Marcus Rüb

According to our database, Marcus Rüb authored at least 4 papers between 2023 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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

CoRR, 2024

Advancing On-Device Neural Network Training with TinyPropv2: Dynamic, Sparse, and Efficient Backpropagation.

Marcus Rüb, Axel Sikora, Daniel Mueller-Gritschneder

Proceedings of the International Joint Conference on Neural Networks, 2024

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, Rosdiadee Nordin, Andrés Oliva Trevisan, Massimo Pavan, Manuel Roveri, Marcus Rüb, Jackline Tum, Marian Verhelst, Salah Abdeljabar, Segun Adebayo, Thomas Amberg, Halleluyah Aworinde, José Bagur, Gregg Barrett, Nabil Benamar, Bharat S. Chaudhari, Ronald Criollo, David Cuartielles, José A. Ferreira Filho, Solomon Gizaw, Evgeni Gousev, Alessandro Grande, Shawn Hymel, Peter Ing, Prashant Manandhar, Pietro Manzoni, Boris Murmann, Eric Pan, Rytis Paskauskas, Ermanno Pietrosemoli, Tales C. Pimenta, Marcelo Rovai, Marco Zennaro, Vijay Janapa Reddi

Proceedings of the AAAI 2024 Spring Symposium Series, 2024

2023

TinyProp - Adaptive Sparse Backpropagation for Efficient TinyML On-device Learning.

Marcus Rüb, Daniel Maier, Daniel Mueller-Gritschneder, Axel Sikora

CoRR, 2023