Training Machine Learning models at the Edge: A Survey
Edge Computing (EC) has gained significant traction in recent years, promising enhanced
efficiency by integrating Artificial Intelligence (AI) capabilities at the edge. While the focus …
efficiency by integrating Artificial Intelligence (AI) capabilities at the edge. While the focus …
LifeLearner: Hardware-Aware Meta Continual Learning System for Embedded Computing Platforms
Continual Learning (CL) allows applications such as user personalization and household
robots to learn on the fly and adapt to context. This is an important feature when context …
robots to learn on the fly and adapt to context. This is an important feature when context …
On-Device Domain Learning for Keyword Spotting on Low-Power Extreme Edge Embedded Systems
Keyword spotting accuracy degrades when neural networks are exposed to noisy
environments. On-site adaptation to previously unseen noise is crucial to recovering …
environments. On-site adaptation to previously unseen noise is crucial to recovering …
Few-Shot Class-Incremental Audio Classification With Adaptive Mitigation of Forgetting and Overfitting
Few-shot Class-incremental Audio Classification (FCAC) is a task to continuously identify
incremental classes with only few training samples after training the model on base classes …
incremental classes with only few training samples after training the model on base classes …
[PDF][PDF] Third Year Report
YD Kwon - 2023 - theyoungkwon.github.io
2. Background. This chapter describes the relevant research in more details in the areas of
on-device ML and CL to discuss the necessity, novelty, and contributions of this thesis. 3 …
on-device ML and CL to discuss the necessity, novelty, and contributions of this thesis. 3 …