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rn nr r rr2 nnnnr 5rnnnrn nr nrrr r n rn rrn rn r2 nn7 nrrrr nr rrnr6rn rnrnrr n 07nnnnnrx284 rrnnrn rnr9nnn nrrrrr7r77r6nAnn7rrrn r7 rrB rnrrrrnr9nnn nrr7 9B rCD rnrr rnn. 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 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 . Group 4: . Nipun . Garg, Surabhi . Mithal. http://www-users.cs.umn.edu/~smithal/. Chapter Organization. 2. OLD Organization. 5.1 . Evaluation of Spatial Operations . 5.2 . Query Optimization . 5.3 . DDD --- RRR OOO MMM DD RRR III VVV EEE LTN-486(S), LTN-485(S), LTN-483(S/L) Features : 48X CD-ROM with Pure-CAV TechnologyE-IDE Interface, Support all PIO, MW-DMA, Ultra DMA/33 modesSupporting Power Yulia Kogan and . Ron . Shiff. 19.06.2016. References. J. Mao, W. Xu, Y. Yang, J. Wang, and A. L. Yuille. Explain images with multimodal recurrent neural networks. . arXiv preprint arXiv:1410.1090, 2014. 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. 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. 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. 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). nnrnnrn45 617rr rr rr rrrrr rr r8 r9 rrrr rrrr 3r rrrr9 rr r r9 r r r rrr rrr r r rr rr 6 r n n/rrr 01rrrrr23rrrr r r r rrrrr n 345n6rrrrrrrrrr r r-4r-rr r 6616rr r5 r5rr6rn7rrrr-1r 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.. Human Language Technologies. Giuseppe Attardi. Some slides from . Arun. . Mallya. Università di Pisa. Recurrent. RNNs are called . recurrent.  because they perform the same task for every element of a sequence, with the output depending on the previous values..