Towards Self-Supervised Learning @ the Edge
Talk, CS Seminar Series, Chicago, Illinois
In the realm of deploying machine learning (ML) algorithms on edge devices, a plethora of challenges surfaces, ranging from compute resource limitations to the nuanced intricacies associated with acquiring dynamic data distributions. The majority of extant ML algorithms implemented on edge cyberinfrastructures undergo training with datasets that inadequately capture the true essence of the data streams collected at the edge. This presentation delves into an investigation of the viability of novel self-supervised learning algorithms to effectively characterize insufficiently curated, imbalanced, and unlabeled datasets in the context of edge computing. The primary focus is directed towards their potential applicability in addressing the unique challenges posed by edge environments. Two specific applications will be presented in this talk: Atmospheric Conditions Characterizations and Ecosystem Activity Monitoring. Both instances exemplify the autonomous characterization capabilities facilitated by the proposed self-supervised learning algorithms, obviating the need for human intervention in the process. Through these case studies, insights into the transformative potential of self-supervised learning in mitigating the challenges associated with deploying ML algorithms on edge devices will be elucidated.