PPT-Handing Uncertain Observations in Unsupervised Topic-Mixtur

Author : faustina-dinatale | Published Date : 2016-05-02

Language Model Adaptation Ekapol Chuangsuwanich 1 Shinji Watanabe 2 Takaaki Hori 2 Tomoharu Iwata 2 James Glass 1 報告者郝柏翰 20130305 ICASSP 2012

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Handing Uncertain Observations in Unsupervised Topic-Mixtur: Transcript

Language Model Adaptation Ekapol Chuangsuwanich 1 Shinji Watanabe 2 Takaaki Hori 2 Tomoharu Iwata 2 James Glass 1 報告者郝柏翰 20130305 ICASSP 2012. eduau Todd Mytkowicz Microsoft Research toddmmicrosoftcom Kathryn S McKinley Microsoft Research mckinleymicrosoftcom Abstract Emerging applications increasingly use estimates such as sen sor data GPS probabilistic models machine learning big data and STANDARD-HANDING DOORS Corridor, & Hinge Side DOUBLE ACTING (LH/RHR) OUT Swing DOOR HANDING CHARTTHE HANDING OF THE DOOR IS ALWAYS DETERMINED FROM THE OUTSIDEHERE ARE A FEW RULES TO FOLLOWThe outside of the exterior door is the street or entrance side The outside of a School of Computing. National University of Singapore. Department of Computer Science. Aalborg. University. Meihui. Zhang. , Su Chen, Christian S. Jensen, . Beng. Chin . Ooi. , . Zhenjie. Zhang. School of Computing. National University of Singapore. Department of Computer Science. Aalborg. University. Meihui. Zhang. , Su Chen, Christian S. Jensen, . Beng. Chin . Ooi. , . Zhenjie. Zhang. Arijit Khan. Systems Group. ETH Zurich. Lei Chen. Hong . Kong University of Science and Technology. Social Network. Transportation Network. Chemical Compound. Biological Network. Graphs are Everywhere. from Text. Padhraic Smyth. Department of Computer Science. University of California, Irvine . . Outline. General aspects of text mining. Named-entity extraction, question-answering systems, etc. Unsupervised learning from text documents. 指導教授：陳良弼老師. 報告者：鄧雅文 . 97753034. Introduction. Related Work. Problem Formulation. Future Work. Outline. Top-. k. query on certain data. Rank results according to a user-defined score. Wherever you roam. And admit that the waters. Around you have grown. And accept it that soon. You'll be drenched to the bone.. If your time to you. Is worth savin'. Then you better start swimmin'. Or you'll sink like a stone. Unsupervised Learning DSCI 415 Brant Deppa, Ph.D. Professor of Statistics & Data Science Winona State University bdeppa@winona.edu The Entire Course in One Day !?!? Course Topics Introduction to Unsupervised Learning Learning What is learning? What are the types of learning? Why aren’t robots using neural networks all the time? They are like the brain, right? Where does learning go in our operational architecture? Wherever you roam. And admit that the waters. Around you have grown. And accept it that soon. You'll be drenched to the bone.. If your time to you. Is worth savin'. Then you better start swimmin'. Or you'll sink like a stone. FROM BIG DATA. Richard Holaj. Humor GENERATING . introduction. very hard . problem. . deep. . semantic. . understanding. . cultural. . contextual. . clues. . solutions. . using. . labelling.

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