150 citations · 745 across the 95 of their papers we have counts for
32 papers · 1 filter
Single-Cell Deep Clustering Method Assisted by Exogenous Gene Information: A Novel Approach to Identifying Cell Types
Dayu Hu, Ke Liang, Hao Yu +1
In recent years, the field of single-cell data analysis has seen a marked advancement in the development of clustering methods. Despite advancements, most of these algorithms still…
Single-cell Multi-view Clustering via Community Detection with Unknown Number of Clusters
Dayu Hu, Zhibin Dong, Ke Liang +3
Single-cell multi-view clustering enables the exploration of cellular heterogeneity within the same cell from different views. Despite the development of several multi-view cluster…
MS-Former: Memory-Supported Transformer for Weakly Supervised Change Detection with Patch-Level Annotations
Zhenglai Li, Chang Tang, Xinwang Liu +3
Fully supervised change detection methods have achieved significant advancements in performance, yet they depend severely on acquiring costly pixel-level labels. Considering that t…
RiskQ: Risk-sensitive Multi-Agent Reinforcement Learning Value Factorization
Siqi Shen, Chennan Ma, Chao Li +5
Multi-agent systems are characterized by environmental uncertainty, varying policies of agents, and partial observability, which result in significant risks. In the context of Mult…
Rethinking SIGN Training: Provable Nonconvex Acceleration without First- and Second-Order Gradient Lipschitz
Tao Sun, Congliang Chen, Peng Qiao +3
Sign-based stochastic methods have gained attention due to their ability to achieve robust performance despite using only the sign information for parameter updates. However, the c…
TMac: Temporal Multi-Modal Graph Learning for Acoustic Event Classification
Meng Liu, Ke Liang, Dayu Hu +6
Audiovisual data is everywhere in this digital age, which raises higher requirements for the deep learning models developed on them. To well handle the information of the multi-mod…
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…
Scalable Incomplete Multi-View Clustering with Structure Alignment
Yi Wen, Siwei Wang, Ke Liang +6
The success of existing multi-view clustering (MVC) relies on the assumption that all views are complete. However, samples are usually partially available due to data corruption or…
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…
Unpaired Multi-View Graph Clustering with Cross-View Structure Matching
Yi Wen, Siwei Wang, Qing Liao +4
Multi-view clustering (MVC), which effectively fuses information from multiple views for better performance, has received increasing attention. Most existing MVC methods assume tha…
Structure Guided Multi-modal Pre-trained Transformer for Knowledge Graph Reasoning
Ke Liang, Sihang Zhou, Yue Liu +3
Multimodal knowledge graphs (MKGs), which intuitively organize information in various modalities, can benefit multiple practical downstream tasks, such as recommendation systems, a…
One-step Multi-view Clustering with Diverse Representation
Xinhang Wan, Jiyuan Liu, Xinwang Liu +5
Multi-view clustering has attracted broad attention due to its capacity to utilize consistent and complementary information among views. Although tremendous progress has been made…
Medical Federated Model with Mixture of Personalized and Sharing Components
Yawei Zhao, Qinghe Liu, Xinwang Liu +1
Although data-driven methods usually have noticeable performance on disease diagnosis and treatment, they are suspected of leakage of privacy due to collecting data for model train…
arXiv4TGC: Large-Scale Datasets for Temporal Graph Clustering
Meng Liu, Ke Liang, Yue Liu +3
Temporal graph clustering (TGC) is a crucial task in temporal graph learning. Its focus is on node clustering on temporal graphs, and it offers greater flexibility for large-scale…
Fast Continual Multi-View Clustering with Incomplete Views
Xinhang Wan, Bin Xiao, Xinwang Liu +3
Multi-view clustering (MVC) has gained broad attention owing to its capacity to exploit consistent and complementary information across views. This paper focuses on a challenging i…
Simultaneous Image-to-Zero and Zero-to-Noise: Diffusion Models with Analytical Image Attenuation
Yuhang Huang, Zheng Qin, Xinwang Liu +1
Recent studies have demonstrated that the forward diffusion process is crucial for the effectiveness of diffusion models in terms of generative quality and sampling efficiency. We…
Object Detection in Hyperspectral Image via Unified Spectral-Spatial Feature Aggregation
Xiao He, Chang Tang, Xinwang Liu +3
Deep learning-based hyperspectral image (HSI) classification and object detection techniques have gained significant attention due to their vital role in image content analysis, in…
GuardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clustering
Hao Yu, Chuan Ma, Meng Liu +5
Federated Learning (FL) offers collaborative model training without data sharing but is vulnerable to backdoor attacks, where poisoned model weights lead to compromised system inte…
Message Intercommunication for Inductive Relation Reasoning
Ke Liang, Lingyuan Meng, Sihang Zhou +5
Inductive relation reasoning for knowledge graphs, aiming to infer missing links between brand-new entities, has drawn increasing attention. The models developed based on Graph Ind…
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…
Deep Temporal Graph Clustering
Meng Liu, Yue Liu, Ke Liang +4
Deep graph clustering has recently received significant attention due to its ability to enhance the representation learning capabilities of models in unsupervised scenarios. Nevert…
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…