3 citations · 10 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
Leveraging Probabilistic Circuits for Nonparametric Multi-Output Regression
Zhongjie Yu, Mingye Zhu, Martin Trapp +2
Inspired by recent advances in the field of expert-based approximations of Gaussian processes (GPs), we present an expert-based approach to large-scale multi-output regression usin…