An et al., 2025 - Google Patents
Ai flow: Perspectives, scenarios, and approachesAn et al., 2025
View PDF- Document ID
- 15577862908499576733
- Author
- An H
- Hu W
- Huang S
- Huang S
- Li R
- Liang Y
- Shao J
- Song Y
- Wang Z
- Yuan C
- Zhang C
- Zhang H
- Zhuang W
- Li X
- Publication year
- Publication venue
- arXiv preprint arXiv:2506.12479
External Links
Snippet
Pioneered by the foundational information theory by Claude Shannon and the visionary framework of machine intelligence by Alan Turing, the convergent evolution of information and communication technologies (IT/CT) has created an unbroken wave of connectivity and …
Classifications
-
- 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
- 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
- G06N3/04—Architectures, e.g. interconnection topology
-
- 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
- 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
- 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
- G06F9/00—Arrangements for programme control, e.g. control unit
- G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
-
- 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
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109947954B (en) | Multi-task collaborative identification method and system | |
| CN113590849B (en) | Multimedia resource classification model training method and multimedia resource recommendation method | |
| Wang et al. | Cross-modal retrieval: a systematic review of methods and future directions | |
| JP2022531641A (en) | Quantization model optimization method, device, information recommendation method, device, neural network model optimization method, device, electronic device and computer program | |
| Yang et al. | Self-supervised high-order information bottleneck learning of spiking neural network for robust event-based optical flow estimation | |
| CN117876941B (en) | Target multimodal model system and construction method, video processing model training method, video processing method | |
| Mu et al. | A comprehensive survey of mixture-of-experts: Algorithms, theory, and applications | |
| Sun et al. | Generative AI for deep reinforcement learning: Framework, analysis, and use cases | |
| CN112115744B (en) | Point cloud data processing method and device, computer storage medium and electronic equipment | |
| Liu et al. | Cross-modal generative semantic communications for mobile AIGC: Joint semantic encoding and prompt engineering | |
| Guo et al. | Large language models and artificial intelligence generated content technologies meet communication networks | |
| Farhadi et al. | Domain adaptation in reinforcement learning: a comprehensive and systematic study | |
| Wang et al. | Fundamentals of artificial intelligence | |
| Han et al. | Dual adaptive learning multi-task multi-view for graph network representation learning | |
| CN119416889A (en) | Multimedia processing, model training methods, systems, devices, equipment, media and products | |
| Yang et al. | Agent-driven generative semantic communication with cross-modality and prediction | |
| An et al. | AI Flow: Perspectives, Scenarios, and Approaches | |
| Zhang et al. | PURE: Personality-Coupled Multi-Task Learning Framework for Aspect-Based Multimodal Sentiment Analysis | |
| Bian et al. | A survey on parameter-efficient fine-tuning for foundation models in federated learning | |
| Wang et al. | Large model empowered metaverse: State-of-the-art, challenges and opportunities | |
| Meng et al. | An efficient pruning and fine-tuning method for deep spiking neural network | |
| Lan et al. | Collaborative multi-agent video fast-forwarding | |
| Vu et al. | Integration of TinyML and LargeML: A Survey of 6G and Beyond | |
| Ding et al. | A novel two-stage learning pipeline for deep neural networks | |
| Chen et al. | Towards General Industrial Intelligence: A Survey of Continual Large Models in Industrial IoT |