Robert A Vandermeulen
Cited by
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Deep One-Class Classification
L Ruff, R Vandermeulen, N Goernitz, L Deecke, SA Siddiqui, A Binder, ...
International Conference on Machine Learning, 4390-4399, 2018
Image anomaly detection with generative adversarial networks
L Deecke, R Vandermeulen, L Ruff, S Mandt, M Kloft
Joint european conference on machine learning and knowledge discovery in …, 2018
Deep semi-supervised anomaly detection
L Ruff, RA Vandermeulen, N Görnitz, A Binder, E Müller, KR Müller, ...
arXiv preprint arXiv:1906.02694, 2019
A unifying review of deep and shallow anomaly detection
L Ruff, JR Kauffmann, RA Vandermeulen, G Montavon, W Samek, M Kloft, ...
Proceedings of the IEEE, 2021
Self-attentive, multi-context one-class classification for unsupervised anomaly detection on text
L Ruff, Y Zemlyanskiy, R Vandermeulen, T Schnake, M Kloft
Proceedings of the 57th Annual Meeting of the Association for Computational …, 2019
Consistency of robust kernel density estimators
R Vandermeulen, C Scott
Conference on Learning Theory, 568-591, 2013
Machine learning in thermodynamics: Prediction of activity coefficients by matrix completion
F Jirasek, RAS Alves, J Damay, RA Vandermeulen, R Bamler, M Bortz, ...
The journal of physical chemistry letters 11 (3), 981-985, 2020
Anomaly detection with generative adversarial networks, 2018
L Deecke, R Vandermeulen, L Ruff, S Mandt, M Kloft
URL https://openreview. net/forum, 2018
On the identifiability of mixture models from grouped samples
RA Vandermeulen, CD Scott
arXiv preprint arXiv:1502.06644, 2015
Explainable deep one-class classification
P Liznerski, L Ruff, RA Vandermeulen, BJ Franks, M Kloft, KR Müller
arXiv preprint arXiv:2007.01760, 2020
Rethinking assumptions in deep anomaly detection
L Ruff, RA Vandermeulen, BJ Franks, KR Müller, M Kloft
arXiv preprint arXiv:2006.00339, 2020
Deep support vector data description for unsupervised and semi-supervised anomaly detection
L Ruff, RA Vandermeulen, N Gornitz, A Binder, E Muller, M Kloft
Proceedings of the ICML 2019 Workshop on Uncertainty and Robustness in Deep …, 2019
An Operator Theoretic Approach to Nonparametric Mixture Models
RA Vandermeulen, CD Scott
arXiv preprint arXiv:1607.00071, 2016
Robust kernel density estimation by scaling and projection in Hilbert space
RA Vandermeulen, CD Scott
arXiv preprint arXiv:1411.4378, 2014
A Proposal for Supervised Density Estimation
RA Vandermeulen, R Saitenmacher, A Ritchie
NeurIPS Pre-Registration Workshop, 2020
Improving nonparametric density estimation with tensor decompositions
RA Vandermeulen
arXiv preprint arXiv:2010.02425, 2020
Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations
A Ritchie, RA Vandermeulen, C Scott
Advances in Neural Information Processing Systems 33, 2020
Supplement to “An operator theoretic approach to nonparametric mixture models.”
RA Vandermeulen, CD Scott
DOI, 2019
Deep Anomaly Detection by Residual Adaptation
L Deecke, L Ruff, RA Vandermeulen, H Bilen
arXiv preprint arXiv:2010.02310, 2020
Input Hessian Regularization of Neural Networks
W Mustafa, RA Vandermeulen, M Kloft
International Conference on Machine Learning: Workshop on Beyond First Order …, 2020
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