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» Learning Generative Models with the Up-Propagation Algorithm
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BMCBI
2011
12 years 11 months ago
Learning sparse models for a dynamic Bayesian network classifier of protein secondary structure
Background: Protein secondary structure prediction provides insight into protein function and is a valuable preliminary step for predicting the 3D structure of a protein. Dynamic ...
Zafer Aydin, Ajit Singh, Jeff Bilmes, William Staf...
NAACL
2004
13 years 9 months ago
Catching the Drift: Probabilistic Content Models, with Applications to Generation and Summarization
We consider the problem of modeling the content structure of texts within a specific domain, in terms of the topics the texts address and the order in which these topics appear. W...
Regina Barzilay, Lillian Lee
ICML
2002
IEEE
14 years 8 months ago
Algorithm-Directed Exploration for Model-Based Reinforcement Learning in Factored MDPs
One of the central challenges in reinforcement learning is to balance the exploration/exploitation tradeoff while scaling up to large problems. Although model-based reinforcement ...
Carlos Guestrin, Relu Patrascu, Dale Schuurmans
SCVMA
2004
Springer
14 years 1 months ago
A Generative Model of Dense Optical Flow in Layers
We introduce a generative model of dense flow fields within a layered representation of 3-dimensional scenes. Using probabilistic inference and learning techniques (namely, varia...
Anitha Kannan, Brendan J. Frey, Nebojsa Jojic
SAT
2004
Springer
88views Hardware» more  SAT 2004»
14 years 1 months ago
Improving First-order Model Searching by Propositional Reasoning and Lemma Learning
The finite model generation problem in the first-order logic is a generalization of the propositional satisfiability (SAT) problem. An essential algorithm for solving the proble...
Zhuo Huang, Hantao Zhang, Jian Zhang