PDF-Generating Text with Recurrent Neural Networks Ilya Sutskever ILYA CS UTORONTO CA James

Toronto ON M5S 3G4 CANADA Abstract Recurrent Neural Networks RNNs are very powerful sequence models that do not enjoy widespread use because it is extremely dif64257 cult to train them properly Fortunately re cent advances in Hessianfree optimizatio. torontoedu Geoffrey Hinton Department of Computer Science University of Toronto Toronto Ontario M5S 3G4 hintoncstorontoedu ABSTRACT We show how to learn a deep graphical model of the wordcount vectors obtained from a large set of documents The values Easy to understand Easy to code by hand Often used to represent inputs to a net Easy to learn This is what mixture models do Each cluster corresponds to one neuron Easy to associate with other representations or responses But localist models are ver – Basic Methods Dr. Saed Sayad University of Toronto 2010 saed.sayad@utoronto.ca 1 http://chem - eng.utoronto.ca/~datamining/ Classification - ZeroR http://chem - eng.utoronto.ca/~datamining/ 1. Recurrent Networks. Some problems require previous history/context in order to be able to give proper output (speech recognition, stock forecasting, target tracking, etc.. One way to do that is to just provide all the necessary context in one "snap-shot" and use standard learning. Brains and games. Introduction. Spiking Neural Networks are a variation of traditional NNs that attempt to increase the realism of the simulations done. They more closely resemble the way brains actually operate. Deep Learning @ . UvA. UVA Deep Learning COURSE - Efstratios Gavves & Max Welling. LEARNING WITH NEURAL NETWORKS . - . PAGE . 1. Machine Learning Paradigm for Neural Networks. The Backpropagation algorithm for learning with a neural network. Recurrent Neural Network Cell. Recurrent Neural Networks (unenrolled). LSTMs, Bi-LSTMs, Stacked Bi-LSTMs. Today. Recurrent Neural Network Cell.  .  .  .  . Recurrent Neural Network Cell.  .  .  . : Pedagogue, Conductor, . Author and Contributor to Music . Education. Scott E. Woodard - Faculty Lecture. West Virginia State University. April 17, 2014 12:30PM Davis Fine Arts Room 103. What is Conducting?. 1. Table of contents. Recurrent models. Partially recurrent neural networks. . Elman networks. Jordan networks. Recurrent neural networks. BackPropagation Through Time. Dynamics of a neuron with feedback. Dongwoo Lee. University of Illinois at Chicago . CSUN (Complex and Sustainable Urban Networks Laboratory). Contents. Concept. Data . Methodologies. Analytical Process. Results. Limitations and Conclusion. Introduction to Back Propagation Neural . Networks BPNN. By KH Wong. Neural Networks Ch9. , ver. 8d. 1. Introduction. Neural Network research is are very . hot. . A high performance Classifier (multi-class). Abigail See, Peter J. Liu, Christopher D. Manning. Presented by: Matan . Eyal. Agenda. Introduction. Word Embeddings. RNNs. Sequence-to-Sequence. Attention. Pointer Networks. Coverage Mechanism. Introduction . Models and applications. Outline. Sequence Data. Recurrent Neural Networks Variants. Handling Long Term Dependencies. Attention Mechanisms. Properties of RNNs. Applications of RNNs. Hands-on LSTM-supported timeseries prediction. 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..