Search Results for author:
Found 23 papers, 3 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.
Epidemic Decision-making System Based Federated Reinforcement Learning
This model can combine the epidemic situation data of various provinces for cooperative training to use as an enhanced learning model for epidemic situation decision, while protecting the privacy of data.
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.
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.
Semantic Structure Enhanced Contrastive Adversarial Hash Network for Cross-media Representation Learning
Secondly, for further improving learning ability of implicit cross-media semantic associations, a semantic label association graph is constructed, and the graph convolutional network is utilized to mine the implicit semantic structures, thus guiding learning of discriminative features of different modalities.
Robust Diversified Graph Contrastive Network for Incomplete Multi-view Clustering
To address these issues, we propose a Robust Diversified Graph Contrastive Network (RDGC) for incomplete multi-view clustering, which integrates multi-view representation learning and diversified graph contrastive regularization into a unified framework.
Scientific and Technological News Recommendation Based on Knowledge Graph with User Perception
KGUPN contains three main layers, which are the propagation representation layer, the contextual information layer and collaborative relation layer.
Scientific Paper Classification Based on Graph Neural Network with Hypergraph Self-attention Mechanism
Then the whole heterogeneous information network is transformed into a hypergraph composed of different hyperedges.
Aspect-Based Sentiment Analysis using Local Context Focus Mechanism with DeBERTa
Aspect-Based Sentiment Analysis (ABSA) is a fine-grained task in the field of sentiment analysis, which aims to predict the polarity of aspects.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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Chinese Word Sense Embedding with SememeWSD and Synonym Set
To address this limitation, we propose SememeWSD Synonym (SWSDS) model to assign a different vector to every sense of polysemous words with the help of word sense disambiguation (WSD) and synonym set in OpenHowNet.
Bi-convolution matrix factorization algorithm based on improved ConvMF
With the rapid development of information technology, "information overload" has become the main theme that plagues people's online life.
Mining and searching association relation of scientific papers based on deep learning
There is a complex correlation among the data of scientific papers.
Research on Domain Information Mining and Theme Evolution of Scientific Papers
In recent years, with the increase of social investment in scientific research, the number of research results in various fields has increased significantly.
Accurate Portraits of Scientific Resources and Knowledge Service Components
With the advent of the cloud computing era, the cost of creating, capturing and managing information has gradually decreased.
Knowledge Graph and Accurate Portrait Construction of Scientific and Technological Academic Conferences
In recent years, with the continuous progress of science and technology, the number of scientific research achievements is increasing day by day, as the exchange platform and medium of scientific research achievements, the scientific and technological academic conferences have become more and more abundant.
Research on Cross-media Science and Technology Information Data Retrieval
Since the era of big data, the Internet has been flooded with all kinds of information.
An unsupervised cluster-level based method for learning node representations of heterogeneous graphs in scientific papers
in the heterogeneous graph of scientific papers.
Research topic trend prediction of scientific papers based on spatial enhancement and dynamic graph convolution network
To simultaneously capture the spatial dependencies and temporal changes between research topics, we propose a deep neural network-based research topic hotness prediction algorithm, a spatiotemporal convolutional network model.
Research Scholar Interest Mining Method based on Load Centrality
In the era of big data, it is possible to carry out cooperative research on the research results of researchers through papers, patents and other data, so as to study the role of researchers, and produce results in the analysis of results.
Scientific and Technological Information Oriented Semantics-adversarial and Media-adversarial Cross-media Retrieval
In light of this, we propose a scientific and technological information oriented Semantics-adversarial and Media-adversarial Cross-media Retrieval method (SMCR) to find an effective common subspace.
NDPNet: A novel non-linear data projection network for few-shot fine-grained image classification
Metric-based few-shot fine-grained image classification (FSFGIC) aims to learn a transferable feature embedding network by estimating the similarities between query images and support classes from very few examples.
