A year ago, to the day, I wrote a column (“Americans really need to relax and stop taking national politics so seriously”) in which I argued that modern Americans are far too concerned with politics ...
Abstract: A fundamental scalability restriction of most Inductive Logic Programming (ILP) systems is that they search syntactically defined program spaces and cannot utilize relations in data. While ...
Logic and probability provide two distinct frameworks for modeling how rational agents ought to draw inferences and learn from the available data in the face of uncertainty. The aim of this conference ...
we are currently developing a R-GCN based GNN for heterogeneous graphs with roughly 8 different node types. The GNN shall detect fraudulent behaviour by analysing the relations between different ...
Many techniques for automated inference of inductive invariants for distributed protocols have been developed over the past several years, but their performance can still be unpredictable and their ...
The problem of induction questions the justification for believing in universal statements derived from experience, particularly in empirical sciences. While many consider these universal statements ...
ULTRA is a model designed to learn universal and transferable graph representations for knowledge graphs (KGs). ULTRA creates relational illustrations by conditioning them on interactions, enabling it ...
The flagship venue for cross-disciplinary examination of the social, moral, and legal implications of socio-technical systems, the 2023 ACM Conference on Fairness, Accountability, and Transparency ...
Abstract: Graph convolutional network (GCN) algorithms have been employed to learn graph embedding due to its inductive inference property, which is extended to GCN with higher-order information.
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