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KDD
2009
ACM
230views Data Mining» more  KDD 2009»
15 years 9 months ago
Grouped graphical Granger modeling methods for temporal causal modeling
We develop and evaluate an approach to causal modeling based on time series data, collectively referred to as“grouped graphical Granger modeling methods.” Graphical Granger mo...
Aurelie C. Lozano, Naoki Abe, Yan Liu, Saharon Ros...
ICML
2007
IEEE
16 years 5 months ago
Conditional random fields for multi-agent reinforcement learning
Conditional random fields (CRFs) are graphical models for modeling the probability of labels given the observations. They have traditionally been trained with using a set of obser...
Xinhua Zhang, Douglas Aberdeen, S. V. N. Vishwanat...
CPAIOR
2009
Springer
15 years 11 months ago
Learning How to Propagate Using Random Probing
Abstract. In constraint programming there are often many choices regarding the propagation method to be used on the constraints of a problem. However, simple constraint solvers usu...
Efstathios Stamatatos, Kostas Stergiou
CVPR
2005
IEEE
16 years 7 months ago
Fields of Experts: A Framework for Learning Image Priors
We develop a framework for learning generic, expressive image priors that capture the statistics of natural scenes and can be used for a variety of machine vision tasks. The appro...
Stefan Roth, Michael J. Black
CORR
2010
Springer
116views Education» more  CORR 2010»
15 years 1 days ago
Mixed-Membership Stochastic Block-Models for Transactional Networks
Abstract: Transactional network data can be thought of as a list of oneto-many communications (e.g., email) between nodes in a social network. Most social network models convert th...
Mahdi Shafiei, Hugh Chipman