Tom Viering
Tom Viering
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TitleCited byYear
Open problem: Monotonicity of learning
T Viering, A Mey, M Loog
Conference on Learning Theory, 3198-3201, 2019
How to Manipulate CNNs to Make Them Lie: the GradCAM Case
T Viering, Z Wang, M Loog, E Eisemann
arXiv preprint arXiv:1907.10901, 2019
Minimizers of the Empirical Risk and Risk Monotonicity
M Loog, T Viering, A Mey
Advances in Neural Information Processing Systems, 7476-7485, 2019
Nuclear discrepancy for active learning
TJ Viering, JH Krijthe, M Loog
arXiv preprint arXiv:1706.02645, 2017
Making Learners (More) Monotone
TJ Viering, A Mey, M Loog
arXiv preprint arXiv:1911.11030, 2019
Nuclear discrepancy for single-shot batch active learning
TJ Viering, JH Krijthe, M Loog
Machine Learning 108 (8-9), 1561-1599, 2019
A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization
A Mey, T Viering, M Loog
arXiv preprint arXiv:1906.06100, 2019
Active Learning by Discrepancy Minimization: A Comparison of Active Learning Methods Motivated by Generalization Bounds
TJ Viering
Generalization Bound Minimization for Active Learning
T Viering, J Krijthe, M Loog
Benelearn 2017: Proceedings of the Twenty-Sixth Benelux Conference on …, 0
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