27 citations · 95 across the 61 of their papers we have counts for
7 papers · 1 filter
Do Not Marginalize Mechanisms, Rather Consolidate!
Moritz Willig, Matej Zečević, Devendra Singh Dhami +1
Structural causal models (SCMs) are a powerful tool for understanding the complex causal relationships that underlie many real-world systems. As these systems grow in size, the num…
Causal Parrots: Large Language Models May Talk Causality But Are Not Causal
Matej Zečević, Moritz Willig, Devendra Singh Dhami +1
Some argue scale is all what is needed to achieve AI, covering even causal models. We make it clear that large language models (LLMs) cannot be causal and give reason onto why some…
Vision Relation Transformer for Unbiased Scene Graph Generation
Gopika Sudhakaran, Devendra Singh Dhami, Kristian Kersting +1
Recent years have seen a growing interest in Scene Graph Generation (SGG), a comprehensive visual scene understanding task that aims to predict entity relationships using a relatio…
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…
Scalable Neural-Probabilistic Answer Set Programming
Arseny Skryagin, Daniel Ochs, Devendra Singh Dhami +1
The goal of combining the robustness of neural networks and the expressiveness of symbolic methods has rekindled the interest in Neuro-Symbolic AI. Deep Probabilistic Programming L…
V-LoL: A Diagnostic Dataset for Visual Logical Learning
Lukas Helff, Wolfgang Stammer, Hikaru Shindo +2
Despite the successes of recent developments in visual AI, different shortcomings still exist; from missing exact logical reasoning, to abstract generalization abilities, to unders…