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» Approximate Learning of Dynamic Models
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JAIR
2007
127views more  JAIR 2007»
15 years 4 months ago
Learning Symbolic Models of Stochastic Domains
In this article, we work towards the goal of developing agents that can learn to act in complex worlds. We develop a a new probabilistic planning rule representation to compactly ...
Hanna M. Pasula, Luke S. Zettlemoyer, Leslie Pack ...
NIPS
2007
15 years 6 months ago
Distributed Inference for Latent Dirichlet Allocation
We investigate the problem of learning a widely-used latent-variable model – the Latent Dirichlet Allocation (LDA) or “topic” model – using distributed computation, where ...
David Newman, Arthur Asuncion, Padhraic Smyth, Max...
EDUTAINMENT
2006
Springer
15 years 8 months ago
Research of Dynamic Terrain in Complex Battlefield Environments
In this paper, we present a novel method for dynamic terrain in battlefield and an efficient plan to simulate crater in the battle. We explore a few methods for dynamic terrain sur...
Xingquan Cai, Fengxia Li, Haiyan Sun, Shouyi Zhan
WSC
2008
15 years 7 months ago
Evaluating the transient behavior of queueing systems via simulation and transfer function modeling
Characterizing the transient behavior of queueing systems is a difficult problem, which has been addressed by either simplified analytical models or simulation. We seek to capture...
Jingang Liu, Feng Yang
ML
2012
ACM
413views Machine Learning» more  ML 2012»
14 years 10 days ago
Gradient-based boosting for statistical relational learning: The relational dependency network case
Dependency networks approximate a joint probability distribution over multiple random variables as a product of conditional distributions. Relational Dependency Networks (RDNs) are...
Sriraam Natarajan, Tushar Khot, Kristian Kersting,...