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» Unsupervised Learning of Part-Based Representations
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IJCNN
2006
IEEE
15 years 3 months ago
In-Place Learning for Positional and Scale Invariance
— In-place learning is a biologically inspired concept, meaning that the computational network is responsible for its own learning. With in-place learning, there is no need for a...
Juyang Weng, Hong Lu, Tianyu Luwang, Xiangyang Xue
ICANN
2010
Springer
14 years 10 months ago
A Bilinear Model for Consistent Topographic Representations
Visual recognition faces the difficult problem of recognizing objects despite the multitude of their appearances. Ample neuroscientific evidence shows that the cortex uses a topogr...
Urs Bergmann, Christoph von der Malsburg
BC
2000
113views more  BC 2000»
14 years 9 months ago
Spatial cognition and neuro-mimetic navigation: a model of hippocampal place cell activity
Abstract. A computational model of hippocampal activity during spatial cognition and navigation tasks is presented. The spatial representation in our model of the rat hippocampus i...
Angelo Arleo, Wulfram Gerstner
EELC
2006
128views Languages» more  EELC 2006»
15 years 1 months ago
Evolving Distributed Representations for Language with Self-Organizing Maps
We present a neural-competitive learning model of language evolution in which several symbol sequences compete to signify a given propositional meaning. Both symbol sequences and p...
Simon D. Levy, Simon Kirby
93
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NIPS
2003
14 years 11 months ago
Unsupervised Context Sensitive Language Acquisition from a Large Corpus
We describe a pattern acquisition algorithm that learns, in an unsupervised fashion, a streamlined representation of linguistic structures from a plain natural-language corpus. Th...
Zach Solan, David Horn, Eytan Ruppin, Shimon Edelm...