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Found 4 papers, 3 papers with code
Neural Modulation Fields for Conditional Cone Beam Neural Tomography
Conventional Computed Tomography (CT) methods require large numbers of noise-free projections for accurate density reconstructions, limiting their applicability to the more complex class of Cone Beam Geometry CT (CBCT) reconstruction.
Modelling Long Range Dependencies in $N$D: From Task-Specific to a General Purpose CNN
Performant Convolutional Neural Network (CNN) architectures must be tailored to specific tasks in order to consider the length, resolution, and dimensionality of the input data.
Towards a General Purpose CNN for Long Range Dependencies in $N$D
The use of Convolutional Neural Networks (CNNs) is widespread in Deep Learning due to a range of desirable model properties which result in an efficient and effective machine learning framework.
Exploiting Redundancy: Separable Group Convolutional Networks on Lie Groups
In addition, thanks to the increase in computational efficiency, we are able to implement G-CNNs equivariant to the $\mathrm{Sim(2)}$ group; the group of dilations, rotations and translations.
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