{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T05:00:36Z","timestamp":1726030836512},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030205201"},{"type":"electronic","value":"9783030205218"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-20521-8_48","type":"book-chapter","created":{"date-parts":[[2019,6,4]],"date-time":"2019-06-04T23:02:40Z","timestamp":1559689360000},"page":"583-595","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Some Insights and Observations on Depth Issues in Deep Learning Networks"],"prefix":"10.1007","author":[{"given":"Arindam","family":"Chaudhuri","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,5,16]]},"reference":[{"key":"48_CR1","first-page":"625","volume":"11","author":"D Erhan","year":"2010","unstructured":"Erhan, D., Bengio, Y., Courville, A., Manzagol, P.A., Vincent, P., Bengio, S.: Why does unsupervised pre-training help deep learning? J. Mach. Learn. Res. 11, 625\u2013660 (2010)","journal-title":"J. Mach. Learn. Res."},{"issue":"5786","key":"48_CR2","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"GE Hinton","year":"2006","unstructured":"Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504\u2013507 (2006)","journal-title":"Science"},{"key":"48_CR3","first-page":"3371","volume":"11","author":"P Vincent","year":"2010","unstructured":"Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371\u20133408 (2010)","journal-title":"J. Mach. Learn. Res."},{"issue":"4","key":"48_CR4","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)","journal-title":"Math. Control Sig. Syst."},{"key":"48_CR5","unstructured":"Dauphin, Y.N., Bengio, Y.: Big neural networks waste capacity. \n                      arXiv:1301.3583\n                      \n                     (2013)"},{"key":"48_CR6","unstructured":"Eigen, D., Rolfe, J., Fergus, R., LeCun, Y.: Understanding deep architectures using a recursive convolutional network. \n                      arXiv:1312.1847\n                      \n                     (2013)"},{"key":"48_CR7","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. \n                      arXiv:1409.1556\n                      \n                     (2014)"},{"key":"48_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. \n                      arXiv:1512.03385\n                      \n                     (2015)","DOI":"10.1109\/CVPR.2016.90"},{"key":"48_CR9","unstructured":"Denil, M., Shakibi, B., Dinh, L., Ranzato, M.A., De Freitas, N.: Predicting parameters in deep learning. \n                      arXiv:1306.0543\n                      \n                     (2013)"},{"key":"48_CR10","doi-asserted-by":"crossref","unstructured":"Cheng, Y., Yu, F.X., Feris, R.S., Kumar, S., Choudhary, A.N., Chang, S.F.: An exploration of parameter redundancy in deep networks with circulant projections. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.327"},{"key":"48_CR11","unstructured":"LeCun, Y., Denker, J.S., Solla, S.A.: Optimal brain damage. In: NIPS (1990)"},{"key":"48_CR12","unstructured":"Hassibi, B., Stork, D.G., Wolff, G.J.: Optimal brain surgeon and general network pruning. In: ICNN (1993)"},{"key":"48_CR13","unstructured":"Mozer, M., Smolensky, P.: Skeletonization: A technique for trimming the fat from a network via relevance assessment. In: NIPS (1988)"},{"issue":"2","key":"48_CR14","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1162\/neco.1990.2.2.188","volume":"2","author":"C Ji","year":"1990","unstructured":"Ji, C., Snapp, R.R., Psaltis, D.: Generalizing smoothness constraints from discrete samples. Neural Comput. 2(2), 188\u2013197 (1990)","journal-title":"Neural Comput."},{"issue":"5","key":"48_CR15","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1109\/72.248452","volume":"4","author":"R Reed","year":"1993","unstructured":"Reed, R.: Pruning algorithms \u2013 a survey. IEEE Trans. Neural Netw. 4(5), 740\u2013747 (1993)","journal-title":"IEEE Trans. Neural Netw."},{"key":"48_CR16","unstructured":"Hinton, G.E., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv (2014)"},{"key":"48_CR17","unstructured":"Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y.: Fitnets: hints for thin deep nets. In: ICLR (2015)"},{"key":"48_CR18","unstructured":"Chaudhuri, A.: Some investigations in deep neural networks for image and text datasets. Technical report TR\u20131896, Samsung R & D Institute Delhi India (2018)"},{"key":"48_CR19","unstructured":"Montufar, G.F., Pascanu, R., Cho, K., Bengio, Y.: On the number of linear regions of deep neural networks. In: NIPS (2014)"},{"key":"48_CR20","unstructured":"Liu, B., Wang, M., Foroosh, H., Tappen, M., Penksy, M.: Sparse convolutional neural networks. In: CVPR (2015)"},{"key":"48_CR21","doi-asserted-by":"crossref","unstructured":"Jaderberg, M., Vedaldi, A., Zisserman, A.: Speeding up convolutional neural networks with low rank expansions. In: BMVC (2014)","DOI":"10.5244\/C.28.88"},{"key":"48_CR22","unstructured":"Denton, E.L., Zaremba, W., Bruna, J., LeCun, Y., Fergus., R.: Exploiting linear structure within convolutional networks for efficient evaluation. In: NIPS (2014)"},{"key":"48_CR23","unstructured":"Gong, Y., Liu, L., Yang, M., Bourdev, L.D.: Compressing deep convolutional networks using vector quantization. \n                      arXiv:1412.6115\n                      \n                     (2014)"},{"key":"48_CR24","unstructured":"Srivastava, R.K., Greff, K., Schmidhuber, J.: Training very deep networks. In: NIPS (2015)"},{"key":"48_CR25","unstructured":"Collins, M.D., Kohli, P.: Memory bounded deep convolutional networks. \n                      arXiv:1412.1442\n                      \n                     (2014)"},{"issue":"2","key":"48_CR26","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1080\/10618600.2012.681250","volume":"22","author":"N Simon","year":"2013","unstructured":"Simon, N., Friedman, J., Hastie, T., Tibshirani, R.: A sparse-group lasso. J. Comput. Graph. Stat. 22(2), 231\u2013245 (2013)","journal-title":"J. Comput. Graph. Stat."},{"key":"48_CR27","unstructured":"Experimental datasets. \n                      https:\/\/www.analyticsvidhya.com\/blog\/2018\/03\/comprehensive-collection-deep-learning-datasets\/"}],"container-title":["Lecture Notes in Computer Science","Advances in Computational Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-20521-8_48","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,6,4]],"date-time":"2019-06-04T23:07:49Z","timestamp":1559689669000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-20521-8_48"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030205201","9783030205218"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-20521-8_48","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"16 May 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IWANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Work-Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Gran Canaria","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 June 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 June 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwann2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iwann.uma.es\/","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"}},{"value":"easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"210","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"150","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"71% - 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"}},{"value":"2,9","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"2,5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}}]}}