Thurs (1/12): Steven Pinker on Language Models
Mark Gluck
netlist at psych.Stanford.EDU
Sun Jan 22 18:16:33 EST 1989
Stanford University Interdisciplinary Colloquium Series:
Adaptive Networks and their Applications
Jan. 24th (Tuesday, 3:30pm):
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Learning by Assertion: Calibrating a Simple Visual System
LARRY MALONEY
Deptartment of Psychology
6 Washington Place; 8th Floor
New York University
New York, NY 10003
email: ltm at xp.psych.nyu.edu
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Abstract
An ideal visual system is calibrated if its estimates reflect the
actual state of the scene: Straight lines, for example, should be judged
to be straight. If an ideal visual system is modeled as a neural network,
then it is calibrated only if the weights linking elements of the
the network are assigned correct values. I describe a method
(`Learning by Assertion') for calibrating an ideal visual system by
adjusting the weights. The method requires no explicit feedback or prior
knowledge concerning the contents of the environment. This work is
relevant to biological visual development and calibration, to the
calibration of machine vision systems, and to the design of adaptive
network algorithms.
Additional Information
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Location: Room 380-380F, which can be reached through the lower level
between the Psychology and Mathematical Sciences buildings.
Technical Level: These talks will be technically oriented and are intended
for persons actively working in related areas. They are not intended
for the newcomer seeking general introductory material.
Mailing lists: To be added to the network mailing list, netmail to
netlist at psych.stanford.edu. For additional information, or contact
Mark Gluck (gluck at psych.stanford.edu).
Co-Sponsored by: Departments of Electrical Engineering (B. Widrow) and
Psychology (D. Rumelhart, M. Pavel, M. Gluck), Stanford Univ.
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