Cargando…
Equivariant neural networks for inverse problems
In recent years the use of convolutional layers to encode an inductive bias (translational equivariance) in neural networks has proven to be a very fruitful idea. The successes of this approach have motivated a line of research into incorporating other symmetries into deep learning methods, in the f...
Autores principales: | Celledoni, Elena, Ehrhardt, Matthias J, Etmann, Christian, Owren, Brynjulf, Schönlieb, Carola-Bibiane, Sherry, Ferdia |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
IOP Publishing
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8317019/ https://www.ncbi.nlm.nih.gov/pubmed/34334869 http://dx.doi.org/10.1088/1361-6420/ac104f |
Ejemplares similares
-
Variational regularisation for inverse problems with imperfect forward operators and general noise models
por: Bungert, Leon, et al.
Publicado: (2020) -
INSIDEnet: Interpretable NonexpanSIve Data‐Efficient network for denoising in grating interferometry breast CT
por: van Gogh, Stefano, et al.
Publicado: (2022) -
Equivariant
Graph Neural Networks for Toxicity Prediction
por: Cremer, Julian, et al.
Publicado: (2023) -
Calibrating the Dice Loss to Handle Neural Network Overconfidence for Biomedical Image Segmentation
por: Yeung, Michael, et al.
Publicado: (2022) -
3D-equivariant graph neural networks for protein model quality assessment
por: Chen, Chen, et al.
Publicado: (2023)