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Found 20 papers, 1 papers with code
Dynamic Self-adaptive Multiscale Distillation from Pre-trained Multimodal Large Model for Efficient Cross-modal Representation Learning
Our methodology streamlines pre-trained multimodal large models using only their output features and original image-level information, requiring minimal computational resources.
Research Team Identification Based on Representation Learning of Academic Heterogeneous Information Network
Academic networks in the real world can usually be described by heterogeneous information networks composed of multi-type nodes and relationships.
Entity Alignment Method of Science and Technology Patent based on Graph Convolution Network and Information Fusion
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.
Semantic Representation Learning of Scientific Literature based on Adaptive Feature and Graph Neural Network
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.
Federated Topic Model and Model Pruning Based on Variational Autoencoder
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.
Interactive Graph Convolutional Filtering
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.
Efficient Partitioning Method of Large-Scale Public Safety Spatio-Temporal Data based on Information Loss Constraints
It can also ensure the load balancing of distributed storage while maintaining spatio-temporal proximity of the data partitioning results.
A Survey on Spectral Graph Neural Networks
Graph neural networks (GNNs) have attracted considerable attention from the research community.
Recent Advances on Federated Learning: A Systematic Survey
In this paper, we provide a systematic survey on federated learning, aiming to review the recent advanced federated methods and applications from different aspects.
T2-GNN: Graph Neural Networks for Graphs with Incomplete Features and Structure via Teacher-Student Distillation
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
In recent years, many efforts have been made on distributed GNN training, and an array of training algorithms and systems have been proposed.
Adaptive Dual Channel Convolution Hypergraph Representation Learning for Technological Intellectual Property
Therefore, a hypergraph neural network model based on dual channel convolution is proposed.
Unsupervised Semantic Representation Learning of Scientific Literature Based on Graph Attention Mechanism and Maximum Mutual Information
Therefore, an unsupervised semantic representation learning method of scientific literature based on graph attention mechanism and maximum mutual information (GAMMI) is proposed.
Embedding Representation of Academic Heterogeneous Information Networks Based on Federated Learning
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.
A sentiment analysis model for car review texts based on adversarial training and whole word mask BERT
Based on this, we propose a car review text sentiment analysis model based on adversarial training and whole word mask BERT(ATWWM-BERT).
Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning
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.
Semantic Similarity Computing for Scientific Academic Conferences fused with domain features
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.
Academic Resource Text Level Multi-label Classification based on Attention
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
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LECF: Recommendation via Learnable Edge Collaborative Filtering
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).
Memory-aware framework for fast and scalable second-order random walk over billion-edge natural graphs
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.
