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- W4386363057 abstract "The group synchronization problem involves esti-mating a collection of group elements from noisy measurements of their pairwise ratios. This task is a key component in many computational problems, including the molecular reconstruction problem in single-particle cryo-electron microscopy (cryo-EM). The standard methods to estimate the group elements are based on iteratively applying linear and non-linear operators, and are not necessarily optimal. Motivated by the structural similarity to deep neural networks, we adopt the concept of algorithm unrolling, where training data is used to optimize the algorithm. We design unrolled algorithms for several group synchronization instances, including synchronization over the group of 3-D rota-tions: the synchronization problem in cryo-EM. We also apply a similar approach to the multi-reference alignment problem. We show by numerical experiments that the unrolling strategy outperforms existing synchronization algorithms in a wide variety of scenarios." @default.
- W4386363057 created "2023-09-02" @default.
- W4386363057 creator A5000527961 @default.
- W4386363057 creator A5067327689 @default.
- W4386363057 date "2023-01-01" @default.
- W4386363057 modified "2023-09-27" @default.
- W4386363057 title "Unrolled Algorithms for Group Synchronization" @default.
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- W4386363057 doi "https://doi.org/10.1109/ojsp.2023.3311354" @default.
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