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Statistical learning theory
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31	We present new ideas which attempt to explain why Deep Learning works, taking lessons from Theoretical Chemistry, and integrating ideas from Protein Folding, Renormalization Group, and Quantum Chemistry. We address the i Add to Reading List Source URL: mmds-data.org Language: English - Date: 2016-06-23 15:50:48 Physics Theoretical physics Mathematical physics Quantum field theory Renormalization group Academia Statistical mechanics Protein structure Renormalization Deep learning Protein folding Energy landscape
32	Variational Bayesian Learning of Cooperative Vector Quantizer Model - The Theory Add to Reading List Source URL: people.cs.pitt.edu Language: English - Date: 2003-05-06 15:46:12 Statistics Statistical models Bayesian network Latent variable Variational Bayesian methods Independent component analysis Expectationmaximization algorithm Generative model Principal component analysis Bayesian inference Normal distribution Mixture model
33	15 JUNEELSNER AND JAGGER 2935 Add to Reading List Source URL: myweb.fsu.edu Language: English - Date: 2016-08-22 10:16:48 Statistics Meteorology Statistical theory Estimation theory Regression analysis Actuarial science Statistical models Machine learning Tropical cyclone Linear regression Bayesian inference Bayesian network
34	Robot Learning with a Spatial, Temporal, and Causal And-Or Graph Caiming Xiong∗ , Nishant Shukla∗ , Wenlong Xiong, and Song-Chun Zhu Abstract— We propose a stochastic graph-based framework for a robot to understand Add to Reading List Source URL: shukla.io Language: English - Date: 2016-02-23 23:39:56 Academia Applied mathematics Philosophy Networks Statistical models Knowledge representation Graph theory Bayesian network Causality Artificial neural network Motion planning Graph rewriting
35	Statistical Relational Learning with Soft Quantifiers Golnoosh Farnadi1,2 , Stephen H. Bach3 , Marjon Blondeel4 , Marie-Francine Moens2 , Lise Getoor5 , and Martine De Cock1,6 1 Dept. of Applied Mathematics, Computer Sc Add to Reading List Source URL: stephenbach.net Language: English - Date: 2015-09-22 18:21:56 Logic Mathematical logic Abstraction Quantification Predicate logic Model theory Semantics Formal languages Quantifier First-order logic Constructible universe Universal quantification
36	Entire Set of Printable Figures For Visual Categorization in Pigeons Huber Figure 2. An example of the Strasser strain of pigeon. Add to Reading List Source URL: www.pigeon.psy.tufts.edu Language: English - Date: 2001-04-30 15:36:30 Semantics Cognition Machine learning Cognitive science Prototype theory Categorization Statistical classification Psychology Philosophy
37	Learning Symmetric Relational Markov Random Fields A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science by Add to Reading List Source URL: www.cs.huji.ac.il Language: English - Date: 2015-08-10 08:23:33 Graphical models Statistics Probability Statistical theory Variable elimination Markov random field Markov chain Belief propagation Conceptual model Bayesian network Deep learning
38	Journal of Machine Learning Research–2213 Submitted; PublishedNoisy-OR Component Analysis and its Application to Link Analysis Add to Reading List Source URL: people.cs.pitt.edu Language: English - Date: 2006-10-16 22:09:03 Statistics Estimation theory Statistical models Bayesian statistics Statistical natural language processing Statistical theory Expectationmaximization algorithm Variational Bayesian methods Mixture model Exponential family Latent Dirichlet allocation Likelihood function
39	Vol. 24 ECCB 2008, pages i160–i166 doi:bioinformatics/btn282 BIOINFORMATICS Efﬁcient representation and P-value computation for Add to Reading List Source URL: pbil.univ-lyon1.fr Language: English - Date: 2008-10-22 02:35:22 Statistics Probability Machine learning Markov models Statistical models Bioinformatics Estimation theory Graph theory Markov chain Dynamic programming Bayesian network Sequence motif
40	TEM Journal–151 Predicting Bidding Price in Construction using Support Vector Machine Silvana Petruseva 1, Phil Sherrod 2, Valentina Zileska Pancovska 1, Aleksandar Petrovski 3 Add to Reading List Source URL: www.temjournal.com Language: English - Date: 2016-06-03 09:19:27 Statistics Machine learning Applied mathematics Statistical classification Computational neuroscience Computational statistics Estimation theory Artificial neural networks Support vector machine Regression analysis Perceptron Feature selection

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