{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T10:36:20Z","timestamp":1774521380607,"version":"3.50.1"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030588168","type":"print"},{"value":"9783030588175","type":"electronic"}],"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"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","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\/978-3-030-58817-5_20","type":"book-chapter","created":{"date-parts":[[2020,9,29]],"date-time":"2020-09-29T16:46:50Z","timestamp":1601398010000},"page":"261-272","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Parameter Tuning Using Adaptive Moment Estimation in Deep Learning Neural Networks"],"prefix":"10.1007","author":[{"given":"Emmanuel","family":"Okewu","sequence":"first","affiliation":[]},{"given":"Sanjay","family":"Misra","sequence":"additional","affiliation":[]},{"given":"Fernandez-Sanz","family":"Lius","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,30]]},"reference":[{"key":"20_CR1","unstructured":"Brownlee, J.: How to choose loss functions when training deep learning neural networks. In: Deep Learning Performance (2019)"},{"key":"20_CR2","unstructured":"Shridhar, K.: A beginners guide to deep learning (2017)"},{"key":"20_CR3","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Feng, J.: Deep forest: towards an alternative to deep neural networks. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 3553\u20133559. AAAI Press (2017)","DOI":"10.24963\/ijcai.2017\/497"},{"key":"20_CR4","unstructured":"Garnelo, M., Schwarz, J., Rosenbaum, D., Rezende, V.F., Eslami, S.M., Teh, Y.W.: Neural processes, arXiv preprint arXiv:1807.01622 (2018)"},{"key":"20_CR5","unstructured":"Damianou, A., Lawrence, N.: Deep Gaussian processes. In: Artificial Intelligence and Statistics, pp. 207\u2013215 (2013)"},{"key":"20_CR6","unstructured":"Pandey, P.: Demystifying neural networks: a mathematical approach (Part 2) (2018)"},{"key":"20_CR7","unstructured":"Zeiler, M.D.: Adadelta: an adaptive learning rate method, arXiv preprint arXiv:1212.5701 (2012)"},{"issue":"2","key":"20_CR8","first-page":"26","volume":"4","author":"T Tieleman","year":"2012","unstructured":"Tieleman, T., Hinton, G.: Lecture 6.5-rmsprop: divide the gradient by a running average of its recent magnitude. COURSERA Neural Netw. Mach. Learn. 4(2), 26\u201331 (2012)","journal-title":"COURSERA Neural Netw. Mach. Learn."},{"key":"20_CR9","unstructured":"Dauphin, Y.N., Pascanu, R., Caglar, G., Kyunghyun, C., Ganguli, S., Bengio, Y.: Identifying and attacking the saddle point problem in high-dimensional non-convex optimization. In: Advances in Neural Information Processing Systems, pp. 2933\u20132941 (2014)"},{"key":"20_CR10","unstructured":"Kawaguchi, K.: Deep learning without poor local minima. In: Advances in Neural Information Processing Systems (NIPS) (2016)"},{"key":"20_CR11","unstructured":"Kim, D., Fessler, J.A.: Optimized first-order methods for smooth convex minimization. Math. Prog. 151, 8\u2013107 (2016)"},{"key":"20_CR12","doi-asserted-by":"crossref","unstructured":"Aji, A.F., Heafield, K.: Combining global sparse gradients with local gradients. In: ICLR Conference (2019)","DOI":"10.18653\/v1\/D19-1373"},{"key":"20_CR13","unstructured":"Walia, A.S.: Types of optimization algorithms used in neural networks and ways to optimize gradient descent (2017)"},{"key":"20_CR14","unstructured":"Kingma, D.P., Ba, J.L.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (2015)"},{"key":"20_CR15","unstructured":"Koushik, J., Hayashi, H.: Improving stochastic gradient descent with feedback. In: Conference Paper at ICLR (2017)"},{"issue":"4","key":"20_CR16","doi-asserted-by":"publisher","first-page":"838","DOI":"10.1137\/0330046","volume":"30","author":"BT Polyak","year":"1992","unstructured":"Polyak, B.T., Juditsky, A.B.: Acceleration of stochastic approximation by averaging (PDF). SIAM J. Control Optim. 30(4), 838\u2013855 (1992)","journal-title":"SIAM J. Control Optim."},{"key":"20_CR17","unstructured":"Zhang, S., Choromanska, A., LeCun, Y.: Deep learning with elastic averaging SGD. In: Neural Information Processing Systems Conference (NIPS) (2015)"},{"issue":"15","key":"20_CR18","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/j.cam.2017.07.017","volume":"328","author":"C Davies","year":"2018","unstructured":"Davies, C., Dembinska, A.: Computing moments of discrete order statistics from non-identical distributions. J. Comput. Appl. Math. 328(15), 340\u2013354 (2018)","journal-title":"J. Comput. Appl. Math."},{"issue":"1","key":"20_CR19","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/S0893-6080(98)00116-6","volume":"12","author":"N Qian","year":"1999","unstructured":"Qian, N.: On the momentum term in gradient descent learning algorithms. Neural Netw. Official J. Int. Neural Netw. Soc. 12(1), 145\u2013151 (1999)","journal-title":"Neural Netw. Official J. Int. Neural Netw. Soc."},{"key":"20_CR20","unstructured":"Lockett, A.: What is the most popular learning rate decay formula in machine learning? The University of Texas at Austin (2012)"},{"key":"20_CR21","unstructured":"Darken, C., Chang, J., Moody, J.: Learning rate schedules for faster stochastic gradient search. In: Neural Networks for Signal Processing II Proceedings of the 1992 IEEE Workshop (1992)"},{"key":"20_CR22","doi-asserted-by":"crossref","unstructured":"Mei, S.: A mean field view of the landscape of two-layer neural networks. In: Proceedings of the National Academy of Sciences (2018)","DOI":"10.1073\/pnas.1806579115"},{"key":"20_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"704","DOI":"10.1007\/978-3-030-24308-1_55","volume-title":"Computational Science and Its Applications \u2013 ICCSA 2019","author":"E Okewu","year":"2019","unstructured":"Okewu, E., Adewole, P., Sennaike, O.: Experimental comparison of stochastic optimizers in deep learning. In: Misra, S., et al. (eds.) ICCSA 2019. LNCS, vol. 11623, pp. 704\u2013715. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-24308-1_55"}],"container-title":["Lecture Notes in Computer Science","Computational Science and Its Applications \u2013 ICCSA 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58817-5_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,23]],"date-time":"2021-04-23T14:21:52Z","timestamp":1619187712000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58817-5_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030588168","9783030588175"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58817-5_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"30 September 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCSA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science and Its Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cagliari","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 July 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccsa2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.iccsa.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Cyber chair 4","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1450","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":"466","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":"32","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":"32% - 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":"2.5","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":"6","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Conference was held virtually due to COVID-19 pandemic.","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)"}}]}}