UR2M: Uncertainty and Resource-Aware Event Detection on Microcontrollers

arXiv:2402.09264v1 Announce Type: new
Abstract: Traditional machine learning techniques are prone to generating inaccurate predictions when confronted with shifts in the distribution of data between the training and testing phases. This vulnerability can lead to severe consequences, especially in applications such as mobile healthcare. Uncertainty estimation has the potential to mitigate this issue by assessing the reliability of a model's output. However, existing uncertainty estimation techniques often require substantial computational resources and memory, making them impractical for implementation on microcontrollers …

abstract applications arxiv consequences cs.hc cs.lg data detection distribution event healthcare machine machine learning machine learning techniques microcontrollers mobile predictions testing traditional machine learning training type uncertainty vulnerability