PDF-rnn

n r n n nrn n nnnnn nnr7n. Recur rent Neural Networks RNNs have the ability in theory to cope with these temporal dependencies by virtue of the shortterm memory implemented by their recurrent feedback connections How ever in practice they are dif64257cult to train success ful seful܈onnectives Ȃefu lfs ؈on lo܋icc܆o vnFin:iF rnn miaoifcefu fibr rrif deFort܉icnfȘ rreFȘk deflcco vilftreci ؗriF à Socher. , Bauer, Manning, NG 2013. Problem. How can we parse a sentence and create a dense representation of it? . N-grams have obvious . problems, most important is . sparsity. Can we resolve syntactic ambiguity with context? “They ate . Jie Bao Chi-Yin Chow Mohamed F. Mokbel. Department of Computer Science and Engineering. University of Minnesota – Twin Cities. Wei-Shinn Ku. Department of Computer Science and Software Engineering. Abhishek Narwekar, Anusri Pampari. CS 598: Deep Learning and Recognition, Fall 2016. Lecture Outline. Introduction. Learning Long Term Dependencies. Regularization. Visualization for RNNs. Section 1: Introduction. l Networks. Presente. d by:. Kunal Parmar. UHID: 1329834. 1. Outline of the presentation . Introduction. Supervised Sequence Labelling. Recurrent Neura. l Networks. How can RNNs be used for supervised sequence labelling?. Omid Kashefi. omid.Kashefi@pitt.edu. Visual Languages Seminar. November, 2016. Outline. Machine Translation. Deep Learning. Neural Machine Translation. Machine Translation. Machine Translation. Use of software in translating from one language into another. S.Bengio. , . O.Vinyals. , . N.Jaitly. , . N.Shazeer. arXiv:1506.03099. Present by Hanyi Zhang. Contents. Sequence Prediction. Recurrent Neural Network. Problem Description and Proposed Models. Training using scheduled sampling. Example Application. Slot Filling. I would like to arrive . Taipei . on . November 2. nd. .. . ticket booking system. Destination:. time of arrival:. Taipei. November 2. nd. . Slot. Example Application. Li Deng . Deep Learning Technology Center. Microsoft AI and Research Group. Invited Presentation at NIPS Symposium, December 8, 2016. Outline. Topic one. : RNN versus Nonlinear Dynamic Systems;. sequential discriminative vs. generative models. . by. . Jointly. Learning . to. . Align. . and. . Translate. Bahdanau. et. al., ICLR 2015. Presented. . by. İhsan Utlu. Outline. . Neural. Machine . Translation. . overview. Relevant. . Xueying. Bai, . Jiankun. Xu. Multi-label Image Classification. Co-occurrence dependency. Higher-order correlation: one label can be predicted using the previous label. Semantic redundancy: labels have overlapping meanings (cat and kitten). . (CVPR. . 2015). Presenters:. . Tianlu. . Wang. ,. . Y. i. n . Zhang . Oct. ober. 5. th. Human: A young girl asleep on the sofa cuddling a stuffed bear.. NIC: A baby is asleep next to a teddy bear.. Machine l earning - based i ttH → inv isible Xubo GU @ Shanghai Jiao Tong University , School of Mechanical Engineering CERN Work Project Report CERN, CMS Supervisor s : Benjamin Krikler , Oli