{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T10:25:52Z","timestamp":1742984752662,"version":"3.40.3"},"publisher-location":"Cham","reference-count":10,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319466743"},{"type":"electronic","value":"9783319466750"}],"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":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016]]},"DOI":"10.1007\/978-3-319-46675-0_63","type":"book-chapter","created":{"date-parts":[[2016,9,28]],"date-time":"2016-09-28T09:55:47Z","timestamp":1475056547000},"page":"572-580","source":"Crossref","is-referenced-by-count":2,"title":["Improving Neural Network Generalization by Combining Parallel Circuits with Dropout"],"prefix":"10.1007","author":[{"given":"Kien Tuong","family":"Phan","sequence":"first","affiliation":[]},{"given":"Tomas Henrique","family":"Maul","sequence":"additional","affiliation":[]},{"given":"Tuong Thuy","family":"Vu","sequence":"additional","affiliation":[]},{"given":"Weng Kin","family":"Lai","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2016,9,29]]},"reference":[{"issue":"1","key":"63_CR1","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1109\/tpds.2005.11","volume":"16","author":"S Suresh","year":"2005","unstructured":"Suresh, S., Omkar, S.N., Mani, V.: Parallel implementation of back-propagation algorithm in networks of workstations. IEEE Trans. Parallel Distrib. Syst. 16(1), 24\u201334 (2005). doi: 10.1109\/tpds.2005.11","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"63_CR2","unstructured":"Phan, K.T., Maul, T.H., Vu, T.T.: A parallel circuit approach for improving the speed and generalization properties of neural networks. In: Paper presented at the 11th International Conference on Natural Computation (ICNC), Zhangjiajie, 15\u201317 August 2015"},{"key":"63_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neunet.2015.07.007","volume":"71","author":"H Wu","year":"2015","unstructured":"Wu, H., Gu, X.: Towards dropout training for convolutional neural networks. Neural Netw. 71, 1\u201310 (2015). doi: 10.1016\/j.neunet.2015.07.007","journal-title":"Neural Netw."},{"key":"63_CR4","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.artint.2014.02.004","volume":"210","author":"P Baldi","year":"2014","unstructured":"Baldi, P., Sadowski, P.: The dropout learning algorithm. Artif. Intell. 210, 78\u2013122 (2014). http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0004370214000216","journal-title":"Artif. Intell."},{"issue":"5\u20136","key":"63_CR5","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1016\/s0893-6080(03)00115-1","volume":"16","author":"M Matsugu","year":"2003","unstructured":"Matsugu, M., Mori, K., Mitari, Y., Kaneda, Y.: Subject independent facial expression recognition with robust face detection using a convolutional neural network. Neural Netw. Official J. Int. Neural Netw. Soc. 16(5\u20136), 555\u2013559 (2003). doi: 10.1016\/s0893-6080(03)00115-1","journal-title":"Neural Netw. Official J. Int. Neural Netw. Soc."},{"issue":"39","key":"63_CR6","doi-asserted-by":"publisher","first-page":"13773","DOI":"10.1073\/pnas.0503610102","volume":"102","author":"N Kashtan","year":"2005","unstructured":"Kashtan, N., Alon, U.: Spontaneous evolution of modularity and network motifs. Proc. Natl. Acad. Sci. U.S. A. 102(39), 13773\u201313778 (2005). doi: 10.1073\/pnas.0503610102","journal-title":"Proc. Natl. Acad. Sci. U.S. A."},{"key":"63_CR7","unstructured":"Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors, pp. 1\u201318 (2012). doi: arXiv:1207.0580"},{"key":"63_CR8","doi-asserted-by":"publisher","first-page":"1929","DOI":"10.1214\/12-aos1000","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. (JMLR) 15, 1929\u20131958 (2014). doi: 10.1214\/12-aos1000","journal-title":"J. Mach. Learn. Res. (JMLR)"},{"key":"63_CR9","unstructured":"Wan, L., Zeiler, M.: Regularization of neural networks using dropconnect. In: Proceedings of the 30th International Conference on Machine Learning (ICML-13)(1), pp. 109\u2013111 (2013)"},{"key":"63_CR10","unstructured":"Lichman, M.: UCI Machine Learning Repository (2013). http:\/\/archive.ics.uci.edu\/ml"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-46675-0_63","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2017,6,24]],"date-time":"2017-06-24T19:52:55Z","timestamp":1498333975000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-46675-0_63"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"ISBN":["9783319466743","9783319466750"],"references-count":10,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-46675-0_63","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2016]]}}}