27 citations · 64 across the 44 of their papers we have counts for
19 papers · 1 filter
QuAnTS: Question Answering on Time Series
Felix Divo, Maurice Kraus, Anh Q. Nguyen +5
Text offers intuitive access to information. This can, in particular, complement the density of numerical time series, thereby enabling improved interactions with time series model…
Tags for DAGs: Graph Refinement with Meta-Informed Relations
Florian Peter Busch, Moritz Willig, Florian Guldan +2
Causal discovery has shifted from data-centric methods to hybrid strategies that integrate semantic knowledge from experts or large language models (LLMs). Such external informatio…
Scaling Probabilistic Circuits via Data Partitioning
Jonas Seng, Florian Peter Busch, Pooja Prasad +3
Probabilistic circuits (PCs) enable us to learn joint distributions over a set of random variables and to perform various probabilistic queries in a tractable fashion. Though the t…
Exploring Neural Granger Causality with xLSTMs: Unveiling Temporal Dependencies in Complex Data
Harsh Poonia, Felix Divo, Kristian Kersting +1
Causality in time series can be challenging to determine, especially in the presence of non-linear dependencies. Granger causality helps analyze potential relationships between var…
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…
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…
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…
Learning Differentiable Logic Programs for Abstract Visual Reasoning
Hikaru Shindo, Viktor Pfanschilling, Devendra Singh Dhami +1
Visual reasoning is essential for building intelligent agents that understand the world and perform problem-solving beyond perception. Differentiable forward reasoning has been dev…
OCAtari: Object-Centric Atari 2600 Reinforcement Learning Environments
Quentin Delfosse, Jannis Blüml, Bjarne Gregori +2
Cognitive science and psychology suggest that object-centric representations of complex scenes are a promising step towards enabling efficient abstract reasoning from low-level per…
Interpretable and Explainable Logical Policies via Neurally Guided Symbolic Abstraction
Quentin Delfosse, Hikaru Shindo, Devendra Dhami +1
The limited priors required by neural networks make them the dominating choice to encode and learn policies using reinforcement learning (RL). However, they are also black-boxes, m…
FEATHERS: Federated Architecture and Hyperparameter Search
Jonas Seng, Pooja Prasad, Martin Mundt +2
Deep neural architectures have profound impact on achieved performance in many of today's AI tasks, yet, their design still heavily relies on human prior knowledge and experience.…
Towards a Solution to Bongard Problems: A Causal Approach
Salahedine Youssef, Matej Zečević, Devendra Singh Dhami +1
Even though AI has advanced rapidly in recent years displaying success in solving highly complex problems, the class of Bongard Problems (BPs) yet remain largely unsolved by modern…
A Taxonomy for Inference in Causal Model Families
Matej Zečević, Devendra Singh Dhami, Kristian Kersting
Neurally-parameterized Structural Causal Models in the Pearlian notion to causality, referred to as NCM, were recently introduced as a step towards next-generation learning systems…
Causal Explanations of Structural Causal Models
Matej Zečević, Devendra Singh Dhami, Constantin A. Rothkopf +1
In explanatory interactive learning (XIL) the user queries the learner, then the learner explains its answer to the user and finally the loop repeats. XIL is attractive for two rea…
Structural Causal Models Reveal Confounder Bias in Linear Program Modelling
Matej Zečević, Devendra Singh Dhami, Kristian Kersting
The recent years have been marked by extended research on adversarial attacks, especially on deep neural networks. With this work we intend on posing and investigating the question…