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Found 4 papers, 2 papers with code
Adaptive scaling of the learning rate by second order automatic differentiation
In the context of the optimization of Deep Neural Networks, we propose to rescale the learning rate using a new technique of automatic differentiation.
Bayesian Optimization of Sampling Densities in MRI
Data-driven optimization of sampling patterns in MRI has recently received a significant attention. Following recent observations on the combinatorial number of minimizers in off-the-grid optimization, we propose a framework to globally optimize the sampling densities using Bayesian optimization.
CorticalFlow: A Diffeomorphic Mesh Deformation Module for Cortical Surface Reconstruction
In this paper we introduce CorticalFlow, a new geometric deep-learning model that, given a 3-dimensional image, learns to deform a reference template towards a targeted object.
Off-the-grid data-driven optimization of sampling schemes in MRI
We propose a novel learning based algorithm to generate efficient and physically plausible sampling patterns in MRI.
