
With the accumulation of resources in the era of big data and the rise of pre-trained models in deep learning, optimizing neural networks for various tasks often involves different…
AtomicRAG: Atom-Entity Graphs for Retrieval-Augmented Generation
Yanning Hou, Duanyang Yuan, Sihang Zhou +5
Recent GraphRAG methods integrate graph structures into text indexing and retrieval, using knowledge graph triples to connect text chunks, thereby improving retrieval coverage and…
Beyond the Academic Monoculture: A Unified Framework and Industrial Perspective for Attributed Graph Clustering
Yunhui Liu, Yue Liu, Yongchao Liu +4
Attributed Graph Clustering (AGC) is a fundamental unsupervised task that partitions nodes into cohesive groups by jointly modeling structural topology and node attributes. While t…
DisRFM: Polar Riemannian Flow Matching for Structure-Preserving Graph Domain Adaptation
Yingxu Wang, Xinwang Liu, Mengzhu Wang +2
Graph Domain Adaptation (GDA) aims to transfer graph classifiers across domains with both semantic and topological shifts. Existing Euclidean adversarial methods face two challenge…
ImgCoT: Compressing Long Chain of Thought into Compact Visual Tokens for Efficient Reasoning of Large Language Model
Xiaoshu Chen, Sihang Zhou, Ke Liang +2
Compressing long chains of thought (CoT) into compact latent tokens is crucial for efficient reasoning with large language models (LLMs). Recent studies employ autoencoders to achi…
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…
Deep Temporal Graph Clustering: A Comprehensive Benchmark and Datasets
Meng Liu, Ke Liang, Siwei Wang +3
Temporal Graph Clustering (TGC) is a new task with little attention, focusing on node clustering in temporal graphs. Compared with existing static graph clustering, it can find the…