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GECCO
2006
Springer
208views Optimization» more  GECCO 2006»
13 years 11 months ago
Comparing evolutionary and temporal difference methods in a reinforcement learning domain
Both genetic algorithms (GAs) and temporal difference (TD) methods have proven effective at solving reinforcement learning (RL) problems. However, since few rigorous empirical com...
Matthew E. Taylor, Shimon Whiteson, Peter Stone
AAAI
2007
13 years 10 months ago
A Connectionist Cognitive Model for Temporal Synchronisation and Learning
The importance of the efforts towards integrating the symbolic and connectionist paradigms of artificial intelligence has been widely recognised. Integration may lead to more e...
Luís C. Lamb, Rafael V. Borges, Artur S. d'...
NIPS
1996
13 years 9 months ago
Why did TD-Gammon Work?
Although TD-Gammon is one of the major successes in machine learning, it has not led to similar impressive breakthroughs in temporal difference learning for other applications or ...
Jordan B. Pollack, Alan D. Blair
CVPR
2005
IEEE
14 years 9 months ago
Learning Appearance Manifolds from Video
The appearance of dynamic scenes is often largely governed by a latent low-dimensional dynamic process. We show how to learn a mapping from video frames to this lowdimensional rep...
Ali Rahimi, Ben Recht, Trevor Darrell
ACL
2012
11 years 10 months ago
Labeling Documents with Timestamps: Learning from their Time Expressions
Temporal reasoners for document understanding typically assume that a document’s creation date is known. Algorithms to ground relative time expressions and order events often re...
Nathanael Chambers