We present a connectionist method for representing images that explicitlyaddresses their hierarchicalnature. It blends data fromneuroscience about whole-object viewpoint sensitive...
The deep Boltzmann machine is a powerful model that extracts the hierarchical structure of observed data. While inference is typically slow due to its undirected nature, we argue ...
The human capability of recognizing objects visually is here held to be a function emerging as result of interactions between epigenetic influences and basic neural plasticity mec...
In this paper, we concentrate on the expressive power of hierarchical structures in neural networks. Recently, the so-called SplitNet model was introduced. It develops a dynamic n...
Recruitment learning in hierarchies is an inherently unstable process (Valiant, 1994). This paper presents conditions on parameters for a feedforward network to ensure stable recru...