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Found 4 papers, 2 papers with code
Continuous Learned Primal Dual
Neural ordinary differential equations (Neural ODEs) propose the idea that a sequence of layers in a neural network is just a discretisation of an ODE, and thus can instead be directly modelled by a parameterised ODE.
Navigating the challenges in creating complex data systems: a development philosophy
In this perspective, we argue that despite the democratization of powerful tools for data science and machine learning over the last decade, developing the code for a trustworthy and effective data science system (DSS) is getting harder.
Numerically robust tetrahedron-based tomographic forward and backward projectors on parallel architectures
X-ray tomographic reconstruction typically uses voxel basis functions to represent volumetric images.
Distributed, Parallel, and Cluster Computing
Arbitrarily large iterative tomographic reconstruction on multiple GPUs using the TIGRE toolbox
Tomographic image sizes keep increasing over time and while the GPUs that compute the tomographic reconstruction are also increasing in memory size, they are not doing so fast enough to reconstruct the largest datasets.
Distributed, Parallel, and Cluster Computing
