Wei et al., 2024 - Google Patents
Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information NetworksWei et al., 2024
View PDF- Document ID
- 8124719992178073447
- Author
- Wei J
- Liu Y
- Huang X
- Zhang X
- Liu W
- Yan X
- Publication year
- Publication venue
- 2024 5th International Conference on Machine Learning and Computer Application (ICMLCA)
External Links
Snippet
This paper explores the applications and challenges of graph neural networks (GNNs) in processing complex graph data brought about by the rapid development of the Internet. Given the heterogeneity and redundancy problems that graph data often have, traditional …
- 238000013528 artificial neural network 0 title abstract description 11
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/20—Handling natural language data
- G06F17/21—Text processing
- G06F17/22—Manipulating or registering by use of codes, e.g. in sequence of text characters
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/20—Handling natural language data
- G06F17/21—Text processing
- G06F17/24—Editing, e.g. insert/delete
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30861—Retrieval from the Internet, e.g. browsers
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation, e.g. computer aided management of electronic mail or groupware; Time management, e.g. calendars, reminders, meetings or time accounting
- G06Q10/101—Collaborative creation of products or services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Wei et al. | Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networks | |
Hu et al. | Deep language models for interpretative and predictive materials science | |
Gao et al. | Video captioning with attention-based LSTM and semantic consistency | |
Truong et al. | Multimodal review generation for recommender systems | |
Psaroudakis et al. | Mixaugment & mixup: Augmentation methods for facial expression recognition | |
Zhu et al. | Work together: Correlation-identity reconstruction hashing for unsupervised cross-modal retrieval | |
Chen et al. | Swafn: Sentimental words aware fusion network for multimodal sentiment analysis | |
Lin et al. | Deep structured scene parsing by learning with image descriptions | |
Pei et al. | A Review of Federated Learning Methods in Heterogeneous scenarios | |
Ni et al. | Federated optimization via knowledge codistillation | |
CN113590928A (en) | Content recommendation method and device and computer-readable storage medium | |
Qiao et al. | Mp-fedcl: Multi-prototype federated contrastive learning for edge intelligence | |
Gu et al. | Toward facial expression recognition in the wild via noise-tolerant network | |
Zhu et al. | Cross-modal retrieval: a systematic review of methods and future directions | |
Baradaran et al. | Ensemble learning-based approach for improving generalization capability of machine reading comprehension systems | |
Miranda-García et al. | Deep learning applications on cybersecurity: A practical approach | |
Aspandi et al. | An enhanced adversarial network with combined latent features for spatio-temporal facial affect estimation in the wild | |
Zhou et al. | A neural group-wise sentiment analysis model with data sparsity awareness | |
Wan et al. | A dual learning-based recommendation approach | |
Wu et al. | AB-GRU: An attention-based bidirectional GRU model for multimodal sentiment fusion and analysis | |
Shen et al. | GD-StarGAN: Multi-domain image-to-image translation in garment design | |
You et al. | Streaming label learning for modeling labels on the fly | |
Jiang et al. | Oraclesage: Towards unified visual-linguistic understanding of oracle bone scripts through cross-modal knowledge fusion | |
Hu et al. | Large language model meets graph neural network in knowledge distillation | |
Yu et al. | A hierarchical heterogeneous graph attention network for emotion-cause pair extraction |