| Multi-target prediction for dummies using two-branch neural networks D Iliadis, B De Baets, W Waegeman Machine Learning 111 (2), 651-684, 2022 | 15 | 2022 |
| A comparison of embedding aggregation strategies in drug–target interaction prediction D Iliadis, B De Baets, T Pahikkala, W Waegeman BMC bioinformatics 25 (1), 59, 2024 | 5 | 2024 |
| Machine-learning-based prediction of disability progression in multiple sclerosis: An observational, international, multi-center study E De Brouwer, T Becker, L Werthen-Brabants, P Dewulf, D Iliadis, ... PLOS digital health 3 (7), e0000533, 2024 | 2 | 2024 |
| Iterative deepening hyperband J Brandt, M Wever, D Iliadis, V Bengs, E Hüllermeier arXiv preprint arXiv:2302.00511, 2023 | 2 | 2023 |
| Hyperparameter optimization of two-branch neural networks in multi-target prediction D Iliadis, M Wever, B De Baets, W Waegeman Applied Soft Computing 165, 111957, 2024 | 1 | 2024 |
| DeepMTP: A Python-based deep learning framework for multi-target prediction D Iliadis, B De Baets, W Waegeman SoftwareX 23, 101516, 2023 | 1 | 2023 |
| Automated Multi-target prediction with two-branch neural networks D Iliadis Ghent University, 2023 | | 2023 |
| Hyperparameter optimization in deep multi-target prediction D Iliadis, M Wever, B De Baets, W Waegeman arXiv preprint arXiv:2211.04362, 2022 | | 2022 |
| Incremental Successive Halving for Hyperparameter Optimization with Budget Constraints J Brandt, M Wever, D Iliadis, V Bengs, E Hüllermeier | | |
| Continual Hyperband J Brandt, M Wever, D Iliadis, V Bengs, E Hüllermeier | | |
