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Artificial intelligence methods show great promise in increasing the quality and speed of work with large astronomical datasets, but the high complexity of these methods leads to the extraction of dataset-specific, non-robust features.
SIMBA: Cosmological simulations with black hole growth and feedback
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Galaxy morphology and star formation in the Illustris Simulation at z = 0
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The Sloan Digital Sky Survey: Technical Summary
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A new nonparametric approach to galaxy morphological classification
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Galaxy Zoo 1: data release of morphological classifications for nearly 900 000 galaxies*
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Galaxy Zoo 1: data release of morphological classifications for nearly 900 000 galaxies
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Erratum: The eight data release of the Sloan Digital Sky Survey: First data from SDSS-III (2011, ApJS, 193, 29)
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A kernel two-sample test
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First results from the IllustrisTNG simulations: radio haloes and magnetic fields
Marinacci, F., Vogelsberger, M., Pakmor, R., Torrey, P., Springel, V., Hernquist, L., Nelson, D., Weinberger, R., et al., 2018 · 2018
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Naiman, J.P., Pillepich, A., Springel, V., Ramirez-Ruiz, E., Torrey, P., Vogelsberger, M., Pakmor, R., Nelson, D., et al., 2018 · 2018
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First results from the IllustrisTNG simulations: matter and galaxy clustering
Springel, V., Pakmor, R., Pillepich, A., Weinberger, R., Nelson, D., Hernquist, L., Vogelsberger, M., Genel, S., et al., 2018 · 2018
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Sutskever, I., Martens, J., Dahl, G., Hinton, G., 2013 · 2013
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Willett, K.W., Lintott, C.J., Bamford, S.P., Masters, K.L., Simmons, B.D., Casteels, K.R.V., Edmondson, E.M., Fortson, L.F., Kaviraj, S., Keel, W.C., et al., 2013 · 2013
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Vogelsberger, M., Genel, S., Springel, V., Torrey, P., Sijacki, D., Xu, D., Snyder, G., Nelson, D., Hernquist, L., 2014 · 2014
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The illustris simulation: Public data release
Nelson, D., Pillepich, A., Genel, S., Vogelsberger, M., Springel, V., Torrey, P., Rodriguez-Gomez, V., Sijacki, D., Snyder, G.F., Griffen, B., Marinacci, F., Blecha, L., Sales, L., Xu, D., Hernquist, L., 2015 · 2015
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GALSIM: The modular galaxy image simulation toolkit
Rowe, B.T.P., Jarvis, M., Mandelbaum, R., Bernstein, G.M., Bosch, J., Simet, M., Meyers, J.E., Kacprzak, T., et al., 2015 · 2015
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The EAGLE project: simulating the evolution and assembly of galaxies and their environments
Schaye, J., Crain, R.A., Bower, R.G., Furlong, M., Schaller, M., Theuns, T., Dalla Vecchia, C., Frenk, C.S., McCarthy, I.G., Helly, J.C., et al., 2015 · 2015
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Taking the human out of the loop: A review of bayesian optimization
Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N., 2016 · 2015
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The Dark Energy Survey: more than dark energy - an overview
Dark Energy Survey Collaboration, Abbott, T., Abdalla, F.B., Aleksić, J., Allam, S., Amara, A., Bacon, D., Balbinot, E., Banerji, M., et al., 2016 · 2016
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Deep visual domain adaptation: A survey
Wang, M., Deng, W., 2018 · 2018
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Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination
Wu, Z., Xiong, Y., Yu, S., Lin, D., 2018 · 2018
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Importance weighted adversarial nets for partial domain adaptation
Zhang, J., Ding, Z., Li, W., Ogunbona, P., 2018 · 2018
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Overview of the DESI Legacy Imaging Surveys
Dey, A., Schlegel, D.J., Lang, D., Blum, R., Burleigh, K., Fan, X., Findlay, J.R., Finkbeiner, D., Herrera, D., Juneau, S., et al., 2019 · 2019
