| Harmonic networks: Deep translation and rotation equivariance DE Worrall, SJ Garbin, D Turmukhambetov, GJ Brostow Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 813 | 2017 |
| Se (3)-transformers: 3d roto-translation equivariant attention networks F Fuchs, D Worrall, V Fischer, M Welling Advances in neural information processing systems 33, 1970-1981, 2020 | 709 | 2020 |
| Message passing neural PDE solvers J Brandstetter, D Worrall, M Welling arXiv preprint arXiv:2202.03376, 2022 | 292 | 2022 |
| Learning likelihoods with conditional normalizing flows C Winkler, D Worrall, E Hoogeboom, M Welling arXiv preprint arXiv:1912.00042, 2019 | 229 | 2019 |
| Deep scale-spaces: Equivariance over scale D Worrall, M Welling Advances in Neural Information Processing Systems 32, 2019 | 186 | 2019 |
| Bayesian image quality transfer with CNNs: exploring uncertainty in dMRI super-resolution R Tanno, DE Worrall, A Ghosh, E Kaden, SN Sotiropoulos, A Criminisi, ... Medical Image Computing and Computer Assisted Intervention− MICCAI 2017 …, 2017 | 181 | 2017 |
| Cubenet: Equivariance to 3d rotation and translation D Worrall, G Brostow Proceedings of the European Conference on Computer Vision (ECCV), 567-584, 2018 | 165 | 2018 |
| Mdp homomorphic networks: Group symmetries in reinforcement learning E Van der Pol, D Worrall, H van Hoof, F Oliehoek, M Welling Advances in Neural Information Processing Systems 33, 4199-4210, 2020 | 164 | 2020 |
| Automated retinopathy of prematurity case detection with convolutional neural networks DE Worrall, CM Wilson, GJ Brostow International workshop on deep learning in medical image analysis, 68-76, 2016 | 119 | 2016 |
| Uncertainty modelling in deep learning for safer neuroimage enhancement: Demonstration in diffusion MRI R Tanno, DE Worrall, E Kaden, A Ghosh, F Grussu, A Bizzi, ... NeuroImage 225, 117366, 2021 | 110 | 2021 |
| Interpretable transformations with encoder-decoder networks DE Worrall, SJ Garbin, D Turmukhambetov, GJ Brostow Proceedings of the IEEE International Conference on Computer Vision, 5726-5735, 2017 | 110 | 2017 |
| Supervised uncertainty quantification for segmentation with multiple annotations S Hu, D Worrall, S Knegt, B Veeling, H Huisman, M Welling Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd …, 2019 | 109 | 2019 |
| Reversible gans for memory-efficient image-to-image translation TFA van der Ouderaa, DE Worrall Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 65 | 2019 |
| Lie point symmetry data augmentation for neural PDE solvers J Brandstetter, M Welling, DE Worrall International Conference on Machine Learning, 2241-2256, 2022 | 54 | 2022 |
| Uncertainty quantification in deep learning for safer neuroimage enhancement R Tanno, D Worrall, E Kaden, A Ghosh, F Grussu, A Bizzi, ... arXiv preprint arXiv:1907.13418, 2019 | 40 | 2019 |
| Learning to convolve: A generalized weight-tying approach N Diaconu, D Worrall International Conference on Machine Learning, 1586-1595, 2019 | 19 | 2019 |
| Neural simulated annealing AHC Correia, DE Worrall, R Bondesan International Conference on Artificial Intelligence and Statistics, 4946-4962, 2023 | 13 | 2023 |
| Witnessing atrocities: quantifying villages destruction in Darfur with crowdsourcing and transfer learning J Cornebise, D Worrall, M Farfour, M Marin Proc. AI for Social Good NeurIPS2018 Workshop, NeurIPS’18, 2018 | 11 | 2018 |
| Chest CT super-resolution and domain-adaptation using memory-efficient 3D reversible GANs TFA van der Ouderaa, DE Worrall, B van Ginneken arXiv preprint arXiv:1908.00295, 2019 | 5 | 2019 |
| Affine self convolution N Diaconu, DE Worrall arXiv preprint arXiv:1911.07704, 2019 | 4 | 2019 |
Max WellingCAIO CuspAI & Professor Machine Learning, University of Amsterdam在 uva.nl 的电子邮件经过验证
