Model family
Probabilistic graphical model
A model of how many variables depend on one another, written as a graph of conditional dependencies — Markov random fields, Bayesian networks, maximum-entropy (Potts) models.
10articles
5Structural biology
3Materials & chemistry
2Astronomy
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Reconstructing the early universe's density field with a smoothed map of cosmic structure
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Robot laser-heats thin films while software picks each next heating condition
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Bayesian program merges X-ray diffraction data from three experiment types in one step
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Machine learning tool flags protein helices likely to bind DNA or RNA
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Eight machine learning methods sorted supernova gravitational wave signals by nuclear physics model
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Neural network built from physics equations extracts solder deformation coefficients
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Machine learning picked which engineered enzymes to build for fatty alcohol production
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Software works out protein shapes from electron microscope images using statistics
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A learning algorithm ran a synchrotron beamline to find a phase-change material
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