150 citations · 535 across the 29 of their papers we have counts for
30 papers
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…
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…
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…
Putting on the Thinking Hats: A Survey on Chain of Thought Fine-tuning from the Perspective of Human Reasoning Mechanism
Xiaoshu Chen, Sihang Zhou, Ke Liang +5
Chain of thought (CoT) fine-tuning aims to endow large language models (LLMs) with reasoning capabilities by training them on curated reasoning traces. It leverages both supervised…
Skip-Thinking: Chunk-wise Chain-of-Thought Distillation Enable Smaller Language Models to Reason Better and Faster
Xiao Chen, Sihang Zhou, Ke Liang +2
Chain-of-thought (CoT) distillation allows a large language model (LLM) to guide a small language model (SLM) in reasoning tasks. Existing methods train the SLM to learn the long r…
Knowledge Graph Completion with Relation-Aware Anchor Enhancement
Duanyang Yuan, Sihang Zhou, Xiaoshu Chen +4
Text-based knowledge graph completion methods take advantage of pre-trained language models (PLM) to enhance intrinsic semantic connections of raw triplets with detailed text descr…
Let Synthetic Data Shine: Domain Reassembly and Soft-Fusion for Single Domain Generalization
Hao Li, Yubin Xiao, Ke Liang +4
Single Domain Generalization (SDG) aims to train models that maintain consistent performance across diverse scenarios using data from a single source. While latent diffusion models…
Object Style Diffusion for Generalized Object Detection in Urban Scene
Hao Li, Xiangyuan Yang, Mengzhu Wang +4
Object detection is a critical task in computer vision, with applications in various domains such as autonomous driving and urban scene monitoring. However, deep learning-based app…
Distilling Reasoning Ability from Large Language Models with Adaptive Thinking
Xiaoshu Chen, Sihang Zhou, Ke Liang +1
Chain of thought finetuning (cot-finetuning) aims to endow small language models (SLM) with reasoning ability to improve their performance towards specific tasks by allowing them t…
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…
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…
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…
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…