Hyperparameter (machine learning) (English Wikipedia)

Analysis of information sources in references of the Wikipedia article "Hyperparameter (machine learning)" in English language version.

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9arxiv.org

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7harvard.edu

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2doi.org

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2semanticscholar.org

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1sciencedirect.com

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1worldcat.org

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1mlr.press

low place

1nih.gov

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1in2p3.fr

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1web.archive.org

1^st place

1scipy.org

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1determined.ai

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arxiv.org

Yang, Li; Shami, Abdallah (2020-11-20). "On hyperparameter optimization of machine learning algorithms: Theory and practice". Neurocomputing. 415: 295–316. arXiv:2007.15745. doi:10.1016/j.neucom.2020.07.061. ISSN 0925-2312. S2CID 220919678.
"Claesen, Marc, and Bart De Moor. "Hyperparameter Search in Machine Learning." arXiv preprint arXiv:1502.02127 (2015)". arXiv:1502.02127. Bibcode:2015arXiv150202127C.
"van Rijn, Jan N., and Frank Hutter. "Hyperparameter Importance Across Datasets." arXiv preprint arXiv:1710.04725 (2017)". arXiv:1710.04725. Bibcode:2017arXiv171004725V.
"Probst, Philipp, Bernd Bischl, and Anne-Laure Boulesteix. "Tunability: Importance of Hyperparameters of Machine Learning Algorithms." arXiv preprint arXiv:1802.09596 (2018)". arXiv:1802.09596. Bibcode:2018arXiv180209596P.
Greff, K.; Srivastava, R. K.; Koutník, J.; Steunebrink, B. R.; Schmidhuber, J. (October 23, 2017). "LSTM: A Search Space Odyssey". IEEE Transactions on Neural Networks and Learning Systems. 28 (10): 2222–2232. arXiv:1503.04069. doi:10.1109/TNNLS.2016.2582924. PMID 27411231. S2CID 3356463.
"Breuel, Thomas M. "Benchmarking of LSTM networks." arXiv preprint arXiv:1508.02774 (2015)". arXiv:1508.02774. Bibcode:2015arXiv150802774B.
"Revisiting Small Batch Training for Deep Neural Networks (2018)". arXiv:1804.07612. Bibcode:2018arXiv180407612M.
"Mania, Horia, Aurelia Guy, and Benjamin Recht. "Simple random search provides a competitive approach to reinforcement learning." arXiv preprint arXiv:1803.07055 (2018)". arXiv:1803.07055. Bibcode:2018arXiv180307055M.
"Vanschoren, Joaquin, et al. "OpenML: networked science in machine learning." arXiv preprint arXiv:1407.7722 (2014)". arXiv:1407.7722. Bibcode:2014arXiv1407.7722V.

determined.ai

Villa, Jennifer; Zimmerman, Yoav (25 May 2018). "Reproducibility in ML: why it matters and how to achieve it". Determined AI Blog. Retrieved 31 August 2020.

doi.org

Yang, Li; Shami, Abdallah (2020-11-20). "On hyperparameter optimization of machine learning algorithms: Theory and practice". Neurocomputing. 415: 295–316. arXiv:2007.15745. doi:10.1016/j.neucom.2020.07.061. ISSN 0925-2312. S2CID 220919678.
Greff, K.; Srivastava, R. K.; Koutník, J.; Steunebrink, B. R.; Schmidhuber, J. (October 23, 2017). "LSTM: A Search Space Odyssey". IEEE Transactions on Neural Networks and Learning Systems. 28 (10): 2222–2232. arXiv:1503.04069. doi:10.1109/TNNLS.2016.2582924. PMID 27411231. S2CID 3356463.

