27 citations · 95 across the 61 of their papers we have counts for
11 papers · 1 filter
SLASH: Embracing Probabilistic Circuits into Neural Answer Set Programming
Arseny Skryagin, Wolfgang Stammer, Daniel Ochs +2
The goal of combining the robustness of neural networks and the expressivity of symbolic methods has rekindled the interest in neuro-symbolic AI. Recent advancements in neuro-symbo…
The Causal Loss: Driving Correlation to Imply Causation
Moritz Willig, Matej Zečević, Devendra Singh Dhami +1
Most algorithms in classical and contemporary machine learning focus on correlation-based dependence between features to drive performance. Although success has been observed in ma…
Relating Graph Neural Networks to Structural Causal Models
Matej Zečević, Devendra Singh Dhami, Petar Veličković +1
Causality can be described in terms of a structural causal model (SCM) that carries information on the variables of interest and their mechanistic relations. For most processes of…
Neuro-Symbolic Forward Reasoning
Hikaru Shindo, Devendra Singh Dhami, Kristian Kersting
Reasoning is an essential part of human intelligence and thus has been a long-standing goal in artificial intelligence research. With the recent success of deep learning, incorpora…
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…