27 citations · 64 across the 44 of their papers we have counts for
16 papers · 1 filter
StaR Maps: Unveiling Uncertainty in Geospatial Relations
Simon Kohaut, Benedict Flade, Julian Eggert +2
The growing complexity of intelligent transportation systems and their applications in public spaces has increased the demand for expressive and versatile knowledge representation.…
Answer Set Networks: Casting Answer Set Programming into Deep Learning
Arseny Skryagin, Daniel Ochs, Phillip Deibert +4
Although Answer Set Programming (ASP) allows constraining neural-symbolic (NeSy) systems, its employment is hindered by the prohibitive costs of computing stable models and the CPU…
Graph Neural Networks Need Cluster-Normalize-Activate Modules
Arseny Skryagin, Felix Divo, Mohammad Amin Ali +2
Graph Neural Networks (GNNs) are non-Euclidean deep learning models for graph-structured data. Despite their successful and diverse applications, oversmoothing prohibits deep archi…
Hybrid Many-Objective Optimization in Probabilistic Mission Design for Compliant and Effective UAV Routing
Simon Kohaut, Nikolas Hohmann, Sebastian Brulin +6
Advanced Aerial Mobility encompasses many outstanding applications that promise to revolutionize modern logistics and pave the way for various public services and industry uses. Ho…
The Constitutional Filter: Bayesian Estimation of Compliant Agents
Simon Kohaut, Felix Divo, Benedict Flade +3
Predicting agents impacted by legal policies, physical limitations, and operational preferences is inherently difficult. In recent years, neuro-symbolic methods have emerged, integ…
Forecasting Company Fundamentals
Felix Divo, Eric Endress, Kevin Endler +2
Company fundamentals are key to assessing companies' financial and overall success and stability. Forecasting them is important in multiple fields, including investing and economet…
xLSTM-Mixer: Multivariate Time Series Forecasting by Mixing via Scalar Memories
Maurice Kraus, Felix Divo, Devendra Singh Dhami +1
Time series data is prevalent across numerous fields, necessitating the development of robust and accurate forecasting models. Capturing patterns both within and between temporal a…
Systems with Switching Causal Relations: A Meta-Causal Perspective
Moritz Willig, Tim Nelson Tobiasch, Florian Peter Busch +3
Most work on causality in machine learning assumes that causal relationships are driven by a constant underlying process. However, the flexibility of agents' actions or tipping poi…
BlendRL: A Framework for Merging Symbolic and Neural Policy Learning
Hikaru Shindo, Quentin Delfosse, Devendra Singh Dhami +1
Humans can leverage both symbolic reasoning and intuitive reactions. In contrast, reinforcement learning policies are typically encoded in either opaque systems like neural network…
Towards Probabilistic Clearance, Explanation and Optimization
Simon Kohaut, Benedict Flade, Devendra Singh Dhami +2
Employing Unmanned Aircraft Systems (UAS) beyond visual line of sight (BVLOS) is an endearing and challenging task. While UAS have the potential to significantly enhance today's lo…
EXPIL: Explanatory Predicate Invention for Learning in Games
Jingyuan Sha, Hikaru Shindo, Quentin Delfosse +2
Reinforcement learning (RL) has proven to be a powerful tool for training agents that excel in various games. However, the black-box nature of neural network models often hinders o…
Mission Design for Unmanned Aerial Vehicles using Hybrid Probabilistic Logic Programs
Simon Kohaut, Benedict Flade, Devendra Singh Dhami +2
Advanced Air Mobility (AAM) is a growing field that demands a deep understanding of legal, spatial and temporal concepts in navigation. Hence, any implementation of AAM is forced t…
United We Pretrain, Divided We Fail! Representation Learning for Time Series by Pretraining on 75 Datasets at Once
Maurice Kraus, Felix Divo, David Steinmann +2
In natural language processing and vision, pretraining is utilized to learn effective representations. Unfortunately, the success of pretraining does not easily carry over to time…
DeiSAM: Segment Anything with Deictic Prompting
Hikaru Shindo, Manuel Brack, Gopika Sudhakaran +3
Large-scale, pre-trained neural networks have demonstrated strong capabilities in various tasks, including zero-shot image segmentation. To identify concrete objects in complex sce…
Right on Time: Revising Time Series Models by Constraining their Explanations
Maurice Kraus, David Steinmann, Antonia Wüst +2
Deep time series models often suffer from reliability issues due to their tendency to rely on spurious correlations, leading to incorrect predictions. To mitigate such shortcuts an…
Pix2Code: Learning to Compose Neural Visual Concepts as Programs
Antonia Wüst, Wolfgang Stammer, Quentin Delfosse +2
The challenge in learning abstract concepts from images in an unsupervised fashion lies in the required integration of visual perception and generalizable relational reasoning. Mor…