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ICML
2008
IEEE
14 years 5 months ago
Efficiently learning linear-linear exponential family predictive representations of state
Exponential Family PSR (EFPSR) models capture stochastic dynamical systems by representing state as the parameters of an exponential family distribution over a shortterm window of...
David Wingate, Satinder P. Singh
NIPS
2003
13 years 6 months ago
Inferring State Sequences for Non-linear Systems with Embedded Hidden Markov Models
We describe a Markov chain method for sampling from the distribution of the hidden state sequence in a non-linear dynamical system, given a sequence of observations. This method u...
Radford M. Neal, Matthew J. Beal, Sam T. Roweis
CCGRID
2010
IEEE
12 years 8 months ago
WORKEM: Representing and Emulating Distributed Scientific Workflow Execution State
- Scientific workflows have become an integral part of cyberinfrastructure as their computational complexity and data sizes have grown. However, the complexity of the distributed i...
Lavanya Ramakrishnan, Dennis Gannon, Beth Plale
DAWAK
2007
Springer
13 years 8 months ago
A UML Profile for Representing Business Object States in a Data Warehouse
Data Warehouse (DWH) systems allow to analyze business objects relevant to an enterprise organization (e.g., orders or customers). Analysts are interested in the states of these bu...
Veronika Stefanov, Beate List
CSL
2010
Springer
13 years 4 months ago
The Hidden Information State model: A practical framework for POMDP-based spoken dialogue management
This paper explains how Partially Observable Markov Decision Processes (POMDPs) can provide a principled mathematical framework for modelling the inherent uncertainty in spoken di...
Steve Young, Milica Gasic, Simon Keizer, Fran&cced...