5 citations · 8 across the 14 of their papers we have counts for
16 papers
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…
Is Hierarchical Quantization Essential for Optimal Reconstruction?
Shirin Reyhanian, Laurenz Wiskott
Vector-quantized variational autoencoders (VQ-VAEs) are central to models that rely on high reconstruction fidelity, from neural compression to generative pipelines. Hierarchical e…
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…
The Course Difficulty Analysis Cookbook
Frederik Baucks, Robin Schmucker, Laurenz Wiskott
Curriculum analytics (CA) studies curriculum structure and student data to ensure the quality of educational programs. An essential aspect is studying course properties, which invo…
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…
Understanding Transformer-Based Vision Models via Modular Feature Inversion
Jan Rathjens, Shirin Reyhanian, David Kappel +1
Understanding the internal mechanisms of deep neural networks remains a central challenge in machine learning. In computer vision, one promising yet only preliminarily explored app…
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…
Gaining Insights into Group-Level Course Difficulty via Differential Course Functioning
Frederik Baucks, Robin Schmucker, Conrad Borchers +2
Curriculum Analytics (CA) studies curriculum structure and student data to ensure the quality of educational programs. One desirable property of courses within curricula is that th…
ProtoP-OD: Explainable Object Detection with Prototypical Parts
Pavlos Rath-Manakidis, Frederik Strothmann, Tobias Glasmachers +1
Interpretation and visualization of the behavior of detection transformers tends to highlight the locations in the image that the model attends to, but it provides limited insight…
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…