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ICML
1994
IEEE
15 years 8 months ago
Learning Without State-Estimation in Partially Observable Markovian Decision Processes
Reinforcement learning (RL) algorithms provide a sound theoretical basis for building learning control architectures for embedded agents. Unfortunately all of the theory and much ...
Satinder P. Singh, Tommi Jaakkola, Michael I. Jord...
NIPS
2008
15 years 6 months ago
Extracting State Transition Dynamics from Multiple Spike Trains with Correlated Poisson HMM
Neural activity is non-stationary and varies across time. Hidden Markov Models (HMMs) have been used to track the state transition among quasi-stationary discrete neural states. W...
Kentaro Katahira, Jun Nishikawa, Kazuo Okanoya, Ma...
GECCO
2008
Springer
118views Optimization» more  GECCO 2008»
15 years 6 months ago
Unsupervised learning of echo state networks: balancing the double pole
A possible alternative to fine topology tuning for Neural Network (NN) optimization is to use Echo State Networks (ESNs), recurrent NNs built upon a large reservoir of sparsely r...
Fei Jiang, Hugues Berry, Marc Schoenauer
INFORMATICALT
2006
101views more  INFORMATICALT 2006»
15 years 5 months ago
Determination of Stress Strain State Components of Butt Welded Joint with Mild Interlayer Subjected to Elasto-Plastic Tension
Abstract. Determination of stress strain state components of butt welded joint with a mild interlayer at elasto-plastic tension is presented in this paper. Function of normal trans...
Algis Brazenas, Dainius Vaiciulis, Vytautas Kleiza
159
Voted
PAMI
2008
189views more  PAMI 2008»
15 years 5 months ago
Detecting Objects of Variable Shape Structure With Hidden State Shape Models
This paper proposes a method for detecting object classes that exhibit variable shape structure in heavily cluttered images. The term "variable shape structure" is used t...
Jingbin Wang, Vassilis Athitsos, Stan Sclaroff, Ma...