MIT Department of Electrical Engineering & Computer Science
Sparse Representations for Fast, One-Shot Learning
Kenneth Yip
Artificial Intelligence Laboratory, MIT
Monday, May 5, 1997
3:00 PM (2:45 refreshments)
Room NE43-518
EECS Special Seminar
Abstract
Humans rapidly and reliably learn many kinds of regularities and
generalizations. We propose a novel model of fast learning that
exploits the properties of sparse representations and the constraints
imposed by a plausible hardware mechanism. To demonstrate our
approach we describe a computational model of acquisition in the
domain of morphophonology. We encapsulate phonological information as
bidirectional boolean constraint relations operating on the classical
linguistic representations of speech sounds in term of distinctive
features. The performance model is described as a hardware mechanism
that incrementally enforces the constraints. Phonological behavior
arises from the action of this mechanism. Constraints are induced
from a corpus of common English nouns and verbs. The induction
algorithm compiles the corpus into increasingly sophisticated
constraints. The algorithm yields one-shot learning from a few
examples. Our model has been implemented as a computer program. The
program exhibits phonological behavior similar to that of young
children. As a bonus the constraints that are acquired can be
interpreted as classical linguistic rules.
Hosts: Prof. G. Sussman and Prof. R. Berwick
URL of this page:
http://www-eecs.mit.edu/AY96-97/events/49.html
Created: Apr 29, 1997
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Modified: Jun 24, 1997
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