43 citations · 120 across the 11 of their papers we have counts for
11 papers · 1 filter
Goal-conditioned Offline Planning from Curious Exploration
Marco Bagatella, Georg Martius
Curiosity has established itself as a powerful exploration strategy in deep reinforcement learning. Notably, leveraging expected future novelty as intrinsic motivation has been sho…
On the Pitfalls of Heteroscedastic Uncertainty Estimation with Probabilistic Neural Networks
Maximilian Seitzer, Arash Tavakoli, Dimitrije Antic +1
Capturing aleatoric uncertainty is a critical part of many machine learning systems. In deep learning, a common approach to this end is to train a neural network to estimate the pa…
Informed Equation Learning
Matthias Werner, Andrej Junginger, Philipp Hennig +1
Distilling data into compact and interpretable analytic equations is one of the goals of science. Instead, contemporary supervised machine learning methods mostly produce unstructu…
Neuro-algorithmic Policies enable Fast Combinatorial Generalization
Marin Vlastelica, Michal Rolínek, Georg Martius
Although model-based and model-free approaches to learning the control of systems have achieved impressive results on standard benchmarks, generalization to task variations is stil…
Demystifying Inductive Biases for -VAE Based Architectures
Dominik Zietlow, Michal Rolinek, Georg Martius
The performance of -Variational-Autoencoders (-VAEs) and their variants on learning semantically meaningful, disentangled representations is unparalleled. On the other hand,…
How to Train Your Differentiable Filter
Alina Kloss, Georg Martius, Jeannette Bohg
In many robotic applications, it is crucial to maintain a belief about the state of a system, which serves as input for planning and decision making and provides feedback during ta…