80 citations · 179 across the 16 of their papers we have counts for
19 papers
On the improvement of model-predictive controllers
L. Féret, A. Gepperth, S. Lambeck
This article investigates synthetic model-predictive control (MPC) problems to demonstrate that an increased precision of the internal prediction model (PM) automatially entails an…
Large-scale gradient-based training of Mixtures of Factor Analyzers
Alexander Gepperth
Gaussian Mixture Models (GMMs) are a standard tool in data analysis. However, they face problems when applied to high-dimensional data (e.g., images) due to the size of the require…
Adiabatic replay for continual learning
Alexander Krawczyk, Alexander Gepperth
Conventional replay-based approaches to continual learning (CL) require, for each learning phase with new data, the replay of samples representing all of the previously learned kno…
A Framework for the Automated Parameterization of a Sensorless Bearing Fault Detection Pipeline
Tobias Wagner, Alexander Gepperth, Elmar Engels
This study proposes a framework for the automated hyperparameter optimization of a bearing fault detection pipeline for permanent magnet synchronous motors (PMSMs) without the need…
Beyond Supervised Continual Learning: a Review
Benedikt Bagus, Alexander Gepperth, Timothée Lesort
Continual Learning (CL, sometimes also termed incremental learning) is a flavor of machine learning where the usual assumption of stationary data distribution is relaxed or omitted…
A Study of Continual Learning Methods for Q-Learning
Benedikt Bagus, Alexander Gepperth
We present an empirical study on the use of continual learning (CL) methods in a reinforcement learning (RL) scenario, which, to the best of our knowledge, has not been described b…