Q-learning

Results: 742



#Item
41Non-linear Metric Learning  Dor Kedem, Stephen Tyree, Kilian Q. Weinberger Dept. of Comp. Sci. & Engi. Washington U. St. Louis, MO 63130

Non-linear Metric Learning Dor Kedem, Stephen Tyree, Kilian Q. Weinberger Dept. of Comp. Sci. & Engi. Washington U. St. Louis, MO 63130

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Source URL: eceweb.ucsd.edu

Language: English - Date: 2015-07-31 19:00:25
42Language Understanding for Text-based Games using Deep Reinforcement Learning Karthik Narasimhan∗ CSAIL, MIT

Language Understanding for Text-based Games using Deep Reinforcement Learning Karthik Narasimhan∗ CSAIL, MIT

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Source URL: www.emnlp2015.org

Language: English - Date: 2015-09-04 01:25:56
43© 2005 Nature Publishing Group http://www.nature.com/natureneuroscience  ARTICLES CO M P U TATI O N A N D SYST E M S

© 2005 Nature Publishing Group http://www.nature.com/natureneuroscience ARTICLES CO M P U TATI O N A N D SYST E M S

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Source URL: www.princeton.edu

Language: English - Date: 2008-01-10 16:25:27
44Random Manhattan Indexing A Randomized Scalable Method for Semantic Similarity Measurement in L1 Normed Spaces Behrang Q. Zadeh Insight Centre for Data Analytics National University of Ireland, Galway behrang.qasemizadeh

Random Manhattan Indexing A Randomized Scalable Method for Semantic Similarity Measurement in L1 Normed Spaces Behrang Q. Zadeh Insight Centre for Data Analytics National University of Ireland, Galway behrang.qasemizadeh

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Source URL: pars.ie

Language: English - Date: 2015-10-23 12:32:06
45Characterization and Application of an External Validation Set for Salmonella Mutagenicity (Q)SAR Models Using Structural Fingerprints of Known Toxicophores Abstract # 791  Naomi L. Kruhlak1, Kevin P. Cross2, Barbara L.

Characterization and Application of an External Validation Set for Salmonella Mutagenicity (Q)SAR Models Using Structural Fingerprints of Known Toxicophores Abstract # 791 Naomi L. Kruhlak1, Kevin P. Cross2, Barbara L.

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Source URL: www.leadscope.com

Language: English - Date: 2015-11-13 06:28:16
46Microsoft Word - hierarchical nash q learning in continuous games.docx

Microsoft Word - hierarchical nash q learning in continuous games.docx

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Source URL: www.csse.uwa.edu.au

Language: English - Date: 2009-02-05 01:17:43
47J. Fluid Mech), vol. 789, pp. 726–749. doi:jfmc Cambridge University Press 2016

J. Fluid Mech), vol. 789, pp. 726–749. doi:jfmc Cambridge University Press 2016

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Source URL: www.cse-lab.ethz.ch

Language: English - Date: 2016-02-10 05:23:25
48A B FA F O U N D AT I O N C O U R S E ST UDENT Q UEST IO NS AND ANSWERS SHEE T The Foundation course is the first level of the ABFA’s Distance Learning Programme. 1.  How many modules are there?

A B FA F O U N D AT I O N C O U R S E ST UDENT Q UEST IO NS AND ANSWERS SHEE T The Foundation course is the first level of the ABFA’s Distance Learning Programme. 1. How many modules are there?

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Source URL: www.abfa.org.uk

Language: English - Date: 2016-01-25 07:38:17
49Kopie von q_anzeige_75x297_e learning E-Recruiting.indd

Kopie von q_anzeige_75x297_e learning E-Recruiting.indd

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Source URL: www.heikoroehl.de

Language: German - Date: 2012-01-28 16:26:21
    502016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering  Mining Analogical Libraries in Q&A Discussions — Incorporating Relational and Categorical Knowledge into Word Embedding Chunya

    2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering Mining Analogical Libraries in Q&A Discussions — Incorporating Relational and Categorical Knowledge into Word Embedding Chunya

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    Source URL: ccywch.github.io

    Language: English - Date: 2016-06-16 04:49:45