The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Auto-Keras: An Efficient Neural Architecture Search System
Open accessAbstract
Neural architecture search (NAS) has been proposed to automatically tune deep neural networks, but existing search algorithms, e.g., NASNet, PNAS, usually suffer from expensive computational cost. Network morphism, which keeps the functionality of a neural network while changing its neural architecture, could be helpful for NAS by enabling more efficient training during the search. In this paper, we propose a novel framework enabling Bayesian optimization to guide the network morphism for efficient neural architecture search. The framework develops a neural network kernel and a tree-structured acquisition function optimization algorithm to efficiently explores the search space. Extensive experiments on real-world benchmark datasets have been done to demonstrate the superior performance of the developed framework over the state-of-the-art methods. Moreover, we build an open-source AutoML system based on our method, namely Auto-Keras. The code and documentation are available at https://autokeras.com. The system runs in parallel on CPU and GPU, with an adaptive search strategy for different GPU memory limits.
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Scikit-learn: Machine Learning in Python
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Practical Bayesian Optimization of Machine Learning Algorithms
Snoek, J., Larochelle, H., & Adams, R. P. (2012) · arXiv · doi:10.48550/arxiv.1206.2944 · Cited by 6,691
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
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MedMNIST v2 - A large-scale lightweight benchmark for 2D and 3D biomedical image classification
Yang, J., Shi, R., Wei, D., Liu, Z., Zhao, L., Ke, B., Pfister, H., & Ni, B. (2023) · Scientific Data · doi:10.1038/s41597-022-01721-8 · Cited by 1,043
Challenges in deploying machine learning: A survey of case studies
Paleyes, A., Urma, R.-G., & Lawrence, N. D. (2023) · ACM Computing Surveys · doi:10.1145/3533378 · Cited by 638
MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis
Yang, J., Shi, R., & Ni, B. (2021) · 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9434062 · Cited by 414
Looking beyond the hype: applied AI and machine learning in translational medicine
Toh, T. S., Dondelinger, F., & Wang, D. (2019) · EBioMedicine · doi:10.1016/j.ebiom.2019.08.027 · Cited by 159
Recommendations and future directions for supervised machine learning in psychiatry
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Metadata
- Authors
- Jin, H., Song, Q., & Hu, X.
- Venue
- Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
- Publisher
- ACM
- Published
- 2019-07-25
- Pages
- 1946-1956
- OpenAlex
- W2964024268
- Cited by
- 891
- References
- 32
- FWCI
- 49.48
- Type
- conference paper
- Language
- en
- Open access
- bronze
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