Joint parsing and alignment with weakly synchronized grammars
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Relaxed marginal inference and its application to dependency parsing
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Blocked inference in Bayesian tree substitution grammars
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
On dual decomposition and linear programming relaxations for natural language processing
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Turbo parsers: dependency parsing by approximate variational inference
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
A latent variable model for geographic lexical variation
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Image segmentation with topic random field
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Proceedings of the 32nd DAGM conference on Pattern recognition
Belief propagation for improved color assessment in structured light
Proceedings of the 32nd DAGM conference on Pattern recognition
Scalable multi-dimensional user intent identification using tree structured distributions
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Max margin learning on domain-independent web information extraction
Proceedings of the 20th ACM international conference on Information and knowledge management
International Journal of Computer Vision
Dual decomposition with many overlapping components
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Computational Statistics & Data Analysis
Efficient ranking in sponsored search
WINE'11 Proceedings of the 7th international conference on Internet and Network Economics
Review of statistical network analysis: models, algorithms, and software
Statistical Analysis and Data Mining
Analysis of Markov Boundary Induction in Bayesian Networks: A New View From Matroid Theory
Fundamenta Informaticae
Training factored PCFGs with expectation propagation
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Approximate MRF inference using bounded treewidth subgraphs
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
The lazy flipper: efficient depth-limited exhaustive search in discrete graphical models
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
Translating related words to videos and back through latent topics
Proceedings of the sixth ACM international conference on Web search and data mining
Full Length Article: Information geometry of target tracking sensor networks
Information Fusion
Image registration using BP-SIFT
Journal of Visual Communication and Image Representation
Training energy-based models for time-series imputation
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Hi-index | 0.00 |
The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building large-scale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical fields, including bioinformatics, communication theory, statistical physics, combinatorial optimization, signal and image processing, information retrieval and statistical machine learning. Many problems that arise in specific instances-including the key problems of computing marginals and modes of probability distributions-are best studied in the general setting. Working with exponential family representations, and exploiting the conjugate duality between the cumulant function and the entropy for exponential families, Graphical Models, Exponential Families and Variational Inference develops general variational representations of the problems of computing likelihoods, marginal probabilities and most probable configurations. It describes how a wide variety of algorithms- among them sum-product, cluster variational methods, expectation-propagation, mean field methods, and max-product-can all be understood in terms of exact or approximate forms of these variational representations. The variational approach provides a complementary alternative to Markov chain Monte Carlo as a general source of approximation methods for inference in large-scale statistical models.