Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
An introduction to variational methods for graphical models
Learning in graphical models
Learning Probabilistic Models of Relational Structure
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Learning and Inferring Image Segmentations using the GBP Typical Cut Algorithm
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Learning Object Shape: From Drawings to Images
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Learning probabilistic relational models
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Convexifying the Bethe free energy
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Towards an integrated protein-protein interaction network
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
Constructing free-energy approximations and generalized belief propagation algorithms
IEEE Transactions on Information Theory
A new class of upper bounds on the log partition function
IEEE Transactions on Information Theory
Turbo decoding as an instance of Pearl's “belief propagation” algorithm
IEEE Journal on Selected Areas in Communications
libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models
The Journal of Machine Learning Research
DARWIN: a framework for machine learning and computer vision research and development
The Journal of Machine Learning Research
Hi-index | 0.00 |
The FastInf C++ library is designed to perform memory and time efficient approximate inference in large-scale discrete undirected graphical models. The focus of the library is propagation based approximate inference methods, ranging from the basic loopy belief propagation algorithm to propagation based on convex free energies. Various message scheduling schemes that improve on the standard synchronous or asynchronous approaches are included. Also implemented are a clique tree based exact inference, Gibbs sampling, and the mean field algorithm. In addition to inference, FastInf provides parameter estimation capabilities as well as representation and learning of shared parameters. It offers a rich interface that facilitates extension of the basic classes to other inference and learning methods.