(Tues. 1/24): Larry Maloney on Visual Calibration

[Mark Gluck]

            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
                          ----------------------

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.