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» Observational Learning with Modular Networks
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NIPS
2008
13 years 9 months ago
Natural Image Denoising with Convolutional Networks
We present an approach to low-level vision that combines two main ideas: the use of convolutional networks as an image processing architecture and an unsupervised learning procedu...
Viren Jain, H. Sebastian Seung
ESANN
2007
13 years 9 months ago
How to process uncertainty in machine learning?
Uncertainty is a popular phenomenon in machine learning and a variety of methods to model uncertainty at different levels has been developed. The aim of this paper is to motivate ...
Barbara Hammer, Thomas Villmann
NECO
2007
150views more  NECO 2007»
13 years 7 months ago
Reinforcement Learning, Spike-Time-Dependent Plasticity, and the BCM Rule
Learning agents, whether natural or artificial, must update their internal parameters in order to improve their behavior over time. In reinforcement learning, this plasticity is ...
Dorit Baras, Ron Meir
SBIA
2004
Springer
14 years 1 months ago
Learning with Drift Detection
Abstract. Most of the work in machine learning assume that examples are generated at random according to some stationary probability distribution. In this work we study the problem...
João Gama, Pedro Medas, Gladys Castillo, Pe...
DAGM
2003
Springer
14 years 26 days ago
Learning Human-Like Opponent Behavior for Interactive Computer Games
Compared to their ancestors in the early 1970s, present day computer games are of incredible complexity and show magnificent graphical performance. However, in programming intelli...
Christian Bauckhage, Christian Thurau, Gerhard Sag...