{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T00:49:15Z","timestamp":1743122955374,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030414030"},{"type":"electronic","value":"9783030414047"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-41404-7_31","type":"book-chapter","created":{"date-parts":[[2020,2,22]],"date-time":"2020-02-22T07:02:58Z","timestamp":1582354978000},"page":"438-452","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Speeding up of the Nelder-Mead Method by Data-Driven Speculative Execution"],"prefix":"10.1007","author":[{"given":"Shuhei","family":"Watanabe","sequence":"first","affiliation":[]},{"given":"Yoshihiko","family":"Ozaki","sequence":"additional","affiliation":[]},{"given":"Yoshiaki","family":"Bando","sequence":"additional","affiliation":[]},{"given":"Masaki","family":"Onishi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,2,23]]},"reference":[{"issue":"Feb","key":"31_CR1","first-page":"281","volume":"13","author":"J Bergstra","year":"2012","unstructured":"Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13(Feb), 281\u2013305 (2012)","journal-title":"J. Mach. Learn. Res."},{"key":"31_CR2","unstructured":"Bergstra, J.S., Bardenet, R., Bengio, Y., K\u00e9gl, B.: Algorithms for hyper-parameter optimization. In: Advances in Neural Information Processing Systems, pp. 2546\u20132554 (2011)"},{"key":"31_CR3","unstructured":"Brochu, E., Cora, V.M., De Freitas, N.: A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv preprint \narXiv:1012.2599\n\n (2010)"},{"key":"31_CR4","doi-asserted-by":"crossref","unstructured":"Dennis, J., Torczon, V.: Parallel implementations of the Nelder-Mead simplex algorithm for unconstrained optimization. In: High Speed Computing, vol. 880, pp. 187\u2013192. International Society for Optics and Photonics (1988)","DOI":"10.1117\/12.944050"},{"key":"31_CR5","series-title":"The Springer Series on Challenges in Machine Learning","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-05318-5_1","volume-title":"Automated Machine Learning","author":"M Feurer","year":"2019","unstructured":"Feurer, M., Hutter, F.: Hyperparameter optimization. In: Hutter, F., Kotthoff, L., Vanschoren, J. (eds.) Automated Machine Learning. TSSCML, pp. 3\u201333. Springer, Cham (2019). \nhttps:\/\/doi.org\/10.1007\/978-3-030-05318-5_1"},{"key":"31_CR6","unstructured":"Gonz\u00e1lez, J., Dai, Z., Hennig, P., Lawrence, N.: Batch Bayesian optimization via local penalization. In: Artificial Intelligence and Statistics, pp. 648\u2013657 (2016)"},{"key":"31_CR7","doi-asserted-by":"crossref","unstructured":"Hansen, N., Auger, A., Ros, R., Finck, S., Po\u0161\u00edk, P.: Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009. In: Proceedings of the 12th Annual Conference Companion on Genetic and Evolutionary Computation, pp. 1689\u20131696. ACM (2010)","DOI":"10.1145\/1830761.1830790"},{"issue":"2","key":"31_CR8","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1162\/106365601750190398","volume":"9","author":"N Hansen","year":"2001","unstructured":"Hansen, N., Ostermeier, A.: Completely derandomized self-adaptation in evolution strategies. Evol. Comput. 9(2), 159\u2013195 (2001)","journal-title":"Evol. Comput."},{"key":"31_CR9","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"31_CR10","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"31_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1007\/978-3-642-25566-3_40","volume-title":"Learning and Intelligent Optimization","author":"F Hutter","year":"2011","unstructured":"Hutter, F., Hoos, H.H., Leyton-Brown, K.: Sequential model-based optimization for general algorithm configuration. In: Coello, C.A.C. (ed.) LION 2011. LNCS, vol. 6683, pp. 507\u2013523. Springer, Heidelberg (2011). \nhttps:\/\/doi.org\/10.1007\/978-3-642-25566-3_40"},{"key":"31_CR12","unstructured":"Kandasamy, K., Krishnamurthy, A., Schneider, J., P\u00f3czos, B.: Parallelised Bayesian optimisation via Thompson sampling. In: International Conference on Artificial Intelligence and Statistics, pp. 133\u2013142 (2018)"},{"key":"31_CR13","unstructured":"Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Technical report, Citeseer (2009)"},{"issue":"2","key":"31_CR14","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/s10614-007-9094-2","volume":"30","author":"D Lee","year":"2007","unstructured":"Lee, D., Wiswall, M.: A parallel implementation of the simplex function minimization routine. Comput. Econ. 30(2), 171\u2013187 (2007)","journal-title":"Comput. Econ."