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Improved open set domain adaptation with backpropagation, in: 2019 IEEE International Conference on Image Processing (ICIP), pp. 2506–2510
Fu, J., Wu, X., Zhang, S., Yan, J., 2019 · 2019
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LSST: From science drivers to reference design and anticipated data products
Ivezić, Ž., Kahn, S.M., Tyson, J.A., Abel, B., Acosta, E., Allsman, R., Alonso, D., AlSayyad, Y., et al., 2019 · 2019
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Separate to adapt: Open set domain adaptation via progressive separation, in: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2922–2931
Liu, H., Cao, Z., Long, M., Wang, J., Yang, Q., 2019 · 2019
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The IllustrisTNG simulations: public data release
Nelson, D., Springel, V., Pillepich, A., Rodriguez-Gomez, V., Torrey, P., Genel, S., Vogelsberger, M., Pakmor, R., Marinacci, F., Weinberger, R., Kelley, L., Lovell, M., Diemer, B., Hernquist, L., 2019 · 2019
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Snyder, G.F., Rodriguez-Gomez, V., Lotz, J.M., Torrey, P., Quirk, A.C.N., Hernquist, L., Vogelsberger, M., Freeman, P.E., 2019 · 2019
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A General Approach to Domain Adaptation with Applications in Astronomy
Vilalta, R., Dhar Gupta, K., Boumber, D., Meskhi, M.M., 2019 · 2019
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Universal domain adaptation, in: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2715–2724
You, K., Long, M., Cao, Z., Wang, J., Jordan, M.I., 2019 · 2019
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Robust unsupervised domain adaptation for neural networks via moment alignment
Zellinger, W., Moser, B.A., Grubinger, T., Lughofer, E., Natschläger, T., Saminger-Platz, S., 2019 · 2019
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A survey of unsupervised deep domain adaptation
Wilson, G., Cook, D.J., 2020 · 2020
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Domain Adaptation for Simulation-Based Dark Matter Searches Using Strong Gravitational Lensing
Alexander, S., Gleyzer, S., Reddy, P., Tidball, M., Toomey, M.W., 2021 · 2021
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Morphological classification of galaxies with deep learning: comparing 3-way and 4-way CNNs
Cavanagh, M.K., Bekki, K., Groves, B.A., 2021 · 2021
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Cheng, T.Y., Conselice, C.J., Aragón-Salamanca, A., Aguena, M., Allam, S., Andrade-Oliveira, F., Annis, J., Bluck, A.F.L., Brooks, D., Burke, D.L., et al., 2021 · 2021
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Ćiprijanović, A., Kafkes, D., Downey, K., Jenkins, S., Perdue, G.N., Madireddy, S., Johnston, T., Snyder, G.F., Nord, B., 2021 · 2021
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Unsupervised Domain Adaptation for Constraining Star Formation Histories
Gilda, S., de Mathelin, A., Bellstedt, S., Richard, G., 2021 · 2021
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Cross-domain adaptive clustering for semi-supervised domain adaptation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2505–2514
Li, J., Li, G., Shi, Y., Yu, Y., 2021 · 2021
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Thota, M., Leontidis, G., 2021 · 2021
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Partial video domain adaptation with partial adversarial temporal attentive network
Xu, Y., Yang, J., Cao, H., Li, Q., Mao, K., Chen, Z., 2021 · 2021
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Ćiprijanović, A., Kafkes, D., Snyder, G., Sánchez, F.J., Perdue, G.N., Pedro, K., Nord, B., Madireddy, S., Wild, S.M., 2022 · 2022
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The DAWES review 10: The impact of deep learning for the analysis of galaxy surveys
Huertas-Company, M., Lanusse, F., 2022 · 2022
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Learning useful representations for radio astronomy “in the wild” with contrastive learning
Slijepcevic, I.V., Scaife, A.M.M., Walmsley, M., Bowles, M., 2022 · 2022
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Adaptive open domain recognition by coarse-to-fine prototype-based network
Yuan, Y., He, X., Jiang, Z., 2022 · 2022
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