{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T06:14:40Z","timestamp":1742969680481,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":16,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819916382"},{"type":"electronic","value":"9789819916399"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-981-99-1639-9_17","type":"book-chapter","created":{"date-parts":[[2023,4,14]],"date-time":"2023-04-14T07:02:39Z","timestamp":1681455759000},"page":"202-214","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Effect of\u00a0Logistic Activation Function and\u00a0Multiplicative Input Noise on\u00a0DNN-kWTA Model"],"prefix":"10.1007","author":[{"given":"Wenhao","family":"Lu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chi-Sing","family":"Leung","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John","family":"Sum","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,4,15]]},"reference":[{"key":"17_CR1","unstructured":"Touretzky, S.: Winner-take-all networks of $$ O (n) $$ complexity. Advances in Neural Information Processing Systems, (1) Morgan Kaufmann, 703\u2013711 (1989)"},{"issue":"4","key":"17_CR2","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1007\/BF00983559","volume":"6","author":"TM Kwon","year":"1995","unstructured":"Kwon, T.M., Zervakis, M.: KWTA networks and their applications. Multidimensional Systems and Signal Processing 6(4), 333\u2013346 (1995)","journal-title":"Multidimensional Systems and Signal Processing"},{"key":"17_CR3","doi-asserted-by":"crossref","unstructured":"Narkiewicz J.D., Burleson W.P.: Rank-order filtering algorithms: A comparison of VLSI implementations. In the 1993 IEEE International Symposium on Circuits and Systems. IEEE, 1941\u20131944 (1993)","DOI":"10.1109\/ISCAS.1993.394130"},{"key":"17_CR4","doi-asserted-by":"crossref","unstructured":"Sum, J.P., Leung, C.S., Tam, P.K., Young, G.H., Kan, W.K., Chan, L.w.: Analysis for a class of winner-take-all model. IEEE transactions on neural networks 10(1), 64\u201371 (1999)","DOI":"10.1109\/72.737494"},{"issue":"12","key":"17_CR5","doi-asserted-by":"publisher","first-page":"2022","DOI":"10.1109\/TNN.2008.2003287","volume":"19","author":"X Hu","year":"2008","unstructured":"Hu, X., Wang, J.: An improved dual neural network for solving a class of quadratic programming problems and its $$k$$-winners-take-all application. IEEE Transactions on Neural networks 19(12), 2022\u20132031 (2008)","journal-title":"IEEE Transactions on Neural networks"},{"key":"17_CR6","unstructured":"Moscovici, A.: High speed A\/D converters: understanding data converters through SPICE, vol. 601. Springer Science & Business Media (2001)"},{"issue":"9","key":"17_CR7","doi-asserted-by":"publisher","first-page":"2188","DOI":"10.1109\/TNNLS.2014.2358851","volume":"26","author":"R Feng","year":"2014","unstructured":"Feng, R., Leung, C.S., Sum, J., Xiao, Y.: Properties and performance of imperfect dual neural network-based $$k$$WTA networks. IEEE transactions on neural networks and learning systems 26(9), 2188\u20132193 (2014)","journal-title":"IEEE transactions on neural networks and learning systems"},{"issue":"20","key":"17_CR8","doi-asserted-by":"publisher","first-page":"1088","DOI":"10.1049\/el:20071017","volume":"43","author":"JM Redout\u00e9","year":"2007","unstructured":"Redout\u00e9, J.M., Steyaert, M.: Measurement of emi induced input offset voltage of an operational amplifier. Electronics Letters 43(20), 1088\u20131090 (2007)","journal-title":"Electronics Letters"},{"key":"17_CR9","doi-asserted-by":"crossref","unstructured":"Kuang, X., Wang, T., Fan, F.: The design of low noise chopper operational amplifier with inverter. In: 2015 IEEE 16th International Conference on Communication Technology (ICCT). pp. 568\u2013571. IEEE (2015)","DOI":"10.1109\/ICCT.2015.7399903"},{"key":"17_CR10","unstructured":"Lee, P.: Low noise amplifier selection guide for optimal noise performance. Analog Devices Application Note, AN-940 (2009)"},{"issue":"4","key":"17_CR11","doi-asserted-by":"publisher","first-page":"1082","DOI":"10.1109\/TNNLS.2016.2645602","volume":"29","author":"R Feng","year":"2017","unstructured":"Feng, R., Leung, C.S., Sum, J.: Robustness analysis on dual neural network-based $$k$$WTA with input noise. IEEE transactions on neural networks and learning systems 29(4), 1082\u20131094 (2017)","journal-title":"IEEE transactions on neural networks and learning systems"},{"issue":"9","key":"17_CR12","doi-asserted-by":"publisher","first-page":"4212","DOI":"10.1109\/TNNLS.2017.2759905","volume":"29","author":"J Sum","year":"2017","unstructured":"Sum, J., Leung, C.S., Ho, K.I.J.: On Wang $$k$$WTA with input noise, output node stochastic, and recurrent state noise. IEEE transactions on neural networks and learning systems 29(9), 4212\u20134222 (2017)","journal-title":"IEEE transactions on neural networks and learning systems"},{"key":"17_CR13","doi-asserted-by":"crossref","unstructured":"Semenova, N., et al.: Fundamental aspects of noise in analog-hardware neural networks. Chaos: An Interdisciplinary Journal of Nonlinear Science 29(10) (2019)","DOI":"10.1063\/1.5120824"},{"issue":"9","key":"17_CR14","doi-asserted-by":"publisher","first-page":"4356","DOI":"10.1109\/TED.2021.3089987","volume":"68","author":"S Kariyappa","year":"2021","unstructured":"Kariyappa, S., et al.: Noise-resilient DNN: Tolerating noise in PCM-based AI accelerators via noise-aware training. IEEE Transactions on Electron Devices 68(9), 4356\u20134362 (2021)","journal-title":"IEEE Transactions on Electron Devices"},{"key":"17_CR15","volume-title":"Estimation of the dosage mortality relationship when the dose is subject to error","author":"DC Haley","year":"1952","unstructured":"Haley, D.C.: Estimation of the dosage mortality relationship when the dose is subject to error. STANFORD UNIV CA APPLIED MATHEMATICS AND STATISTICS LABS, Tech. rep. (1952)"},{"issue":"4","key":"17_CR16","doi-asserted-by":"publisher","first-page":"1452","DOI":"10.1109\/TNNLS.2020.3042395","volume":"33","author":"ST Radev","year":"2020","unstructured":"Radev, S.T., Mertens, U.K., Voss, A., Ardizzone, L., Kothe, U.: Bayesflow: Learning complex stochastic models with invertible neural network. IEEE Transactions on Neural Networks and Learning Systems 33(4), 1452\u20131466 (2020)","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-1639-9_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T08:16:44Z","timestamp":1729239404000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-1639-9_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819916382","9789819916399"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-1639-9_17","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"15 April 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Delhi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","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":"22 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iconip2022.apnns.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":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"810","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":"359","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":"44% - 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":"2.65","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":"3","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":"ICONIP 2022 consists of a two-volume set, LNCS & CCIS, which includes 146 and 213 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)"}}]}}