ArticleNature machine intelligence2019
Learning with Known Operators reduces Maximum Training Error Bounds.
Article in Nature machine intelligence, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
What it found
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Who cites it
32 citing papers in PubMed.
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- Factors necessary for entrustment decision-making in surgical operating rooms: A modified Delphi study.Journal of Taibah University Medical Sciences · 2024Article
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- SSN2V: unsupervised OCT denoising using speckle split.Scientific reports · 2023Article
- Neural network based formation of cognitive maps of semantic spaces and the putative emergence of abstract concepts.Scientific reports · 2023Article
- ReCSAI: recursive compressed sensing artificial intelligence for confocal lifetime localization microscopy.BMC bioinformatics · 2022Article
- FlowRAU-Net: Accelerated 4D Flow MRI of Aortic Valvular Flows With a Deep 2D Residual Attention Network.IEEE transactions on bio-medical engineering · 2022Article
- Trainable joint bilateral filters for enhanced prediction stability in low-dose CT.Scientific reports · 2022Article
- Neural network based successor representations to form cognitive maps of space and language.Scientific reports · 2022Article
- Deep Learning-Based Classification of Inflammatory Arthritis by Identification of Joint Shape Patterns-How Neural Networks Can Tell Us Where to "Deep Dive" Clinically.Frontiers in medicine · 2022Article
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- Comparison of methods for sensitivity correction in Talbot-Lau computed tomography.International journal of computer assisted radiology and surgery · 2021Review
- Flat-panel conebeam CT in the clinic: history and current state.Journal of medical imaging (Bellingham, Wash.) · 2021Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
We describe an approach for incorporating prior knowledge into machine learning algorithms. We aim at applications in physics and signal processing in which we know that certain operations must be embedded into the algorithm. Any operation that allows computation of a gradient or sub-gradient towards its inputs is suited for our framework. We derive a maximal error bound for deep nets that demonstrates that inclusion of prior knowledge results in its reduction. Furthermore, we also show experimentally that known operators reduce the number of free parameters. We apply this approach to various tasks ranging from CT image reconstruction over vessel segmentation to the derivation of previously unknown imaging algorithms. As such the concept is widely applicable for many researchers in physics, imaging, and signal processing. We assume that our analysis will support further investigation of known operators in other fields of physics, imaging, and signal processing.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.