We report results of an interdisciplinary project which aims at endowing a real robot system with the capacity for learning by goaldirected imitation. The control architecture is b...
Wolfram Erlhagen, Albert Mukovskiy, Estela Bicho, ...
This paper proposes novel hierarchical self-organizing associative memory architecture for machine learning. This memory architecture is characterized with sparse and local interco...
Abstract. A key problem in designing artificial neural networks for visual object recognition tasks is the proper choice of the network architecture. Evolutionary optimization met...
Georg Schneider, Heiko Wersing, Bernhard Sendhoff,...
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...
Community detection in social networks varying with time is a common yet challenging problem whereby efficient visualization of evolving relationships and implicit hierarchical s...