Search Results for author: Yawen Li

Found 20 papers, 1 papers with code

Dynamic Self-adaptive Multiscale Distillation from Pre-trained Multimodal Large Model for Efficient Cross-modal Representation Learning

no code implementations16 Apr 2024 Zhengyang Liang, Meiyu Liang, Wei Huang, Yawen Li, Zhe Xue

Our methodology streamlines pre-trained multimodal large models using only their output features and original image-level information, requiring minimal computational resources.

Cross-Modal Retrieval Representation Learning

Research Team Identification Based on Representation Learning of Academic Heterogeneous Information Network

no code implementations2 Nov 2023 Junfu Wang, Yawen Li, Zhe Xue, Ang Li

Academic networks in the real world can usually be described by heterogeneous information networks composed of multi-type nodes and relationships.

Representation Learning

Federated Topic Model and Model Pruning Based on Variational Autoencoder

no code implementations1 Nov 2023 Chengjie Ma, Yawen Li, Meiyu Liang, Ang Li

The first method involves slow pruning throughout the entire model training process, which has limited acceleration effect on the model training process, but can ensure that the pruned model achieves higher accuracy.

Entity Alignment Method of Science and Technology Patent based on Graph Convolution Network and Information Fusion

no code implementations1 Nov 2023 Runze Fang, Yawen Li, Yingxia Shao, Zeli Guan, Zhe Xue

The entity alignment of science and technology patents aims to link the equivalent entities in the knowledge graph of different science and technology patent data sources.

Attribute Entity Alignment

Semantic Representation Learning of Scientific Literature based on Adaptive Feature and Graph Neural Network

no code implementations1 Nov 2023 Hongrui Gao, Yawen Li, Meiyu Liang, Zeli Guan, Zhe Xue

At the same time, in order to enrich the features of scientific literature, a learning method of semantic representation of scientific literature based on adaptive features and graph neural network is proposed.

Graph Attention Representation Learning

Interactive Graph Convolutional Filtering

no code implementations4 Sep 2023 Jin Zhang, Defu Lian, Hong Xie, Yawen Li, Enhong Chen

Furthermore, we employ Bayesian meta-learning methods to effectively address the cold-start problem and derive theoretical regret bounds for our proposed method, ensuring a robust performance guarantee.

Collaborative Filtering Meta-Learning +2

Efficient Partitioning Method of Large-Scale Public Safety Spatio-Temporal Data based on Information Loss Constraints

no code implementations22 Jun 2023 Jie Gao, Yawen Li, Zhe Xue, Zeli Guan

It can also ensure the load balancing of distributed storage while maintaining spatio-temporal proximity of the data partitioning results.

graph partitioning Management

A Survey on Spectral Graph Neural Networks

no code implementations11 Feb 2023 Deyu Bo, Xiao Wang, Yang Liu, Yuan Fang, Yawen Li, Chuan Shi

Graph neural networks (GNNs) have attracted considerable attention from the research community.

Graph Representation Learning

Recent Advances on Federated Learning: A Systematic Survey

no code implementations3 Jan 2023 Bingyan Liu, Nuoyan Lv, Yuanchun Guo, Yawen Li

In this paper, we provide a systematic survey on federated learning, aiming to review the recent advanced federated methods and applications from different aspects.

Federated Learning Privacy Preserving

T2-GNN: Graph Neural Networks for Graphs with Incomplete Features and Structure via Teacher-Student Distillation

no code implementations24 Dec 2022 Cuiying Huo, Di Jin, Yawen Li, Dongxiao He, Yu-Bin Yang, Lingfei Wu

A key premise for the remarkable performance of GNNs relies on complete and trustworthy initial graph descriptions (i. e., node features and graph structure), which is often not satisfied since real-world graphs are often incomplete due to various unavoidable factors.

