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Creators/Authors contains: "Goldman, Gil"

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  1. ; ; ; ; ; (, ACM)
    This paper describes how a system that performs Live Learning can be modified to use radar rather than visible light. The pipelined and iterative machine learning (ML) workflow of this system can operate at low network bandwidths for selective transmission of rare unlabeled events embedded in high-bandwidth real-time sensor data. While radar offers greater range and can overcome the severe signal attenuation experienced by visible light under conditions such as rain or fog, it poses a number of challenges for ML. This paper describes how these challenges can be overcome for Live Learning, and identifies some future directions for research. 
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    Free, publicly-accessible full text available February 26, 2026