},{"key":"31_CR15","unstructured":"Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., Talwalkar, A.: Hyperband: a novel bandit-based approach to hyperparameter optimization. arXiv preprint \narXiv:1603.06560\n\n (2016)"},{"key":"31_CR16","first-page":"1","volume":"1","author":"I Loshchilov","year":"2016","unstructured":"Loshchilov, I., Hutter, F.: CMA-ES for hyperparameter optimization of deep neural networks. Network 1, 1\u20135 (2016)","journal-title":"Network"},{"key":"31_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1007\/3-540-07165-2_55","volume-title":"Optimization Techniques IFIP Technical Conference Novosibirsk, July 1\u20137, 1974","author":"J Mo\u010dkus","year":"1975","unstructured":"Mo\u010dkus, J.: On Bayesian methods for seeking the extremum. In: Marchuk, G.I. (ed.) Optimization Techniques 1974. LNCS, vol. 27, pp. 400\u2013404. Springer, Heidelberg (1975). \nhttps:\/\/doi.org\/10.1007\/3-540-07165-2_55"},{"key":"31_CR18","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1016\/j.fuel.2018.04.142","volume":"228","author":"A Navid","year":"2018","unstructured":"Navid, A., Khalilarya, S., Abbasi, M.: Diesel engine optimization with multi-objective performance characteristics by non-evolutionary Nelder-Mead algorithm: sobol sequence and Latin hypercube sampling methods comparison in DoE process. Fuel 228, 349\u2013367 (2018)","journal-title":"Fuel"},{"issue":"4","key":"31_CR19","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1093\/comjnl\/7.4.308","volume":"7","author":"JA Nelder","year":"1965","unstructured":"Nelder, J.A., Mead, R.: A simplex method for function minimization. Comput. J. 7(4), 308\u2013313 (1965)","journal-title":"Comput. J."},{"key":"31_CR20","unstructured":"Ozaki, Y., Watanabe, S., Onishi, M.: Accelerating the Nelder-Mead method with predictive parallel evaluation. In: 6th ICML Workshop on Automated Machine Learning (2019)"},{"issue":"1","key":"31_CR21","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1186\/s41074-017-0030-7","volume":"9","author":"Y Ozaki","year":"2017","unstructured":"Ozaki, Y., Yano, M., Onishi, M.: Effective hyperparameter optimization using Nelder-Mead method in deep learning. IPSJ Trans. Comput. Vis. Appl. 9(1), 20 (2017)","journal-title":"IPSJ Trans. Comput. Vis. Appl."},{"key":"31_CR22","unstructured":"Snoek, J., Larochelle, H., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. In: Advances in Neural Information Processing Systems, pp. 2951\u20132959 (2012)"},{"key":"31_CR23","doi-asserted-by":"publisher","first-page":"847","DOI":"10.1007\/s11590-018-1284-4","volume":"13","author":"S Wessing","year":"2018","unstructured":"Wessing, S.: Proper initialization is crucial for the Nelder-Mead simplex search. Opt. Lett. 13, 847\u2013856 (2018)","journal-title":"Opt. Lett."},{"key":"31_CR24","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Proceedings of the British Machine Vision Conference (BMVC), pp. 87.1\u201387.12. BMVA Press (2016)","DOI":"10.5244\/C.30.87"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-41404-7_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,2,22]],"date-time":"2020-02-22T14:02:48Z","timestamp":1582380168000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-41404-7_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030414030","9783030414047"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-41404-7_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"23 February 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Auckland","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Zealand","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 November 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 November 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"acpr2019a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.acpr2019.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"214","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"125","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"58% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"for ACPR 2019 Workshops volume accepted 17 full papers and 6 short papers","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}