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Machine learning
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#	Item
761	JMLR: Workshop and Conference Proceedings vol 40:1–77, 2015 Learning Overcomplete Latent Variable Models through Tensor Methods Animashree Anandkumar Add to Reading List Source URL: jmlr.org Language: English - Date: 2015-07-20 20:08:35 Algebra Mathematics Machine learning Linear algebra Statistical models Signal processing Multivariate statistics Higher-order singular value decomposition Independent component analysis Tensor Feature learning Unsupervised learning
762	Reproducibility is imperative for any scientific discovery. More often than not, modern scientific findings rely on statistical analysis of high-dimensional data. At a minimum, reproducibility manifests itself in stabili Add to Reading List Source URL: mmds-data.org Language: English - Date: 2016-06-23 15:50:48 Statistics Machine learning Computational statistics Statistical models Statistical theory Statistical inference Bootstrapping Cross-validation Stability
763	Supervised and unsupervised learning in phonetic adaptation Dave F. Kleinschmidt1 , Rajeev Raizada1 , and T. Florian Jaeger1,2,3 1 Department {dkleinschmidt, raizada, fjeager} @ bcs.rochester.edu of Brain and Cognitive S Add to Reading List Source URL: mindmodeling.org Language: English - Date: 2015-07-27 15:58:32 Machine learning Cognition Linguistics Cognitive science Human communication Phonetics Semantics Semi-supervised learning Speech perception Word-sense disambiguation Supervised learning Categorization
764	Effects of Division of Data in Author Identification Niklas Zechner Department of Computing Science, Ume˚a University Abstract Add to Reading List Source URL: www2.lingfil.uu.se Language: English - Date: 2014-10-16 02:36:04 Machine learning Artificial intelligence Learning Linguistics Computational linguistics Natural language processing Statistical classification Stylometry Accuracy and precision Algorithm Supervised learning Automatic summarization
765	Continuous Deep Q-Learning with Model-based Acceleration arXiv:1603.00748v1 [cs.LG] 2 Mar 2016 Shixiang Gu1 2 3 SG 717@ CAM . AC . UK Add to Reading List Source URL: arxiv.org Language: English - Date: 2016-03-02 20:31:58 Artificial intelligence Machine learning Computational neuroscience Learning Applied mathematics Artificial neural network Mathematical psychology Q-learning Reinforcement learning Supervised learning Feature learning Temporal difference learning
766	Exploring the Limits of Language Modeling arXiv:1602.02410v2 [cs.CL] 11 Feb 2016 Rafal Jozefowicz Oriol Vinyals Add to Reading List Source URL: arxiv.org Language: English - Date: 2016-02-14 20:03:03 Artificial neural networks Artificial intelligence Computational neuroscience Applied mathematics Machine learning Language modeling Computational linguistics Deep learning Recurrent neural network Word embedding Speech recognition Long short-term memory
767	Faster Cover Trees Mike Izbicki Christian Shelton University of California Riverside, 900 University Ave, Riverside, CAAbstract Add to Reading List Source URL: rlair.cs.ucr.edu Language: English - Date: 2015-05-27 17:50:32 Computer programming Computing Learning Machine learning Binary trees K-d tree R-tree Nearest neighbor search Oblivious data structure Cover tree T-tree B-tree
768	ICB ICB CONFIDENTIAL REVIEW COPY. DO NOT DISTRIBUTE. 000 Add to Reading List Source URL: www.vast.uccs.edu Language: English - Date: 2012-07-06 12:49:54 Artificial intelligence Computer vision Learning Surveillance Statistical classification Face recognition Machine learning Biometrics Identification Support vector machine Facial recognition system K-nearest neighbors algorithm
769	Similarity-based learning on structures Michael Biehl, RU Groningen, The Netherlands Barbara Hammer, TU Clausthal, Germany Sepp Hochreiter, University Linz, Austria Stefan C. Kremer, University of Guelph, Canada Thomas Add to Reading List Source URL: www.bioinf.jku.at Language: English - Date: 2011-08-11 05:10:14 Cybernetics Learning Machine learning Sepp Hochreiter Cognition Cognitive science Systems science Data mining
770	Performance-Influence Models for Highly Configurable Systems Norbert Siegmund† , Alexander Grebhahn† , Sven Apel† , Christian Kästner‡ † University of Passau, Germany Add to Reading List Source URL: www.infosun.fim.uni-passau.de Language: English - Date: 2015-07-13 07:23:17 Economy Sampling Survey methodology Global optimization Option Binary option Profiling Heuristic Support vector machine Algorithm Structure Learning

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