Boulder Computational Learning Group
Summer 1998 Schedule

Unless otherwise noted, meeting time is on Mondays, 1-3 p.m., in ECOT 831a (Engineering center, 8th floor, small conference room). Dates in red are at the usual time and place. Dates in blue are special meeting dates.

Schedules from previous semesters

June 19, 10:30, NCAR, Foothills Laboratory, Bldg 2, Room 1001: Tosh Munakata, Cleveland State

The State of AI: A Practical Perspective

The speaker has been active in the areas of practical applications of AI. For example, he served as Guest Editor for two well- received special issues on commercial and industrial AI for the Communications of the ACM (March 1994 and November 1995), and is currently the Chair of the Rough Control Group. Based on his extensive experience, Dr. Munakata will present a seminar that consists of the following two parts:

Part I. Macroscopic overview of the current state and future perspectives of practical AI.

This part includes a quick review of the areas the CACM issues cover, some common characteristics of practical AI, and some forecasts for the near and distant future.

Part II. A Tutorial on Rough Sets and Rough Control (if time allows)

A major feature of rough set theory in terms of practical applications is the classification of empirical data and subsequent decision making. There has also been much recent interest in rough control: applications of rough set theory to control problems.

July 20, 3p.m., ECOT 831: Nicol Schraudolph, IDSIA

Accelerated Gradient Descent by Factor-Centering Decomposition

Gradient factor centering is a new methodology for decomposing neural networks into biased and centered subnets which are then trained in parallel. The decomposition can be applied to any pattern-dependent factor in the network's gradient, and is designed such that the subnets are more amenable to optimization by gradient descent than the original network: biased subnets because of their simplified architecture, centered subnets due to a modified gradient that improves conditioning.

The architectural and algorithmic modifications mandated by this approach include both familiar and novel elements, often in prescribed combinations. The framework suggests for instance that shortcut connections --- a well-known architectural feature --- should work best in conjunction with slope centering, a new technique described herein. Our benchmark experiments bear out this prediction, and show that factor-centering decomposition can speed up learning significantly without adversely affecting the trained network's generalization ability.