Distributed Graph Neural Network Training: A Survey

no code implementations1 Nov 2022 Yingxia Shao, Hongzheng Li, Xizhi Gu, Hongbo Yin, Yawen Li, Xupeng Miao, Wentao Zhang, Bin Cui, Lei Chen

In recent years, many efforts have been made on distributed GNN training, and an array of training algorithms and systems have been proposed.

Distributed Computing

Embedding Representation of Academic Heterogeneous Information Networks Based on Federated Learning

no code implementations7 Oct 2022 Junfu Wang, Yawen Li, Meiyu Liang, Ang Li

To solve the above challenges, aiming at the data information of scientific research teams closely related to science and technology, we proposed an academic heterogeneous information network embedding representation learning method based on federated learning (FedAHE), which utilizes node attention and meta path attention mechanism to learn low-dimensional, dense and real-valued vector representations while preserving the rich topological information and meta-path-based semantic information of nodes in network.

Blocking Federated Learning +1

Unsupervised Semantic Representation Learning of Scientific Literature Based on Graph Attention Mechanism and Maximum Mutual Information

no code implementations7 Oct 2022 Hongrui Gao, Yawen Li, Meiyu Liang, Zeli Guan

Therefore, an unsupervised semantic representation learning method of scientific literature based on graph attention mechanism and maximum mutual information (GAMMI) is proposed.

Contrastive Learning Graph Attention +2

A sentiment analysis model for car review texts based on adversarial training and whole word mask BERT

no code implementations6 Jun 2022 Xingchen Liu, Yawen Li, Yingxia Shao, Ang Li, Jian Liang

Based on this, we propose a car review text sentiment analysis model based on adversarial training and whole word mask BERT(ATWWM-BERT).

Decision Making Sentiment Analysis

Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning

no code implementations30 Apr 2022 Cuiying Huo, Dongxiao He, Yawen Li, Di Jin, Jianwu Dang, Weixiong Zhang, Witold Pedrycz, Lingfei Wu

However, the existing contrastive learning methods are inadequate for heterogeneous graphs because they construct contrastive views only based on data perturbation or pre-defined structural properties (e. g., meta-path) in graph data while ignore the noises that may exist in both node attributes and graph topologies.

Attribute Contrastive Learning

Semantic Similarity Computing for Scientific Academic Conferences fused with domain features

no code implementations21 Mar 2022 Runyu Yu, Yawen Li, Ang Li

Aiming at the problem that the current general-purpose semantic text similarity calculation methods are difficult to use the semantic information of scientific academic conference data, a semantic similarity calculation algorithm for scientific academic conferences by fusion with domain features is proposed.

Keyword Extraction Semantic Similarity +2

Academic Resource Text Level Multi-label Classification based on Attention

no code implementations21 Mar 2022 Yue Wang, Yawen Li, Ang Li

We propose an attention-based hierarchical multi-label classification algorithm of academic texts (AHMCA) by integrating features such as text, keywords, and hierarchical structure, the academic documents are classified into the most relevant categories.

Document Embedding Hierarchical Multi-label Classification +2

LECF: Recommendation via Learnable Edge Collaborative Filtering

1 code implementation Science China Information Sciences 2021 Shitao Xiao, Yingxia Shao, Yawen Li, Hongzhi Yin, Yanyan Shen & Bin Cui

In this paper, we model an interaction between user and item as an edge and propose a novel CF framework, called learnable edge collaborative filtering (LECF).

Collaborative Filtering

Memory-aware framework for fast and scalable second-order random walk over billion-edge natural graphs

no code implementations The VLDB Journal 2021 Yingxia Shao, Shiyue Huang, Yawen Li, Xupeng Miao, Bin Cui & Lei Chen

In this paper, to clearly compare the efficiency of various node sampling methods, we first design a cost model and propose two new node sampling methods: one follows the acceptance-rejection paradigm to achieve a better balance between memory and time cost, and the other is optimized for fast sampling the skewed probability distributions existed in natural graphs.

Community Detection Graph Embedding

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