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CCE
2004
13 years 10 months ago
An algorithmic framework for improving heuristic solutions: Part II. A new version of the stochastic traveling salesman problem
The algorithmic framework developed for improving heuristic solutions of the new version of deterministic TSP [Choi et al., 2002] is extended to the stochastic case. To verify the...
Jaein Choi, Jay H. Lee, Matthew J. Realff
ICASSP
2011
IEEE
13 years 2 months ago
Multi-view and multi-objective semi-supervised learning for large vocabulary continuous speech recognition
Current hidden Markov acoustic modeling for large vocabulary continuous speech recognition (LVCSR) relies on the availability of abundant labeled transcriptions. Given that speech...
Xiaodong Cui, Jing Huang, Jen-Tzung Chien
AIPS
2007
14 years 21 days ago
Learning to Plan Using Harmonic Analysis of Diffusion Models
This paper summarizes research on a new emerging framework for learning to plan using the Markov decision process model (MDP). In this paradigm, two approaches to learning to plan...
Sridhar Mahadevan, Sarah Osentoski, Jeffrey Johns,...
TSP
2008
101views more  TSP 2008»
13 years 10 months ago
Optimal Node Density for Detection in Energy-Constrained Random Networks
The problem of optimal node density maximizing the Neyman-Pearson detection error exponent subject to a constraint on average (per node) energy consumption is analyzed. The spatial...
Animashree Anandkumar, Lang Tong, Ananthram Swami
CODES
2009
IEEE
14 years 1 months ago
An MDP-based application oriented optimal policy for wireless sensor networks
Technological advancements due to Moore’s law have led to the proliferation of complex wireless sensor network (WSN) domains. One commonality across all WSN domains is the need ...
Arslan Munir, Ann Gordon-Ross