32 citations · 60 across the 7 of their papers we have counts for
8 papers
On-Device Continual Learning for Unsupervised Visual Anomaly Detection in Dynamic Manufacturing
Haoyu Ren, Kay Koehle, Kirill Dorofeev +1
In modern manufacturing, Visual Anomaly Detection (VAD) is essential for automated inspection and consistent product quality. Yet, increasingly dynamic and flexible production envi…
On-device Online Learning and Semantic Management of TinyML Systems
Haoyu Ren, Xue Li, Darko Anicic +1
Recent advances in Tiny Machine Learning (TinyML) empower low-footprint embedded devices for real-time on-device Machine Learning. While many acknowledge the potential benefits of…
TinyMetaFed: Efficient Federated Meta-Learning for TinyML
Haoyu Ren, Xue Li, Darko Anicic +1
The field of Tiny Machine Learning (TinyML) has made substantial advancements in democratizing machine learning on low-footprint devices, such as microcontrollers. The prevalence o…
TinyReptile: TinyML with Federated Meta-Learning
Haoyu Ren, Darko Anicic, Thomas A. Runkler
Tiny machine learning (TinyML) is a rapidly growing field aiming to democratize machine learning (ML) for resource-constrained microcontrollers (MCUs). Given the pervasiveness of t…
SeLoC-ML: Semantic Low-Code Engineering for Machine Learning Applications in Industrial IoT
Haoyu Ren, Kirill Dorofeev, Darko Anicic +3
Internet of Things (IoT) is transforming the industry by bridging the gap between Information Technology (IT) and Operational Technology (OT). Machines are being integrated with co…
How to Manage Tiny Machine Learning at Scale: An Industrial Perspective
Haoyu Ren, Darko Anicic, Thomas Runkler
Tiny machine learning (TinyML) has gained widespread popularity where machine learning (ML) is democratized on ubiquitous microcontrollers, processing sensor data everywhere in rea…