| The Lovász-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks M Berman, A Rannen Triki, MB Blaschko | 595 | 2018 |
| Encoder based lifelong learning A Rannen, R Aljundi, MB Blaschko, T Tuytelaars Proceedings of the IEEE International Conference on Computer Vision, 1320-1328, 2017 | 251 | 2017 |
| A Bayesian optimization framework for neural network compression X Ma, AR Triki, M Berman, C Sagonas, J Cali, MB Blaschko Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 20 | 2019 |
| Intraoperative margin assessment of human breast tissue in optical coherence tomography images using deep neural networks AR Triki, MB Blaschko, YM Jung, S Song, HJ Han, SI Kim, C Joo Computerized medical imaging and graphics 69, 21-32, 2018 | 16 | 2018 |
| Function norms for neural networks A Rannen-Triki, M Berman, V Kolmogorov, MB Blaschko Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 11* | 2019 |
| On the role of optimization in double descent: A least squares study I Kuzborskij, C Szepesvári, O Rivasplata, A Rannen-Triki, R Pascanu Advances in Neural Information Processing Systems 34, 29567-29577, 2021 | 4 | 2021 |
| Towards Better Visual Explanations for Deep Image Classifiers A Grabska-Barwinska, A Rannen-Triki, O Rivasplata, A György eXplainable AI approaches for debugging and diagnosis., 2021 | 1 | 2021 |
| NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research J Bornschein, A Galashov, R Hemsley, A Rannen-Triki, Y Chen, ... arXiv preprint arXiv:2211.11747, 2022 | | 2022 |
| Function Norms for Neural Networks: Theory and Applications A Rannen Ep Triki | | 2020 |
| Yes, IoU loss is submodular-as a function of the mispredictions M Berman, MB Blaschko, AR Triki, J Yu arXiv preprint arXiv:1809.01845, 2018 | | 2018 |
Matthew BlaschkoKU LeuvenVerified email at esat.kuleuven.be
