105 citations · 132 across the 9 of their papers we have counts for
9 papers
Formally Exploring Time-Series Anomaly Detection Evaluation Metrics
Dennis Wagner, Arjun Nair, Billy Joe Franks +24
Undetected anomalies in time series can trigger catastrophic failures in safety-critical systems, such as chemical plant explosions or power grid outages. Although many detection m…
Towards Graph Foundation Models: A Study on the Generalization of Positional and Structural Encodings
Billy Joe Franks, Moshe Eliasof, Semih Cantürk +4
Recent advances in integrating positional and structural encodings (PSEs) into graph neural networks (GNNs) have significantly enhanced their performance across various graph learn…
Weisfeiler-Leman at the margin: When more expressivity matters
Billy J. Franks, Christopher Morris, Ameya Velingker +1
The Weisfeiler-Leman algorithm (-WL) is a well-studied heuristic for the graph isomorphism problem. Recently, the algorithm has played a prominent role in understanding the expr…
Deep Anomaly Detection on Tennessee Eastman Process Data
Fabian Hartung, Billy Joe Franks, Tobias Michels +15
This paper provides the first comprehensive evaluation and analysis of modern (deep-learning) unsupervised anomaly detection methods for chemical process data. We focus on the Tenn…
Ordinal Regression for Difficulty Estimation of StepMania Levels
Billy Joe Franks, Benjamin Dinkelmann, Sophie Fellenz +1
StepMania is a popular open-source clone of a rhythm-based video game. As is common in popular games, there is a large number of community-designed levels. It is often difficult fo…
Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images
Philipp Liznerski, Lukas Ruff, Robert A. Vandermeulen +3
Due to the intractability of characterizing everything that looks unlike the normal data, anomaly detection (AD) is traditionally treated as an unsupervised problem utilizing only…
A systematic approach to random data augmentation on graph neural networks
Billy Joe Franks, Markus Anders, Marius Kloft +1
Random data augmentations (RDAs) are state of the art regarding practical graph neural networks that are provably universal. There is great diversity regarding terminology, methodo…
Explainable Deep One-Class Classification
Philipp Liznerski, Lukas Ruff, Robert A. Vandermeulen +3
Deep one-class classification variants for anomaly detection learn a mapping that concentrates nominal samples in feature space causing anomalies to be mapped away. Because this tr…
Rethinking Assumptions in Deep Anomaly Detection
Lukas Ruff, Robert A. Vandermeulen, Billy Joe Franks +2
Though anomaly detection (AD) can be viewed as a classification problem (nominal vs. anomalous) it is usually treated in an unsupervised manner since one typically does not have ac…