Attentive Neural Processes outperform Gaussian Processes and neural networks on light curve interpolation quality, feature recovery, calibration, and speed for 15 transient classes under realistic Rubin cadences.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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astro-ph.IM 2verdicts
UNVERDICTED 2representative citing papers
Frabjous applies deep learning to classify FRB morphologies into five classes at 55% accuracy by augmenting limited real data with simulations.
citing papers explorer
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Probabilistic Data-Driven Modelling of Astrophysical Transients: The Neural Process Family for Ultrafast and Class-Agnostic Light Curve Reconstruction with NightLANP
Attentive Neural Processes outperform Gaussian Processes and neural networks on light curve interpolation quality, feature recovery, calibration, and speed for 15 transient classes under realistic Rubin cadences.
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Frabjous: Deep Learning Fast Radio Burst Morphologies
Frabjous applies deep learning to classify FRB morphologies into five classes at 55% accuracy by augmenting limited real data with simulations.