| dc.contributor.author |
Seth, Kshiteej et al. |
|
| dc.date.accessioned |
2020-05-15T12:42:39Z |
|
| dc.date.available |
2020-05-15T12:42:39Z |
|
| dc.date.issued |
2017-11 |
|
| dc.identifier.citation |
Seth, Kshiteej et al., "Deep-learning the time domain", Proceedings of the International Astronomical Union, DOI: 10.1017/S1743921318002491, vol. 14, no. S339, pp. 165-171, Nov. 2017. |
en_US |
| dc.identifier.issn |
1743-9213 |
|
| dc.identifier.issn |
1743-9221 |
|
| dc.identifier.uri |
http://dx.doi.org/10.1017/S1743921318002491 |
|
| dc.identifier.uri |
https://repository.iitgn.ac.in/handle/123456789/5394 |
|
| dc.description.abstract |
"Deep learning" is finding more and more applications everywhere, and astronomy is not an exception. This talk described the application of convolutional neural networks to time-domain astronomy, specifically to light-curves of sources. The work that is discussed is based on a published paper to which reference can be made for more detail. The talk finished with a note cautioning new practitioners about the pitfalls lurking in out-of-the-box use of deep-learning techniques. |
|
| dc.description.statementofresponsibility |
by Kshiteej Seth et al. |
|
| dc.format.extent |
vol. 14, no. S339, pp. 165-171 |
|
| dc.language.iso |
en_US |
en_US |
| dc.publisher |
Cambridge University Press |
en_US |
| dc.subject |
Techniques: image processing |
|
| dc.subject |
Methods: data analysis |
|
| dc.subject |
Surveys |
|
| dc.title |
Deep-learning the time domain |
en_US |
| dc.type |
Article |
en_US |
| dc.relation.journal |
Proceedings of the International Astronomical Union |
|