| The shape of learning curves: a review T Viering, M Loog IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (6), 7799-7819, 2022 | 160 | 2022 |
| A brief prehistory of double descent M Loog, T Viering, A Mey, JH Krijthe, DMJ Tax Proceedings of the National Academy of Sciences 117 (20), 10625-10626, 2020 | 83 | 2020 |
| Minimizers of the empirical risk and risk monotonicity M Loog, T Viering, A Mey Advances in Neural Information Processing Systems 32, 2019 | 28 | 2019 |
| Open problem: Monotonicity of learning T Viering, A Mey, M Loog Conference on Learning Theory, 3198-3201, 2019 | 26 | 2019 |
| LCDB 1.0: An extensive learning curves database for classification tasks F Mohr, TJ Viering, M Loog, JN van Rijn Joint European Conference on Machine Learning and Knowledge Discovery in …, 2022 | 22 | 2022 |
| 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 | 22 | 2019 |
| Is Wikipedia succeeding in reducing gender bias? Assessing changes in gender bias in Wikipedia using word embeddings KG Schmahl, TJ Viering, S Makrodimitris, AN Jahfari, D Tax, M Loog Proceedings of the Fourth Workshop on Natural Language Processing and …, 2020 | 19 | 2020 |
| Making learners (more) monotone TJ Viering, A Mey, M Loog International Symposium on Intelligent Data Analysis, 535-547, 2020 | 10 | 2020 |
| Nuclear discrepancy for single-shot batch active learning TJ Viering, JH Krijthe, M Loog Machine Learning 108 (8), 1561-1599, 2019 | 8 | 2019 |
| A survey of learning curves with bad behavior: or how more data need not lead to better performance M Loog, T Viering arXiv preprint arXiv:2211.14061, 2022 | 4 | 2022 |
| The unreasonable effectiveness of early discarding after one epoch in neural network hyperparameter optimization R Egele, F Mohr, T Viering, P Balaprakash Neurocomputing, 127964, 2024 | 3 | 2024 |
| Nuclear discrepancy for active learning TJ Viering, JH Krijthe, M Loog arXiv preprint arXiv:1706.02645, 2017 | 2 | 2017 |
| On Safety in Machine Learning TJ Viering TU Delft, 2023 | 1 | 2023 |
| On Safety in Machine Learning TJ Viering TU Delft, 2023 | 1 | 2023 |
| A distribution dependent and independent complexity analysis of manifold regularization A Mey, TJ Viering, M Loog International Symposium on Intelligent Data Analysis, 326-338, 2020 | 1 | 2020 |
| Global patterns in vegetation accessible subsurface water storage emerge from spatially varying importance of individual drivers F van Oorschot, M Hrachowitz, T Viering, A Alessandri, RJ van der Ent Environmental Research Letters 19 (12), 124018, 2024 | | 2024 |
| How catchment ecosystems globally manage root water access under different (climate) conditions F van Oorschot, R van der Ent, T Viering, A Alessandri, M Hrachowitz EGU24, 2024 | | 2024 |
| Different approaches to fitting and extrapolating the learning curve D Kim, T Viering, M Loog unpublished, 2022 | | 2022 |
| To Tune or not to Tune: Hyperparameter Influence on the Learning Curve P Bhaskaran, T Viering | | 2022 |
| Is the batch size affecting the performance of Regression CNNs? J Lamon, Y KATO, T TURAN, T VIERING, Z WANG, M LOOG, D TAX | | 2021 |
Marco LoogRadboud UniversityVerified email at ru.nl
