14 citations · 32 across the 23 of their papers we have counts for
24 papers
ProSearch: Benchmarking Multi-Constraint Protocol Retrieval in Experimental Science
Wenliang Liang, Zhiyuan Ning, Haolin Chen +3
In AI for Science scenarios, experimental design and method reproduction require retrieving protocols that satisfy multiple interdependent constraints, yet existing benchmarks lack…
Beyond Paper-to-Paper: Structured Profiling and Rubric Scoring for Paper-Reviewer Matching
Yicheng Pan, Zhiyuan Ning, Ludi Wang +1
As conference submission volumes continue to grow, accurately recommending suitable reviewers has become a challenge. Most existing methods follow a ``Paper-to-Paper'' matching par…
Soft Graph Clustering for single-cell RNA Sequencing Data
Ping Xu, Pengfei Wang, Zhiyuan Ning +3
Clustering analysis is fundamental in single-cell RNA sequencing (scRNA-seq) data analysis for elucidating cellular heterogeneity and diversity. Recent graph-based scRNA-seq cluste…
Disentangled Multi-span Evolutionary Network against Temporal Knowledge Graph Reasoning
Hao Dong, Ziyue Qiao, Zhiyuan Ning +4
Temporal Knowledge Graphs (TKGs), as an extension of static Knowledge Graphs (KGs), incorporate the temporal feature to express the transience of knowledge by describing when facts…
Distilling Closed-Source LLM's Knowledge for Locally Stable and Economic Biomedical Entity Linking
Yihao Ai, Zhiyuan Ning, Weiwei Dai +5
Biomedical entity linking aims to map nonstandard entities to standard entities in a knowledge base. Traditional supervised methods perform well but require extensive annotated dat…
Rethinking Graph Contrastive Learning through Relative Similarity Preservation
Zhiyuan Ning, Pengfei Wang, Ziyue Qiao +2
Graph contrastive learning (GCL) has achieved remarkable success by following the computer vision paradigm of preserving absolute similarity between augmented views. However, this…
scSiameseClu: A Siamese Clustering Framework for Interpreting single-cell RNA Sequencing Data
Ping Xu, Zhiyuan Ning, Pengjiang Li +5
Single-cell RNA sequencing (scRNA-seq) reveals cell heterogeneity, with cell clustering playing a key role in identifying cell types and marker genes. Recent advances, especially g…
Deep Cut-informed Graph Embedding and Clustering
Zhiyuan Ning, Zaitian Wang, Ran Zhang +8
Graph clustering aims to divide the graph into different clusters. The recently emerging deep graph clustering approaches are largely built on graph neural networks (GNN). However,…
Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization
Xiaohan Huang, Dongjie Wang, Zhiyuan Ning +7
Feature transformation methods aim to find an optimal mathematical feature-feature crossing process that generates high-value features and improves the performance of downstream ma…
Towards Graph Prompt Learning: A Survey and Beyond
Qingqing Long, Yuchen Yan, Peiyan Zhang +12
Large-scale "pre-train and prompt learning" paradigms have demonstrated remarkable adaptability, enabling broad applications across diverse domains such as question answering, imag…
Enhancing Tabular Data Optimization with a Flexible Graph-based Reinforced Exploration Strategy
Xiaohan Huang, Dongjie Wang, Zhiyuan Ning +6
Tabular data optimization methods aim to automatically find an optimal feature transformation process that generates high-value features and improves the performance of downstream…
FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimization
Zhiyuan Ning, Chunlin Tian, Meng Xiao +5
Federated Learning faces significant challenges in statistical and system heterogeneity, along with high energy consumption, necessitating efficient client selection strategies. Tr…
scCDCG: Efficient Deep Structural Clustering for single-cell RNA-seq via Deep Cut-informed Graph Embedding
Ping Xu, Zhiyuan Ning, Meng Xiao +4
Single-cell RNA sequencing (scRNA-seq) is essential for unraveling cellular heterogeneity and diversity, offering invaluable insights for bioinformatics advancements. Despite its p…
COMAE: COMprehensive Attribute Exploration for Zero-shot Hashing
Yuqi Li, Qingqing Long, Yihang Zhou +6
Zero-shot hashing (ZSH) has shown excellent success owing to its efficiency and generalization in large-scale retrieval scenarios. While considerable success has been achieved, the…
Resolving the Imbalance Issue in Hierarchical Disciplinary Topic Inference via LLM-based Data Augmentation
Xunxin Cai, Meng Xiao, Zhiyuan Ning +1
In addressing the imbalanced issue of data within the realm of Natural Language Processing, text data augmentation methods have emerged as pivotal solutions. This data imbalance is…
Temporal Inductive Path Neural Network for Temporal Knowledge Graph Reasoning
Hao Dong, Pengyang Wang, Meng Xiao +3
Temporal Knowledge Graph (TKG) is an extension of traditional Knowledge Graph (KG) that incorporates the dimension of time. Reasoning on TKGs is a crucial task that aims to predict…
Interdisciplinary Fairness in Imbalanced Research Proposal Topic Inference: A Hierarchical Transformer-based Method with Selective Interpolation
Meng Xiao, Min Wu, Ziyue Qiao +4
The objective of topic inference in research proposals aims to obtain the most suitable disciplinary division from the discipline system defined by a funding agency. The agency wil…
Adaptive Path-Memory Network for Temporal Knowledge Graph Reasoning
Hao Dong, Zhiyuan Ning, Pengyang Wang +4
Temporal knowledge graph (TKG) reasoning aims to predict the future missing facts based on historical information and has gained increasing research interest recently. Lots of work…
NEEDED: Introducing Hierarchical Transformer to Eye Diseases Diagnosis
Xu Ye, Meng Xiao, Zhiyuan Ning +4
With the development of natural language processing techniques(NLP), automatic diagnosis of eye diseases using ophthalmology electronic medical records (OEMR) has become possible.…
Graph Soft-Contrastive Learning via Neighborhood Ranking
Zhiyuan Ning, Pengfei Wang, Pengyang Wang +5
Graph Contrastive Learning (GCL) has emerged as a promising approach in the realm of graph self-supervised learning. Prevailing GCL methods mainly derive from the principles of con…
Hierarchical MixUp Multi-label Classification with Imbalanced Interdisciplinary Research Proposals
Meng Xiao, Min Wu, Ziyue Qiao +4
Funding agencies are largely relied on a topic matching between domain experts and research proposals to assign proposal reviewers. As proposals are increasingly interdisciplinary,…
RPT: Toward Transferable Model on Heterogeneous Researcher Data via Pre-Training
Ziyue Qiao, Yanjie Fu, Pengyang Wang +5
With the growth of the academic engines, the mining and analysis acquisition of massive researcher data, such as collaborator recommendation and researcher retrieval, has become in…
LightCAKE: A Lightweight Framework for Context-Aware Knowledge Graph Embedding
Zhiyuan Ning, Ziyue Qiao, Hao Dong +2
Knowledge graph embedding (KGE) models learn to project symbolic entities and relations into a continuous vector space based on the observed triplets. However, existing KGE models…
Context-Enhanced Entity and Relation Embedding for Knowledge Graph Completion
Ziyue Qiao, Zhiyuan Ning, Yi Du +1
Most researches for knowledge graph completion learn representations of entities and relations to predict missing links in incomplete knowledge graphs. However, these methods fail…