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Random search for hyper-parameter optimization
Abstract
Grid search and manual search are the most widely used strategies for hyper-parameter optimization. This paper shows empirically and theoretically that randomly chosen trials are more efficient for...
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Cited by
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Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001
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Wulczyn, E., Thain, N., & Dixon, L. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052591 · Cited by 636
Shin, H.-C., Orton, M. R., Collins, D. J., Doran, S. J., & Leach, M. O. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.277 · Cited by 488
Combining modality specific deep neural networks for emotion recognition in video
Kahou, S. E., Pal, C., Bouthillier, X., Froumenty, P., Gülçehre, Ç., Memisevic, R., Vincent, P., Courville, A., Bengio, Y., Ferrari, R. C., et al. (2013) · Proceedings of the 15th ACM on International conference on multimodal interaction · doi:10.1145/2522848.2531745 · Cited by 391
RAIM: Recurrent Attentive and Intensive Model of Multimodal Patient Monitoring Data
Xu, Y., Biswal, S., Deshpande, S. R., Maher, K. O., & Sun, J. (2018) · Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3219819.3220051 · Cited by 144
Kuang, W., Qian, B., Li, Z., Chen, D., Gao, D., Pan, X., Xie, Y., Li, Y., Ding, B., & Zhou, J. (2024) · Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3637528.3671573 · Cited by 142
Li, B., & Xu, Q. (2024) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2023.3279303 · Cited by 31
Enhancing skin lesion classification: a CNN approach with human baseline comparison
Ajabani, D., Shaikh, Z. A., Yousef, A., Ali, K., & Albahar, M. A. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.2795 · Cited by 18
sincFold: end-to-end learning of short- and long-range interactions in RNA secondary structure
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Sustainable AI: Emerging Trends, Impacts, and Future Challenges
Philipo, A. G., Ning, H., Sarwatt, D. S., Mohamed, J. A., Yusufu, A. S., Shi, F., Urenje, S., & Ding, J. (2025) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2025.3611272 · Cited by 9
Metadata
- Authors
- BergstraJames, & BengioYoshua
- Venue
- Journal of Machine Learning Research
- Publisher
- The MIT Press
- Published
- 2012-02-01
- ISSN
- 1532-4435 (ISSN-L)
- DOI
- 10.5555/2188385.2188395
- OpenAlex
- W2998993395
- Cited by
- 690
- References
- 0
- FWCI
- 4.12
- Type
- journal article
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
