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» Using Learning for Approximation in Stochastic Processes
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NIPS
2000
15 years 3 months ago
Using Free Energies to Represent Q-values in a Multiagent Reinforcement Learning Task
The problem of reinforcement learning in large factored Markov decision processes is explored. The Q-value of a state-action pair is approximated by the free energy of a product o...
Brian Sallans, Geoffrey E. Hinton
ETS
2002
IEEE
78views Hardware» more  ETS 2002»
15 years 2 months ago
Technology in Organizational Learning: Using High Tech for High Touch
This study describes the use of technology to enhance an experiential adult learning process, which occurred in a participatory organizational climate assessment. In this case, co...
Jane B. Maestro-Scherer, Robert E. Rich, Clifford ...
123
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CVPR
2010
IEEE
15 years 10 months ago
Learning from Interpolated Images using Neural Networks for Digital Forensics
Interpolated images have data redundancy, and special correlation exists among neighboring pixels, which is a crucial clue in digital forensics. We design a neural network based f...
Yizhen Huang, Na Fan
DAGM
2010
Springer
15 years 3 months ago
Gaussian Mixture Modeling with Gaussian Process Latent Variable Models
Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristi...
Hannes Nickisch, Carl Edward Rasmussen
SDM
2010
SIAM
200views Data Mining» more  SDM 2010»
15 years 3 months ago
Residual Bayesian Co-clustering for Matrix Approximation
In recent years, matrix approximation for missing value prediction has emerged as an important problem in a variety of domains such as recommendation systems, e-commerce and onlin...
Hanhuai Shan, Arindam Banerjee