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Found 31 papers, 7 papers with code
DiffusionCom: Structure-Aware Multimodal Diffusion Model for Multimodal Knowledge Graph Completion
Most current MKGC approaches are predominantly based on discriminative models that maximize conditional likelihood.
TD3: Tucker Decomposition Based Dataset Distillation Method for Sequential Recommendation
In the era of data-centric AI, the focus of recommender systems has shifted from model-centric innovations to data-centric approaches.
Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspective
Unlike FL, FR has a unique sparse aggregation mechanism, where the embedding of each item is updated by only partial clients, instead of full clients in a dense aggregation of general FL.
Minimum Topology Attacks for Graph Neural Networks
To break this dilemma, we propose a new type of topology attack, named minimum-budget topology attack, aiming to adaptively find the minimum perturbation sufficient for a successful attack on each node.
Relation Extraction Model Based on Semantic Enhancement Mechanism
The CasAug model proposed in this paper based on the CasRel framework combined with the semantic enhancement mechanism can solve this problem to a certain extent.
Topic model based on co-occurrence word networks for unbalanced short text datasets
We propose a straightforward solution for detecting scarce topics in unbalanced short-text datasets.
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.
Dynamic Fair Federated Learning Based on Reinforcement Learning
Federated learning enables a collaborative training and optimization of global models among a group of devices without sharing local data samples.
Federated Heterogeneous Graph Neural Network for Privacy-preserving Recommendation
Existing HIN-based recommender systems operate under the assumption of centralized storage and model training.
Reinforcement Federated Learning Method Based on Adaptive OPTICS Clustering
The algorithm combines OPTICS clustering and adaptive learning technology, and can effective-ly deal with the problem of non-independent and identically distributed data across different user terminals.
Graph Mining for Cybersecurity: A Survey
In recent years, with the proliferation of graph mining techniques, many researchers investigated these techniques for capturing correlations between cyber entities and achieving high performance.
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.
Cross-modal Search Method of Technology Video based on Adversarial Learning and Feature Fusion
The proposed method uses the framework of adversarial learning to construct a video multimodal feature fusion network and a feature mapping network as generator, a modality discrimination network as discriminator.
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.
A Relational Triple Extraction Method Based on Feature Reasoning for Technological Patents
However, most of them only establish separate table features for each relationship, which ignores the implicit relationship between different entity pairs and different relationship features.
CCPL: Contrastive Coherence Preserving Loss for Versatile Style Transfer
CCPL can preserve the coherence of the content source during style transfer without degrading stylization.
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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A Rare Topic Discovery Model for Short Texts Based on Co-occurrence word Network
We provide a simple and general solution for the discovery of scarce topics in unbalanced short-text datasets, namely, a word co-occurrence network-based model CWIBTD, which can simultaneously address the sparsity and unbalance of short-text topics and attenuate the effect of occasional pairwise occurrences of words, allowing the model to focus more on the discovery of scarce topics.
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.
Sentiment Analysis of Online Travel Reviews Based on Capsule Network and Sentiment Lexicon
In this paper, we study the text sentiment classification of online travel reviews based on social media online comments and propose the SCCL model based on capsule network and sentiment lexicon.
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.
An unsupervised cluster-level based method for learning node representations of heterogeneous graphs in scientific papers
in the heterogeneous graph of scientific papers.
Scientific and Technological Text Knowledge Extraction Method of based on Word Mixing and GRU
The knowledge extraction task is to extract triple relations (head entity-relation-tail entity) from unstructured text data.
An Intellectual Property Entity Recognition Method Based on Transformer and Technological Word Information
This paper proposes a method for extracting intellectual property entities based on Transformer and technical word information , and provides accurate word vector representation in combination with the BERT language method.
Web Page Content Extraction Based on Multi-feature Fusion
With the rapid growth of the number and types of web resources, there are still problems to be solved when using a single strategy to extract the text information of different pages.
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.
Leveraging Graph to Improve Abstractive Multi-Document Summarization
Graphs that capture relations between textual units have great benefits for detecting salient information from multiple documents and generating overall coherent summaries.
Deep Collaborative Filtering with Multi-Aspect Information in Heterogeneous Networks
However, the characteristics of users and the properties of items may stem from different aspects, e. g., the brand-aspect and category-aspect of items.
Noisy Softmax: Improving the Generalization Ability of DCNN via Postponing the Early Softmax Saturation
In this paper, we first emphasize that the early saturation behavior of softmax will impede the exploration of SGD, which sometimes is a reason for model converging at a bad local-minima, then propose Noisy Softmax to mitigating this early saturation issue by injecting annealed noise in softmax during each iteration.
Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild
Past research on facial expressions have used relatively limited datasets, which makes it unclear whether current methods can be employed in real world.
