On neural architectures for astronomical time-series classification

@inproceedings{Jamal2020OnNA,
  title={On neural architectures for astronomical time-series classification},
  author={S. Jamal and J. Bloom},
  year={2020}
}
Despite the utility of neural networks (NNs) for astronomical time-series classification, the proliferation of learning architectures applied to diverse datasets has thus far hampered a direct intercomparison of different approaches. Here we perform the first comprehensive study of variants of NN-based learning and inference for astronomical time-series, aiming to provide the community with an overview on relative performance and, hopefully, a set of best-in-class choices for practical… Expand
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References

SHOWING 1-10 OF 100 REFERENCES
Deep Recurrent Neural Networks for Supernovae Classification
  • 52
  • PDF
A CNN adapted to time series for the classification of Supernovae
  • 12
  • PDF
A recurrent neural network for classification of unevenly sampled variable stars
  • 42
  • PDF
Deep Neural Networks to Enable Real-time Multimessenger Astrophysics
  • 111
  • PDF
Deep Neural Network Classifier for Variable Stars with Novelty Detection Capability.
  • 4
  • PDF
Scientific Domain Knowledge Improves Exoplanet Transit Classification with Deep Learning
  • 24
  • PDF
RAPID: Early Classification of Explosive Transients using Deep Learning
  • 35
  • Highly Influential
  • PDF
Rotation-invariant convolutional neural networks for galaxy morphology prediction
  • 397
  • PDF
DASH: Deep Learning for the Automated Spectral Classification of Supernovae and their Hosts
  • 14
  • Highly Influential
  • PDF
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