The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Auto-Pytorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL
Abstract
While early AutoML frameworks focused on optimizing traditional ML pipelines and their hyperparameters, a recent trend in AutoML is to focus on neural architecture search. In this paper, we introduce Auto-PyTorch, which brings together the best of these two worlds by jointly and robustly optimizing the network architecture and the training hyperparameters to enable fully automated deep learning (AutoDL). Auto-PyTorch achieves state-of-the-art performance on several tabular benchmarks by combining multi-fidelity optimization with portfolio construction for warmstarting and ensembling of deep neural networks (DNNs) and common baselines for tabular data. To thoroughly study our assumptions on how to design such an AutoDL system, we additionally introduce a new benchmark on learning curves for DNNs, dubbed LCBench, and run extensive ablation studies of the full Auto-PyTorch on typical AutoML benchmarks, eventually showing that Auto-PyTorch performs better than several state-of-the-art competitors.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Neural Architecture Search with Reinforcement Learning
Zoph, B., & Le, Q. V. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.01578 · Cited by 3,974
Neural Architecture Search: A Survey
Elsken, T., Metzen, J. H., & Hutter, F. (2018) · arXiv · doi:10.48550/arxiv.1808.05377 · Cited by 1,763
Self-citationFeurer, M., & Hutter, F. (2019) · The Springer Series on Challenges in Machine Learning · doi:10.1007/978-3-030-05318-5_1 · Cited by 1,414
Self-citationThe reusable holdout: Preserving validity in adaptive data analysis
Dwork, C., Feldman, V., Hardt, M., Pitassi, T., Reingold, O., & Roth, A. (2015) · Science · doi:10.1126/science.aaa9375 · Cited by 307
NAS-bench-101: Towards reproducible neural architecture search
Ying, C., Klein, A., Real, E., Christiansen, E., Murphy, K., & Hutter, F. (2019) · arXiv · doi:10.48550/arxiv.1902.09635 · Cited by 297
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Cited by
Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges
Bischl, B., Binder, M., Lang, M., Pielok, T., Richter, J., Coors, S., Thomas, J., Ullmann, T., Becker, M., Boulesteix, A., et al. (2023) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1484 · Cited by 930
Self-citationSMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
Lindauer, M., Eggensperger, K., Feurer, M., Biedenkapp, A., Deng, D., Benjamins, C., Ruhopf, T., Sass, R., & Hutter, F. (2021) · arXiv · doi:10.48550/arxiv.2109.09831 · Cited by 195
Self-citationNeural Architecture Search Benchmarks: Insights and Survey
Chitty-Venkata, K. T., Emani, M., Vishwanath, V., & Somani, A. K. (2023) · IEEE Access · doi:10.1109/access.2023.3253818 · Cited by 64
Zhao, X., Liu, M., Wu, S., Zhang, B., Burjoo, A., Yang, Y., & Xu, D. (2023) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-22-965 · Cited by 15
Metadata
- Authors
- Zimmer, L., Lindauer, M., & Hutter, F.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2021-09-01
- Volume
- 43
- Issue
- 9
- Pages
- 3079-3090
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W3139250374
- Cited by
- 188
- References
- 7
- FWCI
- 11.38
- Type
- journal article
- Language
- en
- Open access
- closed
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