Understanding Nas
Codes for Understanding Architectures Learnt by Cell-based Neural Architecture Search
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Awesome Automl | 785 | 5 months ago | gpl-3.0 | |||||||
| Curating a list of AutoML-related research, tools, projects and other resources | ||||||||||
| Awesome Automl And Lightweight Models | 647 | 4 years ago | ||||||||
| A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures, 3.) Model Compression, Quantization and Acceleration, 4.) Hyperparameter Optimization, 5.) Automated Feature Engineering. | ||||||||||
| Naslib | 448 | 5 months ago | 29 | apache-2.0 | Python | |||||
| NASLib is a Neural Architecture Search (NAS) library for facilitating NAS research for the community by providing interfaces to several state-of-the-art NAS search spaces and optimizers. | ||||||||||
| Archai | 428 | 7 months ago | 9 | September 15, 2023 | 2 | mit | Python | |||
| Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research. | ||||||||||
| Nas Without Training | 321 | 3 years ago | 5 | mit | Python | |||||
| Code for Neural Architecture Search without Training (ICML 2021) | ||||||||||
| Naszilla | 269 | a year ago | 1 | November 15, 2020 | 3 | apache-2.0 | Python | |||
| Naszilla is a Python library for neural architecture search (NAS) | ||||||||||
| Awesome Nas Papers | 258 | 3 years ago | ||||||||
| Awesome Neural Architecture Search Papers | ||||||||||
| Meta Learning | 151 | 3 years ago | mit | |||||||
| meta-learning research | ||||||||||
| Delphi | 96 | a year ago | 1 | apache-2.0 | Rust | |||||
| A Cryptographic Inference Service for Neural Networks | ||||||||||
| Tenas | 95 | 3 years ago | 1 | mit | Python | |||||
| [ICLR 2021] "Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective" by Wuyang Chen, Xinyu Gong, Zhangyang Wang | ||||||||||
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