{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T15:48:03Z","timestamp":1743090483838,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030040697"},{"type":"electronic","value":"9783030040703"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"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":[[2018]]},"DOI":"10.1007\/978-3-030-04070-3_31","type":"book-chapter","created":{"date-parts":[[2018,11,21]],"date-time":"2018-11-21T13:56:27Z","timestamp":1542808587000},"page":"401-413","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Information-Theoretic Self-compression of Multi-layered Neural Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4238-3463","authenticated-orcid":false,"given":"Ryotaro","family":"Kamimura","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,11,22]]},"reference":[{"issue":"6","key":"31_CR1","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1016\/0950-7051(96)81920-4","volume":"8","author":"R Andrews","year":"1995","unstructured":"Andrews, R., Diederich, J., Tickle, A.B.: Survey and critique of techniques for extracting rules from trained artificial neural networks. Knowl. Based Syst. 8(6), 373\u2013389 (1995)","journal-title":"Knowl. Based Syst."},{"issue":"7","key":"31_CR2","doi-asserted-by":"publisher","first-page":"e0130140","DOI":"10.1371\/journal.pone.0130140","volume":"10","author":"S Bach","year":"2015","unstructured":"Bach, S., Binder, A., Montavon, G., Klauschen, F., M\u00fcller, K.R., Samek, W.: On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PloS One 10(7), e0130140 (2015)","journal-title":"PloS One"},{"issue":"5","key":"31_CR3","doi-asserted-by":"publisher","first-page":"1156","DOI":"10.1109\/72.623216","volume":"8","author":"JM Ben\u00edtez","year":"1997","unstructured":"Ben\u00edtez, J.M., Castro, J.L., Requena, I.: Are artificial neural networks black boxes? IEEE Trans. Neural Networks 8(5), 1156\u20131164 (1997)","journal-title":"IEEE Trans. Neural Networks"},{"key":"31_CR4","doi-asserted-by":"crossref","unstructured":"Bucilu\u01ce, C., Caruana, R., Niculescu-Mizil, A.: Model compression. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 535\u2013541. ACM (2006)","DOI":"10.1145\/1150402.1150464"},{"key":"31_CR5","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.enbuild.2015.11.071","volume":"112","author":"LM Candanedo","year":"2016","unstructured":"Candanedo, L.M., Feldheim, V.: Accurate occupancy detection of an office room from light, temperature, humidity and co2 measurements using statistical learning models. Energy Build. 112, 28\u201339 (2016)","journal-title":"Energy Build."},{"key":"31_CR6","doi-asserted-by":"crossref","unstructured":"Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., Elhadad, N.: Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1721\u20131730. ACM (2015)","DOI":"10.1145\/2783258.2788613"},{"key":"31_CR7","unstructured":"Che, Z., Purushotham, S., Khemani, R., Liu, Y.: Interpretable deep models for icu outcome prediction. In: AMIA Annual Symposium Proceedings, vol. 2016, p. 371. American Medical Informatics Association (2016)"},{"key":"31_CR8","unstructured":"Cheng, Y., Wang, D., Zhou, P., Zhang, T.: A survey of model compression and acceleration for deep neural networks. arXiv preprint arXiv:1710.09282 (2017)"},{"issue":"2","key":"31_CR9","first-page":"229","volume":"8","author":"S Dietz","year":"2007","unstructured":"Dietz, S., Anderson, D., Stern, N., Taylor, C., Zenghelis, D., et al.: Right for the right reasons. World Econ. 8(2), 229\u2013258 (2007)","journal-title":"World Econ."},{"key":"31_CR10","doi-asserted-by":"crossref","unstructured":"Goodman, B., Flaxman, S.: European union regulations on algorithmic decision-making and a \u201cright to explanation\u201d. arXiv preprint arXiv:1606.08813 (2016)","DOI":"10.1609\/aimag.v38i3.2741"},{"key":"31_CR11","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)"},{"key":"31_CR12","doi-asserted-by":"crossref","unstructured":"Kamimura, R.: Direct potentiality assimilation for improving multi-layered neural networks. In: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, pp. 19\u201323 (2017)","DOI":"10.15439\/2017F552"},{"key":"31_CR13","doi-asserted-by":"crossref","unstructured":"Kamimura, R.: Mutual information maximization for improving and interpreting multi-layered neural network. In: Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence (SSCI) (SSCI 2017) (2017)","DOI":"10.1109\/SSCI.2017.8285182"},{"key":"31_CR14","unstructured":"Karpathy, A., Johnson, J., Fei-Fei, L.: Visualizing and understanding recurrent networks. arXiv preprint arXiv:1506.02078 (2015)"},{"issue":"3","key":"31_CR15","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1023\/A:1007608224229","volume":"40","author":"TS Lim","year":"2000","unstructured":"Lim, T.S., Loh, W.Y., Shih, Y.S.: A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Mach. Learn. 40(3), 203\u2013228 (2000)","journal-title":"Mach. Learn."},{"key":"31_CR16","unstructured":"Loh, W.Y., Shih, Y.S.: Split selection methods for classification trees. Statistica sinica, pp. 815\u2013840 (1997)"},{"key":"31_CR17","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: Why should i trust you?: explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135\u20131144. ACM (2016)","DOI":"10.1145\/2939672.2939778"},{"key":"31_CR18","unstructured":"Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034 (2013)"},{"key":"31_CR19","unstructured":"Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks. arXiv preprint arXiv:1703.01365 (2017)"},{"key":"31_CR20","unstructured":"Szegedy, C., et al.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013)"},{"issue":"3","key":"31_CR21","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1089\/big.2016.0051","volume":"5","author":"KR Varshney","year":"2017","unstructured":"Varshney, K.R., Alemzadeh, H.: On the safety of machine learning: Cyber-physical systems, decision sciences, and data products. Big Data 5(3), 246\u2013255 (2017)","journal-title":"Big Data"},{"key":"31_CR22","unstructured":"Yosinski, J., Clune, J., Nguyen, A., Fuchs, T., Lipson, H.: Understanding neural networks through deep visualization. arXiv preprint arXiv:1506.06579 (2015)"}],"container-title":["Lecture Notes in Computer Science","Theory and Practice of Natural Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-04070-3_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,4]],"date-time":"2021-03-04T14:25:30Z","timestamp":1614867930000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-04070-3_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030040697","9783030040703"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-04070-3_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"22 November 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"TPNC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Theory and Practice of Natural Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dublin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ireland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 December 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"tpnc2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/tpnc2018.irdta.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}