Search Results for author:
Found 27 papers, 7 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.
COMAE: COMprehensive Attribute Exploration for Zero-shot Hashing
Zero-shot hashing (ZSH) has shown excellent success owing to its efficiency and generalization in large-scale retrieval scenarios.
scInterpreter: Training Large Language Models to Interpret scRNA-seq Data for Cell Type Annotation
Despite the inherent limitations of existing Large Language Models in directly reading and interpreting single-cell omics data, they demonstrate significant potential and flexibility as the Foundation Model.
Unveiling Delay Effects in Traffic Forecasting: A Perspective from Spatial-Temporal Delay Differential Equations
To tackle the above issues, we propose a neural Spatial-Temporal Delay Differential Equation model, namely STDDE.
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.
Semi-supervised Domain Adaptation in Graph Transfer Learning
To deal with the domain shift, we add adaptive shift parameters to each of the source nodes, which are trained in an adversarial manner to align the cross-domain distributions of node embedding, thus the node classifier trained on labeled source nodes can be transferred to the target nodes.
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.
Traceable Group-Wise Self-Optimizing Feature Transformation Learning: A Dual Optimization Perspective
Feature transformation aims to reconstruct an effective representation space by mathematically refining the existing features.
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.
Beyond Discrete Selection: Continuous Embedding Space Optimization for Generative Feature Selection
Furthermore, we reconstruct feature selection solutions using these embeddings and select the feature subset with the highest performance for downstream tasks as the optimal subset.
Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting
The distribution shift in Time Series Forecasting (TSF), indicating series distribution changes over time, largely hinders the performance of TSF models.
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.
Traceable Automatic Feature Transformation via Cascading Actor-Critic Agents
Feature transformation for AI is an essential task to boost the effectiveness and interpretability of machine learning (ML).
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.
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.
Self-Optimizing Feature Transformation
Feature transformation aims to extract a good representation (feature) space by mathematically transforming existing features.
Hierarchical Interdisciplinary Topic Detection Model for Research Proposal Classification
Specifically, we first propose a hierarchical transformer to extract the textual semantic information of proposals.
Who Should Review Your Proposal? Interdisciplinary Topic Path Detection for Research Proposals
After extracting the semantic and interdisciplinary knowledge, we design a level-wise prediction component to fuse the two types of knowledge representations and detect interdisciplinary topic paths for each proposal.
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.
Data Augmentation for Graph Convolutional Network on Semi-Supervised Classification
However, data augmentation for graph-based models remains a challenging problem, as graph data is more complex than traditional data, which consists of two features with different properties: graph topology and node attributes.
TWIST-GAN: Towards Wavelet Transform and Transferred GAN for Spatio-Temporal Single Image Super Resolution
We propose a frequency domain-based spatio-temporal remote sensingsingle image super-resolution technique to reconstruct the HR image combined with generative adversarialnetworks (GANs) on various frequency bands (TWIST-GAN).
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.
Reinforced Imitative Graph Representation Learning for Mobile User Profiling: An Adversarial Training Perspective
To solve the problem, we propose an adversarial training strategy to guarantee the robustness of the representation module.
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.
Tree Structure-Aware Graph Representation Learning via Integrated Hierarchical Aggregation and Relational Metric Learning
The integrated hierarchical aggregation module aims to preserve the tree structure by combining GNN with Gated Recurrent Unit to integrate the hierarchical and sequential neighborhood information on the tree structure to node representations.
Heterogeneous Metric Learning with Content-based Regularization for Software Artifact Retrieval
Specifically, this method can capture both the inherent information in the source codes and the semantic information hidden in the comments, descriptions, and identifiers of the source codes.
