1 code implementation • 27 Apr 2023 • Roy R. Lederman, Bogdan Toader
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.
no code implementations • 19 Nov 2022 • Bogdan Toader, Fred J. Sigworth, Roy R. Lederman
Macromolecules change their shape (conformation) in the process of carrying out their functions.
no code implementations • 18 Nov 2022 • Muyuan Chen, Bogdan Toader, Roy Lederman
Resolving the structural variability of proteins is often key to understanding the structure-function relationship of those macromolecular machines.
no code implementations • 8 Aug 2021 • Bogdan Toader, Jerome Boulanger, Yury Korolev, Martin O. Lenz, James Manton, Carola-Bibiane Schonlieb, Leila Muresan
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.