Test-time adaptation (TTA) is a strategy that leverages unlabeled test data to adjust a pretrained model, improving robustness against distribution shifts. Recent advances in this research area are mostly driven by heuristic or empirical approaches with limited theoretical guidance. In particular, there is little theor...
Song-Ming Zhang, Yu-Xiao Luo, Si-Ran Chen et al.· IEEE Transactions on Neural...· 0 citations
Attribute-missing deep graph clustering (DGC), which groups graph data containing samples with missing attributes, holds great potential to address the challenges of label scarcity and data absence in real-world scenarios. Among existing efforts, imputation-driven solutions (e.g., AMGC) employ the idea of classical imp...
Wen-Xuan Tu, Jie-Ren Cheng, Ren-Xiang Guan et al.· IEEE Transactions on Pattern...· 0 citations
Detecting hidden confounding is crucial for reliable causal analysis from observational data, directly determining which downstream causal inference method to be deployed. Inspired by the theory of higher-order regression, recent sample-efficient hypothesis testing strategies overcome the restrictive requirement of mul...
Yi-Kai Chen, Hao-Tian Wang, Yunxin Mao et al.· Proceedings of the Thirty-Fi...· 0 citations
This work introduces DGCBench, the first comprehensive and unified benchmark for DGC methods, and develops PyDGC, an open-source Python library that standardizes the DGC training and evaluation paradigm.
Ben-Yu Wu, Yue Liu, Q. Tan et al.· Neural Information Processin...· 5 citations
The results position JuZhou 1.0 as a practical approach to mobile text-to-image generation and provide a concrete reference for Chinese-native generation, domestic-compute training, and fully offline on-device deployment after one-time installation.
Ce Chen, Cong-Rui Wang, Yong-Lin Li et al.· 0 citations
This work proposes Self-Evolving Online Policy Distillation (SeOPD), which enables LLMs to distill and internalize information generated by their own chain of thought (CoT), thereby improving both non-thinking and deep-thinking capabilities.
Xiao-Shu Chen, Si-Hang Zhou, K. Liang et al.· 0 citations
MODAL is a novel multi-modal object re-identification framework, grounded in coupled sparse coding theory and differential suppression principles, that achieves state-of-the-art performance with superior transparency.
Cheng Huang, Jun-Jie Huang, Long Lan et al.· 0 citations
Multi-view Discrete Optimal Transport is instantiated as a cluster-level transport matching framework that first adopts semi-discrete OT to learn a global OT barycenter via aggregating semantic-geometric information from all views, and then transports samples to barycenter-anchored consensus clusters under consistency...
Yuzhuo Dai, Si-Wei Wang, Zhi-Bin Dong et al.· IEEE Transactions on Pattern...· 0 citations
A subgraph-aware semantic expansion module that coordinates $k$-hop neighborhood information with LLM-generated descriptions to alleviate structural sparsity and a contrastive ontology matching mechanism resolves structural inconsistencies by computing adaptive similarity metrics between ontology embeddings and subgrap...
Hao Li, K. Liang, Lingyuan Meng et al.· IEEE Transactions on Pattern...· 0 citations
This work is among the first to introduce and exploit the idea of prompt learning to align HIN features and textual features, and is composed of a text encoder and a graph encoder and uses contrastive learning to align and fuse the graph–text pair.
Yang Fang, Xiang Zhao, Dao-Jian Zeng et al.· ACM Transactions on Informat...· 0 citations
A unified paradigm based on Cross-View Semantic Propagation (CVSP), a plug-and-play framework that effectively improves anchor quality and generalization capability when integrated with various existing methods is proposed.
Su-Yuan Liu, Si-Wei Wang, Ke Liang et al.· IEEE Transactions on Pattern...· 1 citation
This work proposes SpaCellAgent, an autonomous large language model (LLM) multi-agent framework that automates end-to-end spatiotemporal analysis and narrative generation and establishes a scalable, agent-driven paradigm for computational biology.
Songhan Wang, Haoang Chi, He Li et al.· Proceedings of the 32nd ACM...· 1 citation
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
