Awesome AutoDL
Table of Contents
A curated list of automated deep learning related resources. Inspired by awesome-deep-vision, awesome-adversarial-machine-learning, awesome-deep-learning-papers, and awesome-architecture-search.
Please feel free to pull requests or open an issue to add papers.
Table of Contents
- Awesome Blogs
- Awesome AutoDL Libraies
- Awesome Benchmarks
- Deep Learning-based NAS and HPO
- Awesome Surveys
Awesome Blogs
- AutoML info and AutoML Freiburg-Hannover
- What’s the deal with Neural Architecture Search?
- Google Could AutoML and PocketFlow
- AutoML Challenge and AutoDL Challenge
- In Defense of Weight-sharing for Neural Architecture Search: an optimization perspective
Awesome AutoDL Libraies
Awesome Benchmarks
Deep Learning-based NAS and HPO
| Type | G | RL | EA | PD | Other |
|---|---|---|---|---|---|
| Explanation | gradient-based | reinforcement learning | evolutionary algorithm | performance prediction | other types |
2021 Venues
2020 Venues
2019 Venues
2018 Venues
2017 Venues
| Title | Venue | Type | Code |
|---|---|---|---|
| Neural Architecture Search with Reinforcement Learning | ICLR | RL | - |
| Designing Neural Network Architectures using Reinforcement Learning | ICLR | RL | - |
| Neural Optimizer Search with Reinforcement Learning | ICML | RL | - |
| Learning Curve Prediction with Bayesian Neural Networks | ICLR | PD | - |
| Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization | ICLR | PD | - |
| Hyperparameter Optimization: A Spectral Approach | NeurIPS-W | Other | github |
| Learning to Compose Domain-Specific Transformations for Data Augmentation | NeurIPS | - | - |
Previous Venues
2012-2016
| Title | Venue | Type | Code |
|---|---|---|---|
| Speeding up Automatic Hyperparameter Optimization of Deep Neural Networksby Extrapolation of Learning Curves | IJCAI | PD | github |
arXiv
| Title | Date | Type | Code |
|---|---|---|---|
| NSGA-NET: A Multi-Objective Genetic Algorithm for Neural Architecture Search | 2018.10 | EA | - |
| Training Frankenstein’s Creature to Stack: HyperTree Architecture Search | 2018.10 | G | - |
| Population Based Training of Neural Networks | 2017.11 | EA | GitHub |
Awesome Surveys
| Title | Venue | Year | Code |
|---|---|---|---|
| A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions | ACM Computing Surveys | 2021 | - |
| Automated Machine Learning on Graphs: A Survey | ICLR-W | 2021 | GitHub |
| On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice | Neurocomputing | 2020 | github |
| AutonoML: Towards an Integrated Framework for Autonomous Machine Learning | arXiv | 2020 | - |
| Automated Machine Learning | Springer Book | 2019 | - |
| Neural architecture search: A survey | JMLR | 2019 | - |
| AutoML: A Survey of the State-of-the-Art | arXiv | 2019 | GitHub |
| A Survey on Neural Architecture Search | arXiv | 2019 | - |
| Taking human out of learning applications: A survey on automated machine learning | arXiv | 2018 | - |
Metadata
Release files for awesome-autodl 1.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| awesome_autodl-1.2.tar.gz | 50.2 kB | Details |
Built distributions (wheels)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| awesome_autodl-1.2-py3.8.egg | Legacy Egg format | - | - | Details |
| awesome_autodl-1.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 147.3 kB
Release files / awesome_autodl-1.2.tar.gz
| Download URL | awesome_autodl-1.2.tar.gz |
|---|---|
| Size | 50.2 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Release files / awesome_autodl-1.2-py3.8.egg
| Download URL | awesome_autodl-1.2-py3.8.egg |
|---|---|
| Size | 55.7 kB |
| Tags | Egg |
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SHA-256 checksum How to use checksums |
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twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.8.8
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Release files / awesome_autodl-1.2-py3-none-any.whl
| Download URL | awesome_autodl-1.2-py3-none-any.whl |
|---|---|
| Size | 41.3 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.8.8
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