{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T08:19:06Z","timestamp":1743149946800,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319466804"},{"type":"electronic","value":"9783319466811"}],"license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"unspecified","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":[[2016]]},"DOI":"10.1007\/978-3-319-46681-1_43","type":"book-chapter","created":{"date-parts":[[2016,9,29]],"date-time":"2016-09-29T10:50:26Z","timestamp":1475146226000},"page":"354-362","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Alternating Optimization Method Based on Nonnegative Matrix Factorizations for Deep Neural Networks"],"prefix":"10.1007","author":[{"given":"Tetsuya","family":"Sakurai","sequence":"first","affiliation":[]},{"given":"Akira","family":"Imakura","sequence":"additional","affiliation":[]},{"given":"Yuto","family":"Inoue","sequence":"additional","affiliation":[]},{"given":"Yasunori","family":"Futamura","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2016,9,30]]},"reference":[{"key":"43_CR1","doi-asserted-by":"crossref","unstructured":"Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H.: Greedy layer-wise training of deep networks. In: Proceedings of Advances in Neural Information Processing Systems, vol. 19, pp. 153\u2013160 (2006)","DOI":"10.7551\/mitpress\/7503.003.0024"},{"key":"43_CR2","unstructured":"Ciresan, D.C., Meier, U., Masci, J., Gambardella, L.M., Schmidhuber, J.: Flexible, high performance convolutional neural networks for image classification. In: Proceedings of 22nd International Joint Conference on Artificial Intelligence, pp. 1237\u20131242 (2011)"},{"key":"43_CR3","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/TNNLS.2014.2310059","volume":"26","author":"J Chorowski","year":"2015","unstructured":"Chorowski, J., Member, S.: Learning understandable neural networks with non-negative weight constraints. IEEE Trans. Neural Netw. Learn. Syst. 26, 62\u201369 (2015)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"43_CR4","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1109\/TPAMI.2008.277","volume":"32","author":"D Ding","year":"2010","unstructured":"Ding, D., Li, T., Jordan, M.I.: Convex and semi-nonnegative matrix factorizations. IEEE Trans. Pattern Anal. Mach. Intell. 32, 45\u201355 (2010)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"43_CR5","unstructured":"Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: International Conference on Artificial Intelligence and Statistics, pp. 249\u2013256 (2010)"},{"key":"43_CR6","unstructured":"Glorot, X., Bordes, A., Bengio., Y.: Deep sparse rectifier neural networks. In: Proceedings of 14th International Conference on Artificial Intelligence and Statistics, pp. 315\u2013323 (2011)"},{"key":"43_CR7","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1109\/MSP.2012.2205597","volume":"29","author":"GE Hinton","year":"2012","unstructured":"Hinton, G.E., Deng, L., Yu, D., Dahl, G.E., Mohamed, A., Jaitly, N., Senior, A., Vanhoucke, V.: Deep neural networks for acoustic modeling in speech recognition. IEEE Signal Process. Mag. 29, 82\u201397 (2012)","journal-title":"IEEE Signal Process. Mag."},{"key":"43_CR8","unstructured":"Kingma, D.P., Ba, J.: ADAM: a method for stochastic optimization. In: The International Conference on Learning Representations (ICLR), San Diego (2015)"},{"key":"43_CR9","unstructured":"Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Technical report, Computer Science Department, University of Toronto, vol. 1, p. 7 (2009)"},{"key":"43_CR10","unstructured":"LeCun, Y.: The MNIST database of handwritten digits. http:\/\/yann.lecun.com\/exdb\/mnist"},{"key":"43_CR11","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1038\/44565","volume":"401","author":"DD Lee","year":"1999","unstructured":"Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401, 788\u2013791 (1999)","journal-title":"Nature"},{"key":"43_CR12","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Huffier, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"key":"43_CR13","unstructured":"Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of ICML (2010)"},{"key":"43_CR14","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1002\/env.3170050203","volume":"5","author":"P Paatero","year":"1994","unstructured":"Paatero, P., Tapper, U.: Positive matrix factorization: a non-negative factor model with optimal utilization of error estimates of data values. Environmetrics 5, 111\u2013126 (1994)","journal-title":"Environmetrics"},{"key":"43_CR15","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/323533a0","volume":"323","author":"DE Rumelhart","year":"1986","unstructured":"Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323, 533\u2013536 (1986)","journal-title":"Nature"},{"key":"43_CR16","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"43_CR17","unstructured":"TensorFlow. https:\/\/www.tensorflow.org\/"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-46681-1_43","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,20]],"date-time":"2023-08-20T08:25:20Z","timestamp":1692519920000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-46681-1_43"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"ISBN":["9783319466804","9783319466811"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-46681-1_43","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2016]]},"assertion":[{"value":"30 September 2016","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":"Kyoto","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":"2016","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 October 2016","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 October 2016","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2016","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}