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nrnnnrnnrrnrnrrrn nn n nn nnnn nn n nnnnnnn nnnnnnn nnnn nnnnnnnnnnn nnn n nnnnnnnnnnnnnn nnnnn nnnnnn 0n nnnnnnnn nnn nnnn nn nnn nnn nnn nnn nn nnnnnnnnn1nn 2 n nn nn nnnrn nnnnnrnn nrr3n nnn. 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 M. achine . T. ranslation. EMNLP. ’. 14 paper by . K. yunghyun. . C. ho, et al.. Recurrent Neural Networks (1/3). 2. Recurrent Neural . Networks (2/3). A variable-length sequence . x . = (x. 1. , …, . Professor Qiang Yang. Outline. Introduction. Supervised Learning. Convolutional Neural Network. Sequence Modelling: RNN and its extensions. Unsupervised Learning. Autoencoder. Stacked . Denoising. . 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. Arun Mallya. Best viewed with . Computer Modern fonts. installed. Outline. Why Recurrent Neural Networks (RNNs)?. The Vanilla RNN unit. The RNN forward pass. Backpropagation. refresher. The RNN backward pass. . Xiaodong. GU. . . Sunghun. Kim. The Hong Kong University of Science and Technology. Hongyu. Zhang . Dongmei. Zhang. Microsoft Research. Programming is . hard. Unfamiliar problems. Unfamiliar . 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. . by. . Jointly. Learning . to. . Align. . and. . Translate. Bahdanau. et. al., ICLR 2015. Presented. . by. İhsan Utlu. Outline. . Neural. Machine . Translation. . overview. Relevant. . 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. CS 501:CS Seminar. Min Xian. Assistant Professor. Department of Computer Science. University of Idaho. Image from NVIDIA. Researchers:. Geoff Hinton. Yann . LeCun. Andrew Ng. Yoshua. . Bengio. …. 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 Ad- 0.2inordertoachieveSIRimprovementsandmaintainSARandSDR.Comparingcolumns2,3,4,and5andcolumns6,7,8,and9,wecanobservethatjointlytrainingthenetworkwiththemaskingfunctionachieveslargeimprovements.Since STAT 689. forecasting. Forecasting is the process of making predictions of the future based on past and present data!. forecasting. Coming up with predictions is important.. It is also very hard since none has the correct model of the world..