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NIPS
2003
15 years 8 months ago
Gaussian Processes in Reinforcement Learning
We exploit some useful properties of Gaussian process (GP) regression models for reinforcement learning in continuous state spaces and discrete time. We demonstrate how the GP mod...
Carl Edward Rasmussen, Malte Kuss
166
Voted
CORR
2010
Springer
135views Education» more  CORR 2010»
15 years 7 months ago
Balanced distribution-energy inequalities and related entropy bounds
Let A be a self-adjoint operator acting over a space X endowed with a partition. We give lower bounds on the energy of a mixed state from its distribution in the partition and the...
Michel Rumin
EMNLP
2010
15 years 4 months ago
Crouching Dirichlet, Hidden Markov Model: Unsupervised POS Tagging with Context Local Tag Generation
We define the crouching Dirichlet, hidden Markov model (CDHMM), an HMM for partof-speech tagging which draws state prior distributions for each local document context. This simple...
Taesun Moon, Katrin Erk, Jason Baldridge
TSP
2010
15 years 1 months ago
Joint detection and estimation of multiple objects from image observations
The problem of jointly detecting multiple objects and estimating their states from image observations is formulated in a Bayesian framework by modeling the collection of states as ...
Ba-Ngu Vo, Ba-Tuong Vo, Nam-Trung Pham, David Sute...
188
Voted
ASMTA
2008
Springer
167views Mathematics» more  ASMTA 2008»
15 years 8 months ago
Perfect Simulation of Stochastic Automata Networks
The solution of continuous and discrete-time Markovian models is still challenging mainly when we model large complex systems, for example, to obtain performance indexes of paralle...
Paulo Fernandes, Jean-Marc Vincent, Thais Webber