Efficient Learning of Practical Markov Random Fields with Exact Inference

[1]  David Sontag,et al.  Unsupervised Learning of Noisy-Or Bayesian Networks , 2013, UAI.

[2]  Joseph K. Bradley,et al.  Learning Large-Scale Conditional Random Fields , 2013 .

[3]  Kevin P. Murphy,et al.  Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.

[4]  Mark W. Schmidt,et al.  On Sparse, Spectral and Other Parameterizations of Binary Probabilistic Models , 2012, AISTATS.

[5]  Nando de Freitas,et al.  Asymptotic Efficiency of Deterministic Estimators for Discrete Energy-Based Models: Ratio Matching and Pseudolikelihood , 2011, UAI.

[6]  Nando de Freitas,et al.  On Autoencoders and Score Matching for Energy Based Models , 2011, ICML.

[7]  M. W. Johnson,et al.  Quantum annealing with manufactured spins , 2011, Nature.

[8]  Nando de Freitas,et al.  Toward the Implementation of a Quantum RBM , 2011 .

[9]  N. Reid,et al.  AN OVERVIEW OF COMPOSITE LIKELIHOOD METHODS , 2011 .

[10]  J. Lafferty,et al.  High-dimensional Ising model selection using ℓ1-regularized logistic regression , 2010, 1010.0311.

[11]  Nando de Freitas,et al.  Inductive Principles for Restricted Boltzmann Machine Learning , 2010, AISTATS.

[12]  Nir Friedman,et al.  Probabilistic Graphical Models - Principles and Techniques , 2009 .

[13]  Michael I. Jordan,et al.  Graphical Models, Exponential Families, and Variational Inference , 2008, Found. Trends Mach. Learn..

[14]  Xuejin Chen,et al.  Sketch-based tree modeling using Markov random field , 2008, ACM Trans. Graph..

[15]  Aapo Hyvärinen,et al.  Connections Between Score Matching, Contrastive Divergence, and Pseudolikelihood for Continuous-Valued Variables , 2007, IEEE Transactions on Neural Networks.

[16]  Yair Weiss,et al.  Minimizing and Learning Energy Functions for Side-Chain Prediction , 2007, RECOMB.

[17]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[18]  Matthew Richardson,et al.  Markov logic networks , 2006, Machine Learning.

[19]  Olga Veksler,et al.  Graph Cuts in Vision and Graphics: Theories and Applications , 2006, Handbook of Mathematical Models in Computer Vision.

[20]  Leonhard Held,et al.  Gaussian Markov Random Fields: Theory and Applications , 2005 .

[21]  Larry Wasserman,et al.  All of Statistics , 2004 .

[22]  David Salesin,et al.  Interactive digital photomontage , 2004, ACM Trans. Graph..

[23]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[24]  Geoffrey E. Hinton Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.

[25]  Pedro Larrañaga,et al.  An Introduction to Probabilistic Graphical Models , 2002, Estimation of Distribution Algorithms.

[26]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[27]  John Odentrantz,et al.  Markov Chains: Gibbs Fields, Monte Carlo Simulation, and Queues , 2000, Technometrics.

[28]  David J. Spiegelhalter,et al.  Probabilistic Networks and Expert Systems , 1999, Information Science and Statistics.

[29]  A. Young,et al.  Spin glasses and random fields , 1997 .

[30]  Numerical Simulations of Spin Glass Systems , 1997, cond-mat/9701016.

[31]  T. Ferguson A Course in Large Sample Theory , 1996 .

[32]  Dan Roth,et al.  On the Hardness of Approximate Reasoning , 1993, IJCAI.

[33]  Paul Smolensky,et al.  Information processing in dynamical systems: foundations of harmony theory , 1986 .

[34]  Edward J. Wegman,et al.  Statistical Signal Processing , 1985 .

[35]  Geoffrey E. Hinton,et al.  A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..

[36]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[37]  J. Laurie Snell,et al.  Markov Random Fields and Their Applications , 1980 .

[38]  J. Besag Statistical Analysis of Non-Lattice Data , 1975 .