9 citations · 31 across the 22 of their papers we have counts for
5 papers · 1 filter
RockNet: Distributed Learning on Ultra-Low-Power Devices
Alexander Gräfe, Fabian Mager, Marco Zimmerling +1
As Machine Learning (ML) becomes integral to Cyber-Physical Systems (CPS), there is growing interest in shifting training from traditional cloud-based to on-device processing (Tiny…
MPX: Mixed Precision Training for JAX
Alexander Gräfe, Sebastian Trimpe
Mixed-precision training has emerged as an indispensable tool for enhancing the efficiency of neural network training in recent years. Concurrently, JAX has grown in popularity as…
On Safety in Safe Bayesian Optimization
Christian Fiedler, Johanna Menn, Lukas Kreisköther +1
Optimizing an unknown function under safety constraints is a central task in robotics, biomedical engineering, and many other disciplines, and increasingly safe Bayesian Optimizati…
On kernel-based statistical learning in the mean field limit
Christian Fiedler, Michael Herty, Sebastian Trimpe
In many applications of machine learning, a large number of variables are considered. Motivated by machine learning of interacting particle systems, we consider the situation when…
Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression
Christian Fiedler, Carsten W. Scherer, Sebastian Trimpe
Gaussian Process Regression is a popular nonparametric regression method based on Bayesian principles that provides uncertainty estimates for its predictions. However, these estima…