TY - JOUR AU - Lecun, Y. AU - Bengio, Y. AU - Hinton, G. PY - 2015 DA - 2015// TI - Deep learning JO - Nature VL - 521 UR - https://doi.org/10.1038/nature14539 DO - 10.1038/nature14539 ID - Lecun2015 ER - TY - JOUR AU - Lecun, Y. AU - Bottou, L. AU - Bengio, Y. AU - Haffner, P. PY - 1998 DA - 1998// TI - Gradient-based learning applied to document recognition JO - Proc IEEE VL - 86 UR - https://doi.org/10.1109/5.726791 DO - 10.1109/5.726791 ID - Lecun1998 ER - TY - STD TI - Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ, editors. Advances in neural information processing systems. vol. 25. Red Hook: Curran Associates, Inc.; 2012. p. 1097–105. http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf. Accessed 10 Oct 2016. UR - http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf ID - ref3 ER - TY - STD TI - Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. 2014. arXiv preprint arXiv:1409.1556. UR - http://arxiv.org/abs/1409.1556 ID - ref4 ER - TY - STD TI - Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. 2015. arXiv preprint arXiv:1512.00567. UR - http://arxiv.org/abs/1512.00567 ID - ref5 ER - TY - STD TI - He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. 2015. arXiv preprint arXiv:1512.03385. UR - http://arxiv.org/abs/1512.03385 ID - ref6 ER - TY - JOUR AU - Tulsiani, S. AU - Kar, A. AU - Carreira, J. AU - Malik, J. PY - 2017 DA - 2017// TI - Learning category-specific deformable 3d models for object reconstruction JO - IEEE Trans Pattern Anal Mach Intell VL - 39 UR - https://doi.org/10.1109/TPAMI.2016.2574713 DO - 10.1109/TPAMI.2016.2574713 ID - Tulsiani2017 ER - TY - STD TI - Mikolov T. Recurrent neural network based language model. ID - ref8 ER - TY - STD TI - Sutskever I, Vinyals O, Le QV. Sequence to sequence learning with neural networks. In: Advances in neural information processing systems. 2014. p. 3104–12. ID - ref9 ER - TY - STD TI - Sak H, Senior A, Beaufays F. Long short-term memory recurrent neural network architectures for large scale acoustic modeling. In: Fifteenth annual conference of the international speech communication association. 2014. ID - ref10 ER - TY - STD TI - Cho K, Van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y. Learning phrase representations using RNN encoder-decoder for statistical machine translation. 2014. arXiv preprint arXiv:1406.1078. UR - http://arxiv.org/abs/1406.1078 ID - ref11 ER - TY - STD TI - Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate. 2014. arXiv preprint arXiv:1409.0473. UR - http://arxiv.org/abs/1409.0473 ID - ref12 ER - TY - STD TI - Cho SJK, Memisevic R, Bengio Y. On using very large target vocabulary for neural machine translation. ID - ref13 ER - TY - STD TI - Fang X. Databridge: Bridging data using sociometric approaches. PhD thesis. 2016. Copyright—Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works; Last updated—2016-08-05. http://libproxy.lib.ilstu.edu/login?url=https://search.proquest.com/docview/1808419308?accountid=11578. UR - http://libproxy.lib.ilstu.edu/login?url=https://search.proquest.com/docview/1808419308?accountid=11578 ID - ref14 ER - TY - STD TI - Zeiler MD, Fergus R. Visualizing and understanding convolutional networks. 2013. arXiv preprint arXiv:1311.2901. UR - http://arxiv.org/abs/1311.2901 ID - ref15 ER - TY - STD TI - Zeiler MD, Krishnan D, Taylor GW, Fergus R. Deconvolutional networks. In: 2010 IEEE conference on computer vision and pattern recognition (CVPR). New York: IEEE; 2010. p. 2528–35. ID - ref16 ER - TY - STD TI - Noh H, Hong S, Han B. Learning deconvolution network for semantic segmentation. In: 2015 IEEE international conference on computer vision (ICCV). 2015. ID - ref17 ER - TY - JOUR AU - Langner, O. AU - Dotsch, R. AU - Bijlstra, G. AU - Wigboldus, D. H. AU - Hawk, S. T. AU - Knippenberg, A. PY - 2010 DA - 2010// TI - Presentation and validation of the radboud faces database JO - Cogn Emot VL - 24 UR - https://doi.org/10.1080/02699930903485076 DO - 10.1080/02699930903485076 ID - Langner2010 ER - TY - STD TI - Dumoulin V, Visin F. A guide to convolution arithmetic for deep learning. 2016. arXiv preprint arXiv:1603.07285. UR - http://arxiv.org/abs/1603.07285 ID - ref19 ER - TY - STD TI - Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks. In: In proceedings of the international conference on artificial intelligence and statistics. Society for Artificial Intelligence and Statistics; 2010. ID - ref20 ER - TY - JOUR AU - Duchi, J. AU - Hazan, E. AU - Singer, Y. PY - 2011 DA - 2011// TI - Adaptive subgradient methods for online learning and stochastic optimization JO - J Mach Learn Res VL - 12 ID - Duchi2011 ER - TY - STD TI - Kingma D, Ba J. Adam: a method for stochastic optimization. 2014. arXiv preprint arXiv:1412.6980. UR - http://arxiv.org/abs/1412.6980 ID - ref22 ER -