{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T22:24:55Z","timestamp":1742941495888,"version":"3.40.3"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031085291"},{"type":"electronic","value":"9783031085307"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-08530-7_59","type":"book-chapter","created":{"date-parts":[[2022,8,29]],"date-time":"2022-08-29T12:13:00Z","timestamp":1661775180000},"page":"707-713","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Benchmarking Training Methodologies for\u00a0Dense Neural Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3289-8022","authenticated-orcid":false,"given":"Isaac","family":"Tonkin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4284-8619","authenticated-orcid":false,"given":"Geoff","family":"Harris","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1418-4686","authenticated-orcid":false,"given":"Volodymyr","family":"Novykov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,8,30]]},"reference":[{"issue":"2","key":"59_CR1","doi-asserted-by":"publisher","first-page":"2465","DOI":"10.1093\/MNRAS\/STAA713","volume":"494","author":"P Breen","year":"2020","unstructured":"Breen, P., Foley, C., Boekholt, T., Zwart, S.: Newton versus the machine: solving the chaotic three-body problem using deep neural networks. Mon. Not. R. Astron. Soc. 494(2), 2465\u20132470 (2020). https:\/\/doi.org\/10.1093\/MNRAS\/STAA713","journal-title":"Mon. Not. R. Astron. Soc."},{"issue":"4","key":"59_CR2","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/BF02551274","volume":"2","author":"G Cybenko","year":"1989","unstructured":"Cybenko, G.: Approximation by superpositions of a sigmoidal function. Math. Control Sig. Syst. 2(4), 303\u2013314 (1989). https:\/\/doi.org\/10.1007\/BF02551274","journal-title":"Math. Control Sig. Syst."},{"key":"59_CR3","doi-asserted-by":"publisher","unstructured":"Emmert-Streib, F., Yang, Z., Feng, H., Tripathi, S., Dehmer, M.: An introductory review of deep learning for prediction models with big data. Front. Artif. Intell. 3 (2020). https:\/\/doi.org\/10.3389\/frai.2020.00004","DOI":"10.3389\/frai.2020.00004"},{"key":"59_CR4","doi-asserted-by":"publisher","unstructured":"Hornik, K.: Approximation capabilities of multilayer feedforward networks. Neural Netw. 4(2), 251\u2013257 (1991). ISSN 0893-6080, https:\/\/doi.org\/10.1016\/0893-6080(91)90009-T","DOI":"10.1016\/0893-6080(91)90009-T"},{"issue":"16\u201318","key":"59_CR5","doi-asserted-by":"publisher","first-page":"3056","DOI":"10.1016\/j.neucom.2007.02.009","volume":"70","author":"GB Huang","year":"2007","unstructured":"Huang, G.B., Chen, L.: Convex incremental extreme learning machine. Neurocomputing 70(16\u201318), 3056\u20133062 (2007). https:\/\/doi.org\/10.1016\/j.neucom.2007.02.009","journal-title":"Neurocomputing"},{"issue":"1\u20133","key":"59_CR6","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"GB Huang","year":"2006","unstructured":"Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1\u20133), 489\u2013501 (2006). https:\/\/doi.org\/10.1016\/j.neucom.2005.12.126","journal-title":"Neurocomputing"},{"issue":"2","key":"59_CR7","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/MASSP.1987.1165576","volume":"4","author":"R Lippmann","year":"1987","unstructured":"Lippmann, R.: An introduction to computing with neural nets. IEEE ASSP Mag. 4(2), 4\u201322 (1987). https:\/\/doi.org\/10.1109\/MASSP.1987.1165576","journal-title":"IEEE ASSP Mag."},{"key":"59_CR8","doi-asserted-by":"publisher","unstructured":"Mingard, C., Skalse, J., Valle-P\u00e9rez, G., Mart\u00ednez-Rubio, D., Mikulik, V., Louis, A.A.: Neural networks are a priori biased towards Boolean functions with low entropy. arXiv preprint (2019). https:\/\/doi.org\/10.48550\/arxiv.1909.11522","DOI":"10.48550\/arxiv.1909.11522"},{"issue":"3","key":"59_CR9","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1002\/num.22445","volume":"36","author":"A Namadchian","year":"2020","unstructured":"Namadchian, A., Ramezani, M.: Analytical solution of stochastic differential equation by multilayer perceptron neural network approximation of Fokker-Planck equation. Numer. Methods Partial Differ. Equations 36(3), 637\u2013653 (2020). https:\/\/doi.org\/10.1002\/num.22445","journal-title":"Numer. Methods Partial Differ. Equations"},{"key":"59_CR10","doi-asserted-by":"publisher","unstructured":"Nanda, U., Rajput, S., Agrawal, H., Goel, A., Gurnani, M.: On context awareness and analysis of various classification algorithms. Adv. Intell. Syst. Comput. 381, 175\u2013181 (2016). https:\/\/doi.org\/10.1007\/978-81-322-2526-3_19","DOI":"10.1007\/978-81-322-2526-3_19"},{"key":"59_CR11","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1017\/S0962492900002919","volume":"8","author":"A Pinkus","year":"1999","unstructured":"Pinkus, A.: Approximation theory of the MLP model in neural networks. Acta Numer. 8, 143\u2013195 (1999). https:\/\/doi.org\/10.1017\/S0962492900002919","journal-title":"Acta Numer."},{"issue":"5","key":"59_CR12","doi-asserted-by":"publisher","first-page":"836","DOI":"10.1109\/TNN.2007.912306","volume":"19","author":"S Trenn","year":"2008","unstructured":"Trenn, S.: Multilayer perceptrons: approximation order and necessary number of hidden units. IEEE Trans. Neural Netw. 19(5), 836\u2013844 (2008). https:\/\/doi.org\/10.1109\/TNN.2007.912306","journal-title":"IEEE Trans. Neural Netw."},{"key":"59_CR13","doi-asserted-by":"publisher","unstructured":"Wang, J., Lu, S., Wang, S.-H., Zhang, Y.-D.: A review on extreme learning machine. Multimedia Tools Appl. 1\u201350 (2021). https:\/\/doi.org\/10.1007\/s11042-021-11007-7","DOI":"10.1007\/s11042-021-11007-7"},{"key":"59_CR14","unstructured":"Zainuddin, Z., Ong, P.: Function approximation using artificial neural networks. WSEAS Trans. Math. 7(6), 333\u2013338 (2008)"}],"container-title":["Lecture Notes in Computer Science","Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-08530-7_59","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T16:06:32Z","timestamp":1710259592000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-08530-7_59"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031085291","9783031085307"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-08530-7_59","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"30 August 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IEA\/AIE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kitakyushu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 July 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 July 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"35","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ieaaie2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ieaaie2022.wordpress.com\/","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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"127","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":"67","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":"14","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":"53% - 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":"5","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)"}}]}}