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ICMLA
2009
14 years 9 months ago
Learning Deep Neural Networks for High Dimensional Output Problems
State-of-the-art pattern recognition methods have difficulty dealing with problems where the dimension of the output space is large. In this article, we propose a new framework ba...
Benjamin Labbé, Romain Hérault, Cl&e...
SIAMSC
2010
159views more  SIAMSC 2010»
14 years 10 months ago
Parameter and State Model Reduction for Large-Scale Statistical Inverse Problems
A greedy algorithm for the construction of a reduced model with reduction in both parameter and state is developed for efficient solution of statistical inverse problems governed b...
Chad Lieberman, Karen Willcox, Omar Ghattas
70
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JETAI
2006
63views more  JETAI 2006»
14 years 12 months ago
Principled exploitation of behavioural coupling
In robot building, attention tends to focus on internal signal processing and the way desired motor signals are generated. But equally important is the selection and configuration...
Chris Thornton
CORR
2010
Springer
95views Education» more  CORR 2010»
14 years 9 months ago
When are feedforward microcircuits well-modeled by maximum entropy methods?
Describing the collective activity of neural populations is a daunting task: the number of possible patterns grows exponentially with the number of cells, resulting in practically...
Andrea K. Barreiro, Julijana Gjorgjieva, Fred Riek...
IJCAI
1997
15 years 1 months ago
Mental Tracking: A Computational Model of Spatial Development
Psychological experiments on children's development of spatial knowledge suggest experience at self-locomotion with visual tracking as important factors. Yet, the mechanism u...
Kazuo Hiraki, Akio Sashima, Steven Phillips