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» Bottom-Up Learning of Markov Network Structure
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ICDAR
2007
IEEE
14 years 2 days ago
Energy-Based Models in Document Recognition and Computer Vision
The Machine Learning and Pattern Recognition communities are facing two challenges: solving the normalization problem, and solving the deep learning problem. The normalization pro...
Yann LeCun, Sumit Chopra, Marc'Aurelio Ranzato, Fu...
IJCNN
2008
IEEE
14 years 4 days ago
Hybrid learning architecture for unobtrusive infrared tracking support
—The system architecture presented in this paper is designed for helping an aged person to live longer independently in their own home by detecting unusual and potentially hazard...
K. K. Kiran Bhagat, Stefan Wermter, Kevin Burn
JMLR
2010
140views more  JMLR 2010»
13 years 16 days ago
Mean Field Variational Approximation for Continuous-Time Bayesian Networks
Continuous-time Bayesian networks is a natural structured representation language for multicomponent stochastic processes that evolve continuously over time. Despite the compact r...
Ido Cohn, Tal El-Hay, Nir Friedman, Raz Kupferman
ICASSP
2008
IEEE
14 years 5 days ago
Bayesian update of dialogue state for robust dialogue systems
This paper presents a new framework for accumulating beliefs in spoken dialogue systems. The technique is based on updating a Bayesian Network that represents the underlying state...
Blaise Thomson, Jost Schatzmann, Steve Young
RECOMB
2007
Springer
14 years 6 months ago
A Feature-Based Approach to Modeling Protein-DNA Interactions
Transcription factor (TF) binding to its DNA target site is a fundamental regulatory interaction. The most common model used to represent TF binding specificities is a position spe...
Eilon Sharon, Eran Segal