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Search query: formula-driven supervised learning
3 articles match your search "formula-driven supervised learning"Results 1 to 3, sorted by relevance:
![j logo](https://journals.iucr.org/logos/jicons/j_36x36.png)
This paper proposes a ptychographic phase-retrieval algorithm combined with a deep neural network (DNN). The proposed method allows the measurement model to be explicitly incorporated into the DNN-based approach, improving imaging capability and robustness to changes in experimental conditions.
![m logo](https://journals.iucr.org/logos/jicons/m_36x36.png)
The incorporation of prior knowledge about the structures of biological macromolecules into the reconstruction process of cryo-EM structure determination is proposed. Using a novel algorithm inspired by regularization by denoising, it is shown how convolutional neural networks can be used within this framework to improve reconstructions from simulated data.
![s logo](https://journals.iucr.org/logos/jicons/s_36x36.png)
Dissimilar hardware and software conventions at various synchrotrons lead to quantitative differences in experimental results. This paper proposes a method to improve reproducibility of tomographic reconstructions by optimizing the filtering step in commonly used reconstruction algorithms.