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ICML
2009
IEEE
15 years 10 months ago
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
There has been much interest in unsupervised learning of hierarchical generative models such as deep belief networks. Scaling such models to full-sized, high-dimensional images re...
Honglak Lee, Roger Grosse, Rajesh Ranganath, Andre...
CEC
2009
IEEE
15 years 4 months ago
How robot morphology and training order affect the learning of multiple behaviors
— Automatically synthesizing behaviors for robots with articulated bodies poses a number of challenges beyond those encountered when generating behaviors for simpler agents. One ...
Joshua S. Auerbach, Josh C. Bongard
GRAPHICSINTERFACE
2000
14 years 11 months ago
Using a 3D Puzzle as a Metaphor for Learning Spatial Relations
We introduce a new metaphor for learning spatial relations--the 3D puzzle. With this metaphor users learn spatial relations by assembling a geometric model themselves. For this pu...
Bernhard Preim, Felix Ritter, Oliver Deussen, Thom...
ICCV
2011
IEEE
13 years 9 months ago
Adaptive Deconvolutional Networks for Mid and High Level Feature Learning
We present a hierarchical model that learns image decompositions via alternating layers of convolutional sparse coding and max pooling. When trained on natural images, the layers ...
Matthew D. Zeiler, Graham W. Taylor, Rob Fergus
IROS
2008
IEEE
117views Robotics» more  IROS 2008»
15 years 4 months ago
Towards a cognitive robot that uses internal rehearsal to learn affordance relations
—This paper introduces a new approach to develop robots that can learn general affordance relations from their experiences. Our approach is a part of larger efforts to develop a ...
Erdem Erdemir, Carl B. Frankel, Kazuhiko Kawamura,...