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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
BC
2002
193views more  BC 2002»
13 years 7 months ago
Resonant spatiotemporal learning in large random recurrent networks
Taking a global analogy with the structure of perceptual biological systems, we present a system composed of two layers of real-valued sigmoidal neurons. The primary layer receives...
Emmanuel Daucé, Mathias Quoy, Bernard Doyon
JKM
2006
135views more  JKM 2006»
13 years 7 months ago
Learning from the Mars Rover Mission: scientific discovery, learning and memory
Purpose Knowledge management for space exploration is part of a multi-generational effort. Each mission builds on knowledge from prior missions, and learning is the first step in ...
Charlotte Linde
DIS
2009
Springer
14 years 2 months ago
An Iterative Learning Algorithm for Within-Network Regression in the Transductive Setting
Within-network regression addresses the task of regression in partially labeled networked data where labels are sparse and continuous. Data for inference consist of entities associ...
Annalisa Appice, Michelangelo Ceci, Donato Malerba
CVPR
1997
IEEE
13 years 12 months ago
Learning bilinear models for two-factor problems in vision
In many vision problems, we want to infer two (or more) hidden factors which interact to produce our observations. We may want to disentangle illuminant and object colors in color...
William T. Freeman, Joshua B. Tenenbaum