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Found 4 papers, 1 papers with code
On Manifold Learning in Plato's Cave: Remarks on Manifold Learning and Physical Phenomena
Many techniques in machine learning attempt explicitly or implicitly to infer a low-dimensional manifold structure of an underlying physical phenomenon from measurements without an explicit model of the phenomenon or the measurement apparatus.
Methods for Cryo-EM Single Particle Reconstruction of Macromolecules having Continuous Heterogeneity
Macromolecules change their shape (conformation) in the process of carrying out their functions.
Integrating molecular models into CryoEM heterogeneity analysis using scalable high-resolution deep Gaussian mixture models
Resolving the structural variability of proteins is often key to understanding the structure-function relationship of those macromolecular machines.
Image reconstruction in light-sheet microscopy: spatially varying deconvolution and mixed noise
Then, we formulate a variational model that accounts for the combination of Poisson and Gaussian noise through a data fidelity term consisting of the infimal convolution of the single noise fidelities, first introduced in L. Calatroni et al. "Infimal convolution of data discrepancies for mixed noise removal", SIAM Journal on Imaging Sciences 10. 3 (2017), 1196-1233.
