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Robert A Vandermeulen
Robert A Vandermeulen
Verified email at tu-berlin.de - Homepage
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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
11652018
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 109 (5), 756-795, 2021
3402021
Deep semi-supervised anomaly detection
L Ruff, RA Vandermeulen, N Görnitz, A Binder, E Müller, KR Müller, ...
International Conference on Learning Representations, 2019
3132019
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
1782018
Explainable deep one-class classification
P Liznerski, L Ruff, RA Vandermeulen, BJ Franks, M Kloft, KR Müller
International Conference on Learning Representations, 2020
962020
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
542021
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
482020
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
422019
Consistency of robust kernel density estimators
R Vandermeulen, C Scott
Conference on Learning Theory, 568-591, 2013
202013
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
172019
An Operator Theoretic Approach to Nonparametric Mixture Models
RA Vandermeulen, CD Scott
Annals of Statistics 47 (5), 2704-2733, 2019
142019
Anomaly detection with generative adversarial networks, 2018
L Deecke, R Vandermeulen, L Ruff, S Mandt, M Kloft
URL https://openreview. net/forum, 2018
112018
Transfer-based semantic anomaly detection
L Deecke, L Ruff, RA Vandermeulen, H Bilen
International Conference on Machine Learning, 2546-2558, 2021
92021
Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations
A Ritchie, RA Vandermeulen, C Scott
Advances in Neural Information Processing Systems 33, 2020
92020
On the identifiability of mixture models from grouped samples
RA Vandermeulen, CD Scott
arXiv preprint arXiv:1502.06644, 2015
82015
Robust kernel density estimation by scaling and projection in hilbert space
RA Vandermeulen, C Scott
Advances in Neural Information Processing Systems 27, 2014
72014
A Proposal for Supervised Density Estimation
RA Vandermeulen, R Saitenmacher, A Ritchie
NeurIPS Pre-Registration Workshop, 2020
42020
Deep Anomaly Detection by Residual Adaptation
L Deecke, L Ruff, RA Vandermeulen, H Bilen
4*
Input Hessian Regularization of Neural Networks
W Mustafa, RA Vandermeulen, M Kloft
International Conference on Machine Learning: Workshop on Beyond First Order …, 2020
32020
Improving nonparametric density estimation with tensor decompositions
RA Vandermeulen
arXiv preprint arXiv:2010.02425, 2020
22020
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Articles 1–20