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Ph.D. Proposal Oral Exam - Beomseok Kang

Title:  Lightweight, Data Efficient, and Robust Deep Learning for Multi-agent Systems

Committee: 

Dr. Mukhopadhyay, Advisor   

Dr. Romberg, Chair

Dr. Hao

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Summary

The objective of the proposed research is learning the intelligent tasks for multi-agent systems with the limited amount of training data even under the imperfect environments such as the presence of stochastic interaction and resource-constrained scenarios. The proposed research is organized into three tasks: (1) learning the representation of spatial structures in the systems, (2) learning the interaction between agents, and (3) learning the representation and interaction in evolving systems. We present novel learning algorithms and network designs for multi-agent systems at the various scales, ranging from few to thousands agents.

Details

Friday
Nov 3 2023
04:00pm - 06:00pm

Location: Room 3126, Klaus

In campus calendar: No

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  • Workflow Status:Published
  • Created By:Daniela Staiculescu
  • Created:11/02/2023
  • Modified By:Daniela Staiculescu
  • Modified:11/02/2023

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