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ICANN
2001
Springer
14 years 6 days ago
Market-Based Reinforcement Learning in Partially Observable Worlds
Unlike traditional reinforcement learning (RL), market-based RL is in principle applicable to worlds described by partially observable Markov Decision Processes (POMDPs), where an ...
Ivo Kwee, Marcus Hutter, Jürgen Schmidhuber
NIPS
2008
13 years 9 months ago
Weighted Sums of Random Kitchen Sinks: Replacing minimization with randomization in learning
Randomized neural networks are immortalized in this well-known AI Koan: In the days when Sussman was a novice, Minsky once came to him as he sat hacking at the PDP-6. "What a...
Ali Rahimi, Benjamin Recht
ICML
2007
IEEE
14 years 8 months ago
Recovering temporally rewiring networks: a model-based approach
A plausible representation of relational information among entities in dynamic systems such as a living cell or a social community is a stochastic network which is topologically r...
Fan Guo, Steve Hanneke, Wenjie Fu, Eric P. Xing
WIOPT
2011
IEEE
12 years 11 months ago
Network utility maximization over partially observable Markovian channels
Abstract—This paper considers maximizing throughput utility in a multi-user network with partially observable Markov ON/OFF channels. Instantaneous channel states are never known...
Chih-Ping Li, Michael J. Neely
ICCV
2007
IEEE
14 years 9 months ago
Stochastic Adaptive Tracking In A Camera Network
We present a novel stochastic, adaptive strategy for tracking multiple people in a large network of video cameras. Similarities between features (appearance and biometrics) observ...
Bi Song, Amit K. Roy Chowdhury