Number of items: 3.
2019
Malz, A. I.
and
Hložek, R.
and
Allam, T., Jr.
et al.
(2019)
Photometric LSST Astronomical Time-series Classification Challenge PLAsTiCC: Selection of a Performance Metric for Classification Probabilities Balancing Diverse Science Goals.
Astronomical Journal, 158
(5).
Art. No. 171.
ISSN 1538-3881.
https://resolver.caltech.edu/CaltechAUTHORS:20191010-133618114
Ishida, E. E. O.
and
Beck, R.
and
González-Gaitán, S.
et al.
(2019)
Optimizing spectroscopic follow-up strategies for supernova photometric classification with active learning.
Monthly Notices of the Royal Astronomical Society, 483
(1).
pp. 2-18.
ISSN 0035-8711.
https://resolver.caltech.edu/CaltechAUTHORS:20190411-143047160
2017
Vilalta, R.
and
Ishida, E. E. O.
and
Beck, R.
et al.
(2017)
Photometric redshift estimation: An active learning approach.
In:
2017 IEEE Symposium Series on Computational Intelligence (SSCI).
IEEE
, Piscataway, NJ, pp. 1-8.
ISBN 978-1-5386-2727-3.
https://resolver.caltech.edu/CaltechAUTHORS:20180220-071833166
This list was generated on Fri Aug 14 07:56:41 2020 PDT.
