5 citations · 8 across the 14 of their papers we have counts for
11 papers · 1 filter
Probing Length Generalization in Mamba via Image Reconstruction
Jan Rathjens, Robin Schiewer, Laurenz Wiskott +1
Mamba has attracted widespread interest as a general-purpose sequence model due to its low computational complexity and competitive performance relative to transformers. However, i…
Effects of Distributional Biases on Gradient-Based Causal Discovery in the Bivariate Categorical Case
Tim Schwabe, Moritz Lange, Laurenz Wiskott +1
Gradient-based causal discovery shows great potential for deducing causal structure from data in an efficient and scalable way. Those approaches however can be susceptible to distr…
Object-centric Denoising Diffusion Models for Physical Reasoning
Moritz Lange, Raphael C. Engelhardt, Wolfgang Konen +2
Reasoning about the trajectories of multiple, interacting objects is integral to physical reasoning tasks in machine learning. This involves conditions imposed on the objects at di…
Slow Feature Analysis on Markov Chains from Goal-Directed Behavior
Merlin Schüler, Eddie Seabrook, Laurenz Wiskott
Slow Feature Analysis is a unsupervised representation learning method that extracts slowly varying features from temporal data and can be used as a basis for subsequent reinforcem…
Slow Feature Analysis as Variational Inference Objective
Merlin Schüler, Laurenz Wiskott
This work presents a novel probabilistic interpretation of Slow Feature Analysis (SFA) through the lens of variational inference. Unlike prior formulations that recover linear SFA…
Putting the Iterative Training of Decision Trees to the Test on a Real-World Robotic Task
Raphael C. Engelhardt, Marcel J. Meinen, Moritz Lange +2
In previous research, we developed methods to train decision trees (DT) as agents for reinforcement learning tasks, based on deep reinforcement learning (DRL) networks. The samples…
What is the relation between Slow Feature Analysis and the Successor Representation?
Eddie Seabrook, Laurenz Wiskott
Slow feature analysis (SFA) is an unsupervised method for extracting representations from time series data. The successor representation (SR) is a method for representing states in…
Exploring the limits of Hierarchical World Models in Reinforcement Learning
Robin Schiewer, Anand Subramoney, Laurenz Wiskott
Hierarchical model-based reinforcement learning (HMBRL) aims to combine the benefits of better sample efficiency of model based reinforcement learning (MBRL) with the abstraction c…
Interpretable Brain-Inspired Representations Improve RL Performance on Visual Navigation Tasks
Moritz Lange, Raphael C. Engelhardt, Wolfgang Konen +1
Visual navigation requires a whole range of capabilities. A crucial one of these is the ability of an agent to determine its own location and heading in an environment. Prior works…
Classification and Reconstruction Processes in Deep Predictive Coding Networks: Antagonists or Allies?
Jan Rathjens, Laurenz Wiskott
Predictive coding-inspired deep networks for visual computing integrate classification and reconstruction processes in shared intermediate layers. Although synergy between these pr…
Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-Visual Environments: A Comparison
Moritz Lange, Noah Krystiniak, Raphael C. Engelhardt +2
Real-world reinforcement learning (RL) environments, whether in robotics or industrial settings, often involve non-visual observations and require not only efficient but also relia…