Robert A Vandermeulen
Title
Cited by
Cited by
Year
Deep One-Class Classification
L Ruff, R Vandermeulen, N Goernitz, L Deecke, SA Siddiqui, A Binder, ...
International Conference on Machine Learning, 4390-4399, 2018
5462018
Deep semi-supervised anomaly detection
L Ruff, RA Vandermeulen, N Görnitz, A Binder, E Müller, KR Müller, ...
arXiv preprint arXiv:1906.02694, 2019
1232019
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
1092018
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
612021
Explainable deep one-class classification
P Liznerski, L Ruff, RA Vandermeulen, BJ Franks, M Kloft, KR Müller
arXiv preprint arXiv:2007.01760, 2020
192020
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
182019
Consistency of robust kernel density estimators
R Vandermeulen, C Scott
Conference on Learning Theory, 568-591, 2013
172013
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
162020
Rethinking assumptions in deep anomaly detection
L Ruff, RA Vandermeulen, BJ Franks, KR Müller, M Kloft
ICML 2021 Workshop on Uncertainty & Robustness in Deep Learning, 2021
142021
On the identifiability of mixture models from grouped samples
RA Vandermeulen, CD Scott
arXiv preprint arXiv:1502.06644, 2015
92015
An Operator Theoretic Approach to Nonparametric Mixture Models
RA Vandermeulen, CD Scott
Annals of Statistics 47 (5), 2704-2733, 2019
82019
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
72019
Robust kernel density estimation by scaling and projection in Hilbert space
RA Vandermeulen, C Scott
Advances in Neural Information Processing Systems 27, 433-441, 2014
52014
Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations
A Ritchie, RA Vandermeulen, C Scott
Advances in Neural Information Processing Systems 33, 2020
42020
Deep Anomaly Detection by Residual Adaptation
L Deecke, L Ruff, RA Vandermeulen, H Bilen
3*
A Proposal for Supervised Density Estimation
RA Vandermeulen, R Saitenmacher, A Ritchie
NeurIPS Pre-Registration Workshop, 2020
12020
Improving nonparametric density estimation with tensor decompositions
RA Vandermeulen
arXiv preprint arXiv:2010.02425, 2020
12020
Supplement to “An operator theoretic approach to nonparametric mixture models.”
RA Vandermeulen, CD Scott
DOI, 2019
12019
Transfer-Based Semantic Anomaly Detection
L Deecke, L Ruff, RA Vandermeulen, H Bilen
International Conference on Machine Learning, 2546-2558, 2021
2021
Learning Interpretable Concept Groups in CNNs
S Varshneya, A Ledent, RA Vandermeulen, Y Lei, M Enders, D Borth, ...
2021
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