Paper Citation Record · LEDGER

Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging

As of 20 August 2026, this Paper Citation Record lists 71 of 71 outbound references and 2 inbound Pith citation observations for arXiv:1908.03610.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1908.03610 v2

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Reference resolution

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Outbound references

Observation fad640ac-3e01-43f9-866c-8089e5ad3bf2 · outbound

This paper cites write newline.

Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging write newline

Reference 1

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging Unresolved cited work

Reference 2

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This paper cites G., van den Bergh S., Nair P., 2003, @doi [ ] 10.1086/373919 , https://ui.adsabs.harvard.edu/abs/2003ApJ...588..218A 588, 218.

Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging G., van den Bergh S., Nair P., 2003, @doi [ ] 10.1086/373919 , https://ui.adsabs.harvard.edu/abs/2003ApJ...588..218A 588, 218

Reference 3

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This paper cites E., Luo W., 2019, @doi [ ] 10.3847/1538-4357/ab16d9 , https://ui.adsabs.harvard.edu/abs/2019ApJ...877...58A 877, 58.

Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging E., Luo W., 2019, @doi [ ] 10.3847/1538-4357/ab16d9 , https://ui.adsabs.harvard.edu/abs/2019ApJ...877...58A 877, 58

Reference 4

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Observation 2633c733-259d-4c67-8f2b-241415a84e26 · outbound

This paper cites M., Brunner R.

Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging M., Brunner R

Reference 6

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This paper cites R., et al., 2018, @doi [ ] 10.1093/mnras/sty503 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.476.5516B 476, 5516.

Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging R., et al., 2018, @doi [ ] 10.1093/mnras/sty503 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.476.5516B 476, 5516

Reference 9

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Observation e72f7514-c859-46c0-a29b-133e3aebf5a1 · outbound

This paper cites M., 2006, Pattern Recognition and Machine Learning (Information Science and Statistics).

Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging M., 2006, Pattern Recognition and Machine Learning (Information Science and Statistics)

Reference 10

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This paper cites P., 1997, @doi [Pattern Recognition] https://doi.org/10.1016/S0031-3203(96)00142-2 , 30, 1145.

Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging P., 1997, @doi [Pattern Recognition] https://doi.org/10.1016/S0031-3203(96)00142-2 , 30, 1145

Reference 11

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Observation 388447f7-e7a6-4164-8c6a-72bb318b3d05 · outbound

This paper cites Learn.] 10.1023/A:1010933404324 , 45, 5–32.

Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging Learn.] 10.1023/A:1010933404324 , 45, 5–32

Reference 12

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging Unresolved cited work

Reference 13

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This paper cites J., 2003, @doi [ ] 10.1086/375001 , https://ui.adsabs.harvard.edu/abs/2003ApJS..147....1C 147, 1.

Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging J., 2003, @doi [ ] 10.1086/375001 , https://ui.adsabs.harvard.edu/abs/2003ApJS..147....1C 147, 1

Reference 14

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This paper cites pp 273--297.

Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging pp 273--297

Reference 15

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging Unresolved cited work

Reference 16

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This paper cites J., 2007, k-Nearest Neighbour Classifiers.

Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging J., 2007, k-Nearest Neighbour Classifiers

Reference 17

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Observation 3ee1e4e8-4d36-4b73-9e04-0324d88d6491 · outbound

This paper cites pp 886--893 vol.

Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging pp 886--893 vol

Reference 18

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This paper cites W., Dambre J., 2015, @doi [ ] 10.1093/mnras/stv632 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.450.1441D 450, 1441.

Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging W., Dambre J., 2015, @doi [ ] 10.1093/mnras/stv632 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.450.1441D 450, 1441

Reference 19

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging L., 2018, @doi [ ] 10.1093/mnras/sty338 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.476.3661D 476, 3661

Reference 20

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging Unresolved cited work

Reference 21

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging Unresolved cited work

Reference 22

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This paper cites M., Elyan E., 2014, @doi [Systems Science & Control Engineering] 10.1080/21642583.2014.956265 , 2, 602.

Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging M., Elyan E., 2014, @doi [Systems Science & Control Engineering] 10.1080/21642583.2014.956265 , 2, 602

Reference 23

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging Unresolved cited work

Reference 24

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This paper cites L., 1989, International Statistical Review / Revue Internationale de Statistique, 57, 238.

Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging L., 1989, International Statistical Review / Revue Internationale de Statistique, 57, 238

Reference 25

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging Unresolved cited work

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging Unresolved cited work

Reference 27

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging Unresolved cited work

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging Unresolved cited work

Reference 29

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging N., Lolling S

Reference 31

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging E., 2002, @doi [Neural Comput.] 10.1162/089976602760128018 , 14, 1771–1800

Reference 32

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging E., Sun Y., Davey N., 2018, @doi [ ] 10.1093/mnras/stx2351 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.473.1108H 473, 1108

Reference 33

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This paper cites Department of Computer Science, National Taiwan University, http://www.csie.ntu.edu.tw/ cjlin/papers.html.

Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging Department of Computer Science, National Taiwan University, http://www.csie.ntu.edu.tw/ cjlin/papers.html

Reference 34

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging P., 1926, @doi [ ] 10.1086/143018 , https://ui.adsabs.harvard.edu/abs/1926ApJ....64..321H 64, 321

Reference 35

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging Unresolved cited work

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging Unresolved cited work

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging Unresolved cited work

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging Unresolved cited work

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging M., Hogg D

Reference 41

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging M., Hegadi R

Reference 42

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging Unresolved cited work

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging D., Polsterer K., Hoecker M., 2015, @doi [ ] 10.1051/0004-6361/201424801 , https://ui.adsabs.harvard.edu/abs/2015A&A...576A.132K 576, A132

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging J., Storrie-Lombardi M

Reference 45

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging Unresolved cited work

Reference 46

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging J., et al., 2008, @doi [ ] 10.1111/j.1365-2966.2008.13689.x , https://ui.adsabs.harvard.edu/abs/2008MNRAS.389.1179L 389, 1179

Reference 47

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging Unresolved cited work

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging H., Hakala P

Reference 49

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Observation d7e58992-5c87-47d9-a720-6c7d1b3fe93c · outbound

This paper cites Chapman and Hall/CRC Monographs on Statistics and Applied Probability Series, Chapman & Hall, http://books.google.com/books?id=h9kFH2\_FfBkC.

Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging Chapman and Hall/CRC Monographs on Statistics and Applied Probability Series, Chapman & Hall, http://books.google.com/books?id=h9kFH2\_FfBkC

Reference 50

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging B., et al., 2019, @doi [ ] 10.1051/0004-6361/201832797 , https://ui.adsabs.harvard.edu/abs/2019A&A...625A.119M 625, A119

Reference 51

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging J., Storrie-Lombardi M

Reference 52

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Observation cd489978-3208-4bf8-9d56-73b7efa31e83 · outbound

This paper cites C., Stockwell E.

Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging C., Stockwell E

Reference 53

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Observation 9163e554-08f3-4583-8ad6-64cdb5ca3f51 · outbound

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging Unresolved cited work

Reference 54

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Observation 182ff41b-556a-4876-98e9-b3ca25e71fc3 · outbound

This paper cites Scikit-learn: Machine Learning in Python.

Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging Scikit-learn: Machine Learning in Python

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-14T14:11:27.445809Z

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This paper cites L., Gieseke F., Kramer O., 2012, Galaxy Classification without Feature Extraction.

Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging L., Gieseke F., Kramer O., 2012, Galaxy Classification without Feature Extraction

Reference 56

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Reference 57

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging Unresolved cited work

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This paper cites E., Hinton G.

Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging E., Hinton G

Reference 59

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging ICML ’07

Reference 60

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging J., 2001, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:11:28.704826Z

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging Unresolved cited work

Reference 62

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This paper cites D., Fukunaga K., 1981, IEEE Trans.

Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging D., Fukunaga K., 1981, IEEE Trans

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:11:28.691660Z

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Reference 64

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Observation 1fa40cf0-954d-4d66-ba66-8df3a7f792fc · outbound

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging MIT Press, Cambridge, MA, USA, p

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:11:28.677838Z

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Observation eaea0b67-b0b1-4a00-b809-ba1b859bdcae · outbound

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging Unresolved cited work

Reference 66

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging C., Lahav O., Sodre L

Reference 67

Resolution: verified exact; doi, observed 2026-08-14T14:11:27.589479Z

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Observation 67cf833d-ed4d-4a4d-a2ec-c2efa5c57a98 · outbound

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging M., 1950, @doi [Mind] 10.1093/mind/LIX.236.433 , LIX, 433

Reference 68

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This paper cites N., 1995, The Nature of Statistical Learning Theory.

Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging N., 1995, The Nature of Statistical Learning Theory

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:11:28.663930Z

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This paper cites M., Djorgovski S., 1995, @doi [ ] 10.1086/117459 , https://ui.adsabs.harvard.edu/abs/1995AJ....109.2401W 109, 2401.

Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging M., Djorgovski S., 1995, @doi [ ] 10.1086/117459 , https://ui.adsabs.harvard.edu/abs/1995AJ....109.2401W 109, 2401

Reference 70

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging Unresolved cited work

Reference 71

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Observation 4595cbe4-b2df-4c17-93b6-004c17d02f6d · outbound

This paper cites W., et al., 2013, @doi [ ] 10.1093/mnras/stt1458 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.435.2835W 435, 2835.

Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging W., et al., 2013, @doi [ ] 10.1093/mnras/stt1458 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.435.2835W 435, 2835

Reference 72

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Observation 9992ccff-6ac1-4127-aef2-1cbfcf6c6299 · outbound

This paper cites Y., 2003, in In ICML 2003 Workshop on Learning from Imbalanced Data Sets.

Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging Y., 2003, in In ICML 2003 Workshop on Learning from Imbalanced Data Sets

Reference 73

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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging Unresolved cited work

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Observation 1946dd26-2426-45ab-a2d9-d320553e6fac · outbound

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Reference 75

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Pith citing papers

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Deblending and Classifying Astronomical Sources with Mask R-CNN Deep Learning cites this paper.

Deblending and Classifying Astronomical Sources with Mask R-CNN Deep Learning Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging

Reference 21

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Observation 74e401a4-a65e-4718-8dbc-2475e2d0d1bf · inbound

Uncertainty-Aware Deep Learning for the Ly$\alpha$ Forest: CNN-Based Absorber Detection and Characterization cites this paper.

Uncertainty-Aware Deep Learning for the Ly$\alpha$ Forest: CNN-Based Absorber Detection and Characterization Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging

Reference 25

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