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ORCIDDaniel E. Worrall
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Books and Theses
- 2019
[b1]Daniel E. Worrall:
Equivariance for deep learning and retinal imaging. University College London, UK, 2019
Journal Articles
- 2021
- [j1]Ryutaro Tanno, Daniel E. Worrall
, Enrico Kaden, Aurobrata Ghosh, Francesco Grussu, Alberto Bizzi, Stamatios N. Sotiropoulos, Antonio Criminisi, Daniel C. Alexander:
Uncertainty modelling in deep learning for safer neuroimage enhancement: Demonstration in diffusion MRI. NeuroImage 225: 117366 (2021)
Conference and Workshop Papers
- 2023
- [c15]Alvaro H. C. Correia, Daniel E. Worrall, Roberto Bondesan:
Neural Simulated Annealing. AISTATS 2023: 4946-4962 - 2022
- [c14]Daniel E. Worrall, Markus Peschl, Arash Behboodi, Roberto Bondesan:
Learning Perturbations for Soft-Output Linear MIMO Demappers. GLOBECOM 2022: 5213-5218 - [c13]Johannes Brandstetter, Daniel E. Worrall, Max Welling:
Message Passing Neural PDE Solvers. ICLR 2022 - [c12]Johannes Brandstetter, Max Welling, Daniel E. Worrall:
Lie Point Symmetry Data Augmentation for Neural PDE Solvers. ICML 2022: 2241-2256 - 2020
- [c11]Fabian Fuchs, Daniel E. Worrall, Volker Fischer, Max Welling:
SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks. NeurIPS 2020 - [c10]Elise van der Pol, Daniel E. Worrall, Herke van Hoof, Frans A. Oliehoek, Max Welling:
MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning. NeurIPS 2020 - 2019
- [c9]Tycho F. A. van der Ouderaa, Daniel E. Worrall:
Reversible GANs for Memory-Efficient Image-To-Image Translation. CVPR 2019: 4720-4728 - [c8]Nichita Diaconu, Daniel E. Worrall:
Learning to Convolve: A Generalized Weight-Tying Approach. ICML 2019: 1586-1595 - [c7]Shi Hu, Daniel E. Worrall, Stefan Knegt, Bas Veeling, Henkjan J. Huisman, Max Welling:
Supervised Uncertainty Quantification for Segmentation with Multiple Annotations. MICCAI (2) 2019: 137-145 - [c6]Daniel E. Worrall, Max Welling:
Deep Scale-spaces: Equivariance Over Scale. NeurIPS 2019: 7364-7376 - 2018
- [c5]Daniel E. Worrall, Gabriel J. Brostow:
CubeNet: Equivariance to 3D Rotation and Translation. ECCV (5) 2018: 585-602 - 2017
- [c4]Daniel E. Worrall, Stephan J. Garbin, Daniyar Turmukhambetov, Gabriel J. Brostow:
Harmonic Networks: Deep Translation and Rotation Equivariance. CVPR 2017: 7168-7177 - [c3]Daniel E. Worrall, Stephan J. Garbin, Daniyar Turmukhambetov, Gabriel J. Brostow:
Interpretable Transformations with Encoder-Decoder Networks. ICCV 2017: 5737-5746 - [c2]Ryutaro Tanno, Daniel E. Worrall, Aurobrata Ghosh, Enrico Kaden, Stamatios N. Sotiropoulos, Antonio Criminisi, Daniel C. Alexander:
Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution. MICCAI (1) 2017: 611-619 - 2016
- [c1]Daniel E. Worrall, Clare M. Wilson, Gabriel J. Brostow:
Automated Retinopathy of Prematurity Case Detection with Convolutional Neural Networks. LABELS/DLMIA@MICCAI 2016: 68-76
Informal and Other Publications
- 2022
- [i18]Johannes Brandstetter, Daniel E. Worrall, Max Welling:
Message Passing Neural PDE Solvers. CoRR abs/2202.03376 (2022) - [i17]Johannes Brandstetter, Max Welling, Daniel E. Worrall:
Lie Point Symmetry Data Augmentation for Neural PDE Solvers. CoRR abs/2202.07643 (2022) - [i16]Alvaro H. C. Correia, Daniel E. Worrall, Roberto Bondesan:
Neural Simulated Annealing. CoRR abs/2203.02201 (2022) - [i15]Daniel E. Worrall, Markus Peschl, Arash Behboodi, Roberto Bondesan:
Learning Perturbations for Soft-Output Linear MIMO Demappers. CoRR abs/2209.00529 (2022) - 2020
- [i14]Fabian B. Fuchs, Daniel E. Worrall, Volker Fischer, Max Welling:
SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks. CoRR abs/2006.10503 (2020) - [i13]Elise van der Pol, Daniel E. Worrall, Herke van Hoof, Frans A. Oliehoek, Max Welling:
MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning. CoRR abs/2006.16908 (2020) - 2019
- [i12]Tycho F. A. van der Ouderaa, Daniel E. Worrall:
Reversible GANs for Memory-efficient Image-to-Image Translation. CoRR abs/1902.02729 (2019) - [i11]Nichita Diaconu, Daniel E. Worrall:
Learning to Convolve: A Generalized Weight-Tying Approach. CoRR abs/1905.04663 (2019) - [i10]Daniel E. Worrall, Max Welling:
Deep Scale-spaces: Equivariance Over Scale. CoRR abs/1905.11697 (2019) - [i9]Shi Hu, Daniel E. Worrall, Stefan Knegt, Bastiaan S. Veeling, Henkjan J. Huisman, Max Welling:
Supervised Uncertainty Quantification for Segmentation with Multiple Annotations. CoRR abs/1907.01949 (2019) - [i8]Ryutaro Tanno, Daniel E. Worrall, Enrico Kaden, Aurobrata Ghosh, Francesco Grussu, Alberto Bizzi, Stamatios N. Sotiropoulos, Antonio Criminisi, Daniel C. Alexander:
Uncertainty Quantification in Deep Learning for Safer Neuroimage Enhancement. CoRR abs/1907.13418 (2019) - [i7]Nichita Diaconu, Daniel E. Worrall:
Affine Self Convolution. CoRR abs/1911.07704 (2019) - [i6]Christina Winkler, Daniel E. Worrall, Emiel Hoogeboom, Max Welling:
Learning Likelihoods with Conditional Normalizing Flows. CoRR abs/1912.00042 (2019) - 2018
- [i5]Daniel E. Worrall, Gabriel J. Brostow:
CubeNet: Equivariance to 3D Rotation and Translation. CoRR abs/1804.04458 (2018) - 2017
- [i4]Ryutaro Tanno, Daniel E. Worrall, Aurobrata Ghosh, Enrico Kaden, Stamatios N. Sotiropoulos, Antonio Criminisi, Daniel C. Alexander:
Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution. CoRR abs/1705.00664 (2017) - [i3]Daniel E. Worrall, Stephan J. Garbin, Daniyar Turmukhambetov, Gabriel J. Brostow:
Interpretable Transformations with Encoder-Decoder Networks. CoRR abs/1710.07307 (2017) - [i2]Saki Shinoda, Daniel E. Worrall, Gabriel J. Brostow:
Virtual Adversarial Ladder Networks For Semi-supervised Learning. CoRR abs/1711.07476 (2017) - 2016
- [i1]Daniel E. Worrall, Stephan J. Garbin, Daniyar Turmukhambetov, Gabriel J. Brostow:
Harmonic Networks: Deep Translation and Rotation Equivariance. CoRR abs/1612.04642 (2016)
Coauthor Index
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