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Robert A. Vandermeulen
Robert A. Vandermeulen
Technische Universität Berlin & BIFOLD
在 tu-berlin.de 的电子邮件经过验证 - 首页
标题
引用次数
引用次数
年份
Deep One-Class Classification
L Ruff, R Vandermeulen, N Goernitz, L Deecke, SA Siddiqui, A Binder, ...
International Conference on Machine Learning, 4390-4399, 2018
16982018
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
6012021
Deep semi-supervised anomaly detection
L Ruff, RA Vandermeulen, N Görnitz, A Binder, E Müller, KR Müller, ...
International Conference on Learning Representations, 2019
4832019
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
2182018
Explainable deep one-class classification
P Liznerski, L Ruff, RA Vandermeulen, BJ Franks, M Kloft, KR Müller
International Conference on Learning Representations, 2020
1632020
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
802021
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
662020
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
582019
Transfer-based semantic anomaly detection
L Deecke, L Ruff, RA Vandermeulen, H Bilen
International Conference on Machine Learning, 2546-2558, 2021
242021
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
192019
Consistency of robust kernel density estimators
R Vandermeulen, C Scott
Conference on Learning Theory, 568-591, 2013
192013
An Operator Theoretic Approach to Nonparametric Mixture Models
RA Vandermeulen, CD Scott
Annals of Statistics 47 (5), 2704-2733, 2019
182019
Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images
P Liznerski, L Ruff, RA Vandermeulen, BJ Franks, KR Müller, M Kloft
Transactions on Machine Learning Research, 2022
142022
Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations
A Ritchie, RA Vandermeulen, C Scott
Advances in Neural Information Processing Systems 33, 2020
142020
Anomaly detection with generative adversarial networks, 2018
L Deecke, R Vandermeulen, L Ruff, S Mandt, M Kloft
URL https://openreview. net/forum, 2018
142018
On the identifiability of mixture models from grouped samples
RA Vandermeulen, CD Scott
arXiv preprint arXiv:1502.06644, 2015
102015
Human alignment of neural network representations
L Muttenthaler, L Linhardt, J Dippel, RA Vandermeulen, S Kornblith
SVRHM 2022 Workshop@ NeurIPS, 2022
92022
VICE: Variational Inference for Concept Embeddings
L Muttenthaler, CY Zheng, P McClure, RA Vandermeulen, MN Hebart, ...
Advances in NeurIPS, 2022
8*2022
Robust kernel density estimation by scaling and projection in hilbert space
RA Vandermeulen, C Scott
Advances in Neural Information Processing Systems 27, 2014
82014
Input Hessian Regularization of Neural Networks
W Mustafa, RA Vandermeulen, M Kloft
International Conference on Machine Learning: Workshop on Beyond First Order …, 2020
62020
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