TinyProp - Adaptive Sparse Backpropagation for Efficient TinyML On-device Learning. - Marcus Rüb; Daniel Maier; Daniel Mueller-Gritschneder; Axel Sikora (CoRR, 2023)

TinyML4D: Scaling Embedded Machine Learning Education in the Developing World. - Brian Plancher; Sebastian Büttrich; Jeremy Ellis; Neena Goveas; Laila D. Kazimierski; Jesús Alfonso López Sotelo; Milan Lukic; Diego Mendez 001; Rosdiadee Nordin; Andrés Oliva Trevisan; Massimo Pavan; Manuel Roveri; et al. (AAAI Spring Symposia, 2024)

A Continual and Incremental Learning Approach for TinyML On-device Training Using Dataset Distillation and Model Size Adaption. - Marcus Rüb; Philipp Tuchel; Axel Sikora; Daniel Mueller-Gritschneder (ICPS, 2024)

Advancing On-Device Neural Network Training with TinyPropv2: Dynamic, Sparse, and Efficient Backpropagation. - Marcus Rüb; Axel Sikora; Daniel Mueller-Gritschneder (IJCNN, 2024)

Sparse Forward-Forward Algorithm: Efficient Machine Learning for Resource-Constrained Edge Devices. - Marcus Rüb; Michael Rüb; Axel Sikora (ICPS, 2025)

DRIP: DRop unImportant data Points - Enhancing Machine Learning Efficiency with Grad-CAM-Based Real-Time Data Prioritization for On-Device Training. - Marcus Rüb; Daniel Konegen; Axel Sikora; Daniel Mueller-Gritschneder (CoRR, 2025)