{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T19:35:44Z","timestamp":1774640144212,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":18,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819981373","type":"print"},{"value":"9789819981380","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T00:00:00Z","timestamp":1700956800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T00:00:00Z","timestamp":1700956800000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-8138-0_19","type":"book-chapter","created":{"date-parts":[[2023,11,25]],"date-time":"2023-11-25T10:02:23Z","timestamp":1700906543000},"page":"229-244","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Explainable Sparse Associative Self-optimizing Neural Networks for\u00a0Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9001-4198","authenticated-orcid":false,"given":"Adrian","family":"Horzyk","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2715-2738","authenticated-orcid":false,"given":"Jakub","family":"Kosno","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9518-4815","authenticated-orcid":false,"given":"Daniel","family":"Bulanda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2678-5515","authenticated-orcid":false,"given":"Janusz A.","family":"Starzyk","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,26]]},"reference":[{"key":"19_CR1","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning, MIT Press (2016)"},{"key":"19_CR2","doi-asserted-by":"crossref","unstructured":"Ali, S., et al.: Explainable artificial intelligence (XAI): what we know and what is left to attain trustworthy artificial intelligence. Inf. Fusion 99, 101805 (2023). Elsevier","DOI":"10.1016\/j.inffus.2023.101805"},{"key":"19_CR3","doi-asserted-by":"crossref","unstructured":"Dwivedi, R., et al: Explainable AI (XAI): core ideas, techniques, and solutions. ACM Comput. Surv. 55(9), 1\u201333 (2023). ACM New York, NY","DOI":"10.1145\/3561048"},{"key":"19_CR4","unstructured":"Hedstr\u00f6m, A., et al.: Quantus: an explainable AI toolkit for responsible evaluation of neural network explanations and beyond. J. Mach. Learn. Res. 24(34), 1\u201311 (2023)"},{"key":"19_CR5","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017). https:\/\/arxiv.org\/abs\/1706.03762"},{"key":"19_CR6","unstructured":"Subutai, A., Scheinkman, L.: How can we be so dense? The benefits of using highly sparse representations. arXiv preprint arXiv:1903.11257 (2019)"},{"key":"19_CR7","doi-asserted-by":"publisher","unstructured":"Runge, K., Cardoso, C., de Chevigny, A.: Dendritic Spine plasticity: function and mechanisms. Front. Synaptic Neurosci. 12, 36 (2020). https:\/\/doi.org\/10.3389\/fnsyn.2020.00036. PMID: 32982715; PMCID: PMC7484486","DOI":"10.3389\/fnsyn.2020.00036"},{"key":"19_CR8","doi-asserted-by":"publisher","unstructured":"Pchitskaya, E., Bezprozvanny, I.: Dendritic Spines shape analysis-classification or clusterization? Perspective. Front. Synaptic Neurosci. 12, 31 (2020). https:\/\/doi.org\/10.3389\/fnsyn.2020.00031. PMID: 33117142; PMCID: PMC7561369","DOI":"10.3389\/fnsyn.2020.00031"},{"key":"19_CR9","series-title":"Springer Series on Bio- and Neurosystems","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-57715-8","volume-title":"Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence","author":"NK Kasabov","year":"2019","unstructured":"Kasabov, N.K.: Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence. SSBN, vol. 7. Springer, Heidelberg (2019). https:\/\/doi.org\/10.1007\/978-3-662-57715-8"},{"key":"19_CR10","doi-asserted-by":"crossref","unstructured":"Horzyk, A: Associative graph data structures with an efficient access via AVB+trees. In: 11th International Conference on Human System Interaction (HSI), IEEE Xplore, pp. 169\u2013175 (2018)","DOI":"10.1109\/HSI.2018.8430973"},{"key":"19_CR11","volume-title":"Artificial Associative Systems and Associative Artificial Intelligence","author":"A Horzyk","year":"2013","unstructured":"Horzyk, A.: Artificial Associative Systems and Associative Artificial Intelligence. Academic Publishing House EXIT, Warsaw (2013)"},{"key":"19_CR12","unstructured":"Linoff, G.S., Berry, M.A.: Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, 3rd Edition (2011)"},{"key":"19_CR13","doi-asserted-by":"crossref","unstructured":"Olson, R.S., La Cava, W., Orzechowski, P., Urbanowicz, R., Moore, J.H.: PMLB: a large benchmark suite for machine learning evaluation and comparison. BioData Min. 10(1), 36 (2017)","DOI":"10.1186\/s13040-017-0154-4"},{"key":"19_CR14","unstructured":"https:\/\/github.com\/jakubkosno\/ASONNv2"},{"key":"19_CR15","unstructured":"Gale, T., Elsen, E., Hooker, S.: The State of Sparsity in Deep Neural Networks (2019). arXiv:1902.09574"},{"key":"19_CR16","unstructured":"Liu, S., et al.: Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together! (2023). arXiv preprint arXiv:2303.02141"},{"key":"19_CR17","doi-asserted-by":"publisher","first-page":"2383","DOI":"10.1038\/s41467-018-04316-3","volume":"9","author":"DC Mocanu","year":"2018","unstructured":"Mocanu, D.C., Mocanu, E., Stone, P., et al.: Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science. Nat. Commun. 9, 2383 (2018). https:\/\/doi.org\/10.1038\/s41467-018-04316-3","journal-title":"Nat. Commun."},{"key":"19_CR18","doi-asserted-by":"crossref","unstructured":"Atashgahi, Z., Pieterse, J., Liu, S., et al.: A brain-inspired algorithm for training highly sparse neural networks. Mach. Learn. 111, 4411\u20134452 (2022)","DOI":"10.1007\/s10994-022-06266-w"}],"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\/978-981-99-8138-0_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T17:32:46Z","timestamp":1710351166000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8138-0_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,26]]},"ISBN":["9789819981373","9789819981380"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8138-0_19","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,26]]},"assertion":[{"value":"26 November 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":"Changsha","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1274","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":"650","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":"51% - 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":"4.14","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":"2.46","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)"}}]}}