Billy Joe Franks

Publication Activity (10 Years)

Years Active: 2020-2023
Publications (10 Years): 10

Top Topics

Anomaly Detection

Ordinal Regression

Surgical Planning

Top Venues

Trans. Mach. Learn. Res.

Publications

Billy Joe Franks, Markus Anders, Marius Kloft, Pascal Schweitzer
A Systematic Approach to Universal Random Features in Graph Neural Networks. Trans. Mach. Learn. Res. 2023 (2023)
Billy Joe Franks, Benjamin Dinkelmann, Marius Kloft, Sophie Fellenz
Ordinal Regression for Difficulty Prediction of StepMania Levels. ECML/PKDD (6) (2023)
Fabian Hartung, Billy Joe Franks, Tobias Michels, Dennis Wagner, Philipp Liznerski, Steffen Reithermann, Sophie Fellenz, Fabian Jirasek, Maja Rudolph, Daniel Neider, Heike Leitte, Chen Song, Benjamin Klöpper, Stephan Mandt, Michael Bortz, Jakob Burger, Hans Hasse, Marius Kloft
Deep Anomaly Detection on Tennessee Eastman Process Data. CoRR (2023)
Billy Joe Franks, Benjamin Dinkelmann, Sophie Fellenz, Marius Kloft
Ordinal Regression for Difficulty Estimation of StepMania Levels. CoRR (2023)
Philipp Liznerski, Lukas Ruff, Robert A. Vandermeulen, Billy Joe Franks, Klaus-Robert Müller, Marius Kloft
Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images. CoRR (2022)
Philipp Liznerski, Lukas Ruff, Robert A. Vandermeulen, Billy Joe Franks, Klaus-Robert Müller, Marius Kloft
Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images. Trans. Mach. Learn. Res. 2022 (2022)
Billy Joe Franks, Markus Anders, Marius Kloft, Pascal Schweitzer
Trainability for Universal GNNs Through Surgical Randomness. CoRR (2021)
Philipp Liznerski, Lukas Ruff, Robert A. Vandermeulen, Billy Joe Franks, Marius Kloft, Klaus-Robert Müller
Explainable Deep One-Class Classification. ICLR (2021)
Philipp Liznerski, Lukas Ruff, Robert A. Vandermeulen, Billy Joe Franks, Marius Kloft, Klaus-Robert Müller
Explainable Deep One-Class Classification. CoRR (2020)
Lukas Ruff, Robert A. Vandermeulen, Billy Joe Franks, Klaus-Robert Müller, Marius Kloft
Rethinking Assumptions in Deep Anomaly Detection. CoRR (2020)