PPT-NLP Word Embeddings Deep Learning

What Is the Feature Vector x Typically a vector representation of a single character or word Often reflects the context in which that word is found Could just do counts but that leads to sparse vectors. com Koray Kavukcuoglu DeepMind Technologies koraydeepmindcom Abstract Continuousvalued word embeddings learned by neural language models have re cently been shown to capture semantic and syntactic information about words very well setting performance embeddings. encode about syntax?. Jacob Andreas and Dan Klein. UC Berkeley. Everybody loves word . embeddings. few. most. that. the. a. each. this. every. [. Collobert. 2011]. [. Collobert. 2011, . . Learning. for. . Word, Sense, Phrase, Document and Knowledge. Natural . Language Processing . Lab. , Tsinghua . University. Yu Zhao. , Xinxiong Chen, Yankai Lin, Yang Liu. Zhiyuan Liu. , Maosong Sun. Ohio Center of Excellence in Knowledge-enabled Computing (. Kno.e.sis. ). Wright State University, Dayton, OH, USA. Amit Sheth. amit@knoesis.org. . . Derek Doran. derek@knoesis.org. . . Presented . Continuous. Scoring in Practical Applications. Tuesday 6/28/2016. By Greg Makowski. Greg@Ligadata.com. www.Linkedin.com/in/GregMakowski. Community @. . http. ://. Kamanja.org. . . Try out. Future . The Future of Real-Time Rendering?. 1. Deep Learning is Changing the Way We Do Graphics. [Chaitanya17]. [Dahm17]. [Laine17]. [Holden17]. [Karras17]. [Nalbach17]. Video. “. Audio-Driven Facial Animation by Joint End-to-End Learning of Pose and Emotion”. Map nodes to low-dimensional . embeddings. .. 2) Graph neural networks. Deep learning architectures for graph-structured data. 3) Applications. Representation Learning on Networks, snap.stanford.edu/proj/embeddings-www, WWW 2018. 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. Topic 3. 4/15/2014. Huy V. Nguyen. 1. outline. Deep learning overview. Deep v. shallow architectures. Representation learning. Breakthroughs. Learning principle: greedy layer-wise training. Tera. . scale: data, model, . 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 . @Weekly Meetup. 李博放. About me. Bofang Li 李 . 博放. . libofang@ruc.edu.cn. . http://bofang.stat-nba.com. . Renmin University of China . 中国人民大学. 09/2014-present. Ph.D. candidate. Garima Lalwani Karan Ganju Unnat Jain. Today’s takeaways. Bonus RL recap. Functional Approximation. Deep Q Network. Double Deep Q Network. Dueling Networks. Recurrent DQN. Solving “Doom”. William L. Hamilton, Rex Ying, Jure . Leskovec. Keshav Balasubramanian. Outline. Main goal: generating node embeddings. Survey of past methods. GCNs. GraphSAGE. Algorithm. Optimization and learning. Aggregators. Textual word embeddings map words to meaning and are thus based on semantics. Different words can map to a similar location in the features space even though the letters composing the word are not the same..