Search Results for author:
Found 13 papers, 4 papers with code
scCDCG: Efficient Deep Structural Clustering for single-cell RNA-seq via Deep Cut-informed Graph Embedding
Addressing these limitations, we introduce scCDCG (single-cell RNA-seq Clustering via Deep Cut-informed Graph), a novel framework designed for efficient and accurate clustering of scRNA-seq data that simultaneously utilizes intercellular high-order structural information.
Resolving the Imbalance Issue in Hierarchical Disciplinary Topic Inference via LLM-based Data Augmentation
In addressing the imbalanced issue of data within the realm of Natural Language Processing, text data augmentation methods have emerged as pivotal solutions.
Temporal Inductive Path Neural Network for Temporal Knowledge Graph Reasoning
Subsequently, we utilize the defined query-aware temporal paths on a history temporal graph to model historical path information related to queries for reasoning.
Interdisciplinary Fairness in Imbalanced Research Proposal Topic Inference: A Hierarchical Transformer-based Method with Selective Interpolation
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.
Multi-scale Spatial-temporal Interaction Network for Video Anomaly Detection
To address these deficiencies, we propose a Multi-scale Spatial-Temporal Interaction Network (MSTI-Net) for VAD.
Adaptive Path-Memory Network for Temporal Knowledge Graph Reasoning
Temporal knowledge graph (TKG) reasoning aims to predict the future missing facts based on historical information and has gained increasing research interest recently.
NEEDED: Introducing Hierarchical Transformer to Eye Diseases Diagnosis
It aims to evaluate the condition of both eyes of a patient respectively, and we formulate it as a particular multi-label classification task in this paper.
Hierarchical MixUp Multi-label Classification with Imbalanced Interdisciplinary Research Proposals
In response to this question, we propose a hierarchical mixup multiple-label classification framework, which we called H-MixUp.
Graph Soft-Contrastive Learning via Neighborhood Ranking
Moreover, as the neighborhood size exponentially expands with more hops considered, we propose neighborhood sampling strategies to improve learning efficiency.
RPT: Toward Transferable Model on Heterogeneous Researcher Data via Pre-Training
In this paper, we propose a multi-task self-supervised learning-based researcher data pre-training model named RPT.
LightCAKE: A Lightweight Framework for Context-Aware Knowledge Graph Embedding
Knowledge graph embedding (KGE) models learn to project symbolic entities and relations into a continuous vector space based on the observed triplets.
Context-Enhanced Entity and Relation Embedding for Knowledge Graph Completion
Most researches for knowledge graph completion learn representations of entities and relations to predict missing links in incomplete knowledge graphs.
Team Peter-Parker at SemEval-2019 Task 4: BERT-Based Method in Hyperpartisan News Detection
This paper describes the team peter-parker{'}s participation in Hyperpartisan News Detection task (SemEval-2019 Task 4), which requires to classify whether a given news article is bias or not.