harvard.edu

ui.adsabs.harvard.edu

"Claesen, Marc, and Bart De Moor. "Hyperparameter Search in Machine Learning." arXiv preprint arXiv:1502.02127 (2015)". arXiv:1502.02127. Bibcode:2015arXiv150202127C.
"van Rijn, Jan N., and Frank Hutter. "Hyperparameter Importance Across Datasets." arXiv preprint arXiv:1710.04725 (2017)". arXiv:1710.04725. Bibcode:2017arXiv171004725V.
"Probst, Philipp, Bernd Bischl, and Anne-Laure Boulesteix. "Tunability: Importance of Hyperparameters of Machine Learning Algorithms." arXiv preprint arXiv:1802.09596 (2018)". arXiv:1802.09596. Bibcode:2018arXiv180209596P.
"Breuel, Thomas M. "Benchmarking of LSTM networks." arXiv preprint arXiv:1508.02774 (2015)". arXiv:1508.02774. Bibcode:2015arXiv150802774B.
"Revisiting Small Batch Training for Deep Neural Networks (2018)". arXiv:1804.07612. Bibcode:2018arXiv180407612M.
"Mania, Horia, Aurelia Guy, and Benjamin Recht. "Simple random search provides a competitive approach to reinforcement learning." arXiv preprint arXiv:1803.07055 (2018)". arXiv:1803.07055. Bibcode:2018arXiv180307055M.
"Vanschoren, Joaquin, et al. "OpenML: networked science in machine learning." arXiv preprint arXiv:1407.7722 (2014)". arXiv:1407.7722. Bibcode:2014arXiv1407.7722V.

in2p3.fr

indico.lal.in2p3.fr

"Greff, Klaus, and Jürgen Schmidhuber. "Introducing Sacred: A Tool to Facilitate Reproducible Research."" (PDF). 2015.

mlr.press

proceedings.mlr.press

Leyton-Brown, Kevin; Hoos, Holger; Hutter, Frank (January 27, 2014). "An Efficient Approach for Assessing Hyperparameter Importance": 754–762 – via proceedings.mlr.press. {{cite journal}}: Cite journal requires |journal= (help)

nih.gov

pubmed.ncbi.nlm.nih.gov

Greff, K.; Srivastava, R. K.; Koutník, J.; Steunebrink, B. R.; Schmidhuber, J. (October 23, 2017). "LSTM: A Search Space Odyssey". IEEE Transactions on Neural Networks and Learning Systems. 28 (10): 2222–2232. arXiv:1503.04069. doi:10.1109/TNNLS.2016.2582924. PMID 27411231. S2CID 3356463.

sciencedirect.com

Yang, Li; Shami, Abdallah (2020-11-20). "On hyperparameter optimization of machine learning algorithms: Theory and practice". Neurocomputing. 415: 295–316. arXiv:2007.15745. doi:10.1016/j.neucom.2020.07.061. ISSN 0925-2312. S2CID 220919678.

scipy.org

conference.scipy.org

"Greff, Klaus, et al. "The Sacred Infrastructure for Computational Research."" (PDF). 2017. Archived from the original (PDF) on 2020-09-29. Retrieved 2018-04-06.

semanticscholar.org

api.semanticscholar.org

Yang, Li; Shami, Abdallah (2020-11-20). "On hyperparameter optimization of machine learning algorithms: Theory and practice". Neurocomputing. 415: 295–316. arXiv:2007.15745. doi:10.1016/j.neucom.2020.07.061. ISSN 0925-2312. S2CID 220919678.
Greff, K.; Srivastava, R. K.; Koutník, J.; Steunebrink, B. R.; Schmidhuber, J. (October 23, 2017). "LSTM: A Search Space Odyssey". IEEE Transactions on Neural Networks and Learning Systems. 28 (10): 2222–2232. arXiv:1503.04069. doi:10.1109/TNNLS.2016.2582924. PMID 27411231. S2CID 3356463.

web.archive.org

"Greff, Klaus, et al. "The Sacred Infrastructure for Computational Research."" (PDF). 2017. Archived from the original (PDF) on 2020-09-29. Retrieved 2018-04-06.

worldcat.org

search.worldcat.org

Yang, Li; Shami, Abdallah (2020-11-20). "On hyperparameter optimization of machine learning algorithms: Theory and practice". Neurocomputing. 415: 295–316. arXiv:2007.15745. doi:10.1016/j.neucom.2020.07.061. ISSN 0925-2312. S2CID 220919678.