Chen et al., 2021 - Google Patents
Nas-bench-zero: A large scale dataset for understanding zero-shot neural architecture searchChen et al., 2021
View PDF- Document ID
- 3286465473037009984
- Author
- Chen H
- Lin M
- Sun X
- Li H
- Publication year
External Links
Snippet
Zero-shot Neural Architecture Search (ZS-NAS) is a recently developed low-cost NAS framework which identifies top-performer neural architectures from a large candidate pool without training their parameters. Despite its popularity in recent NAS literatures, the …
- 230000001537 neural 0 title abstract description 7
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
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
- G06F17/30386—Retrieval requests
- G06F17/30424—Query processing
- G06F17/30533—Other types of queries
-
- 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
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
- G06F17/30312—Storage and indexing structures; Management thereof
-
- 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
- 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
- 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
- G06K9/6267—Classification techniques
- G06K9/6279—Classification techniques relating to the number of classes
-
- 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
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Chen et al. | Mongoose: A learnable lsh framework for efficient neural network training | |
Cai et al. | Active learning for graph embedding | |
US9330165B2 (en) | Context-aware query suggestion by mining log data | |
CN113344016B (en) | Deep transfer learning method, device, electronic device and storage medium | |
Bai et al. | CNN feature boosted SeqSLAM for real‐time loop closure detection | |
Sun et al. | Explain any concept: Segment anything meets concept-based explanation | |
Chen et al. | Nas-bench-zero: A large scale dataset for understanding zero-shot neural architecture search | |
Pang et al. | Building discriminative CNN image representations for object retrieval using the replicator equation | |
CN104601438A (en) | Friend recommendation method and device | |
Nguyen et al. | BiasedWalk: Biased sampling for representation learning on graphs | |
Lopez-Cifuentes et al. | Attention-based knowledge distillation in scene recognition: The impact of a DCT-driven loss | |
CN113407808B (en) | Method, device and computer equipment for determining the applicability of graph neural network models | |
CN108038211A (en) | A kind of unsupervised relation data method for detecting abnormality based on context | |
Sagala et al. | Enhanced churn prediction model with boosted trees algorithms in the banking sector | |
Kennedy et al. | Impact of class imbalance on unsupervised label generation for Medicare fraud detection | |
CN115546567A (en) | Unsupervised field adaptive classification method, system, equipment and storage medium | |
Wang et al. | Reinforcement learning transfer based on subgoal discovery and subtask similarity | |
Wistuba et al. | Inductive transfer for neural architecture optimization | |
Wang et al. | Towards efficient convolutional neural networks through low-error filter saliency estimation | |
CN118965081A (en) | A method for identifying multiple accounts for one person based on online local sensitive hashing algorithm | |
CN117496118B (en) | A method and system for analyzing theft vulnerability of target detection model | |
Dhyaram et al. | RANDOM SUBSET FEATURE SELECTION FOR CLASSIFICATION. | |
Lan et al. | Grand: A fast and accurate graph retrieval framework via knowledge distillation | |
Heidari et al. | Filter-Centric Vector Indexing: Geometric Transformation for Efficient Filtered Vector Search | |
Yang et al. | Adaptive density peak clustering for determinging cluster center |