750 citations · 1.4k across the 30 of their papers we have counts for
9 papers · 1 filter
Cyclophobic Reinforcement Learning
Stefan Sylvius Wagner, Peter Arndt, Jan Robine +1
In environments with sparse rewards, finding a good inductive bias for exploration is crucial to the agent's success. However, there are two competing goals: novelty search and sys…
A Survey on Self-Supervised Representation Learning
Tobias Uelwer, Jan Robine, Stefan Sylvius Wagner +5
Learning meaningful representations is at the heart of many tasks in the field of modern machine learning. Recently, a lot of methods were introduced that allow learning of image r…
Transformer-based World Models Are Happy With 100k Interactions
Jan Robine, Marc Höftmann, Tobias Uelwer +1
Deep neural networks have been successful in many reinforcement learning settings. However, compared to human learners they are overly data hungry. To build a sample-efficient worl…
Time-Myopic Go-Explore: Learning A State Representation for the Go-Explore Paradigm
Marc Höftmann, Jan Robine, Stefan Harmeling
Very large state spaces with a sparse reward signal are difficult to explore. The lack of a sophisticated guidance results in a poor performance for numerous reinforcement learning…
Optimizing Intermediate Representations of Generative Models for Phase Retrieval
Tobias Uelwer, Sebastian Konietzny, Stefan Harmeling
Phase retrieval is the problem of reconstructing images from magnitude-only measurements. In many real-world applications the problem is underdetermined. When training data is avai…
Learning to Plan via a Multi-Step Policy Regression Method
Stefan Wagner, Michael Janschek, Tobias Uelwer +1
We propose a new approach to increase inference performance in environments that require a specific sequence of actions in order to be solved. This is for example the case for maze…
Smaller World Models for Reinforcement Learning
Jan Robine, Tobias Uelwer, Stefan Harmeling
Sample efficiency remains a fundamental issue of reinforcement learning. Model-based algorithms try to make better use of data by simulating the environment with a model. We propos…
On the Vulnerability of Capsule Networks to Adversarial Attacks
Felix Michels, Tobias Uelwer, Eric Upschulte +1
This paper extensively evaluates the vulnerability of capsule networks to different adversarial attacks. Recent work suggests that these architectures are more robust towards adver…
Modular Block-diagonal Curvature Approximations for Feedforward Architectures
Felix Dangel, Stefan Harmeling, Philipp Hennig
We propose a modular extension of backpropagation for the computation of block-diagonal approximations to various curvature matrices of the training objective (in particular, the H…