PPT-Better Word Representations with Recursive Neural Networks

Thang Luong Joint work with Richard Socher and Christopher D Manning Word frequencies in Wikipedia documents 986 million tokens And more indistinctly nondistinct indistinctive nondistinctive indistinctness . 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. Natural Language Processing. Tomas Mikolov, Facebook. ML Prague 2016. Structure of this talk. Motivation. Word2vec. Architecture. Evaluation. Examples. Discussion. Motivation. Representation of text is very important for performance of many real-world applications: search, ads recommendation, ranking, spam filtering, …. CAP5615 Intro. to Neural Networks. Xingquan (Hill) Zhu. Outline. Multi-layer Neural Networks. Feedforward Neural Networks. FF NN model. Backpropogation (BP) Algorithm. BP rules derivation. Practical Issues of FFNN. Week 5. Applications. Predict the taste of Coors beer as a function of its chemical composition. What are Artificial Neural Networks? . Artificial Intelligence (AI) Technique. Artificial . Neural Networks. 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. Recurrent Neural Network Cell. Recurrent Neural Networks (unenrolled). LSTMs, Bi-LSTMs, Stacked Bi-LSTMs. Today. Recurrent Neural Network Cell.  .  .  .  . Recurrent Neural Network Cell.  .  .  . Nitish Gupta, Shreya Rajpal. 25. th. April, 2017. 1. Story Comprehension. 2. Joe went to the kitchen. Fred went to the kitchen. Joe picked up the milk. Joe travelled to his office. Joe left the milk. Joe went to the bathroom. . 2017-03-24. 조수현. Contents. Extrinsic task. Softmax. classification and regularization. Window classification. Neural networks. Extrinsic task. Extrinsic task:. Using the resulting word vectors for some other extrinsic task. 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. 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. Weifeng Li, . Victor Benjamin, Xiao . Liu, and . Hsinchun . Chen. University of Arizona. 1. Acknowledgements. Many of the pictures, results, and other materials are taken from:. Aarti. Singh, Carnegie Mellon University. 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 . . 循环神经网络. Neural Networks. Recurrent Neural Networks. Humans don’t start their thinking from scratch every second. As you read this essay, you understand each word based on your understanding of previous words. You don’t throw everything away and start thinking from scratch again. Your thoughts have persistence..