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Adaptive Mixtures of Local Experts.

Robert A Jacobs1, Michael I Jordan1, Steven J Nowlan2

  • 1Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139 USA.

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Summary
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A novel supervised learning method uses modular networks to divide tasks, linking multilayer and competitive learning approaches. This system successfully partitions a vowel discrimination task for simple expert networks to solve.

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Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Computational Neuroscience

Background:

  • Supervised learning typically involves single, complex networks.
  • Existing methods like multilayer networks and competitive learning have distinct mechanisms.
  • A need exists for integrated approaches that leverage modularity.

Purpose of the Study:

  • Introduce a new supervised learning procedure for systems with multiple separate networks.
  • Establish a connection between modular supervised networks and associative competitive learning.
  • Demonstrate the efficacy of this procedure in task decomposition.

Main Methods:

  • Developed a supervised learning procedure for distributed network systems.
  • Each network is trained on a subset of the data.
  • The procedure integrates aspects of multilayer and competitive learning paradigms.

Main Results:

  • The proposed learning procedure effectively divides complex tasks into simpler subtasks.
  • Demonstrated successful application to a vowel discrimination task.
  • Showcased that each subtask can be solved by a simple expert network.

Conclusions:

  • The new procedure offers a modular approach to supervised learning.
  • It bridges the gap between multilayer and competitive learning strategies.
  • This method enables efficient task specialization within a network system.