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With increased adoption of supervised deep learning methods for processing and analysis of cosmological survey data, the assessment of data perturbation effects (that can naturally occur in the data processing and analysis pipelines) and the development of methods that increase model robustness are increasingly important.
Adversarial examples are a natural consequence of test error in noise
Ford, N., Gilmer, J., Carlini, N., Cubuk, D., 2019 · 1901
Earlier work this paper cites.
Contrastive Adaptation Network for Unsupervised Domain Adaptation
Kang, G., Jiang, L., Yang, Y., Hauptmann, A.G., 2019 · 1901
Earlier work this paper cites.
Liii. on lines and planes of closest fit to systems of points in space
Pearson, K., 1901 · 1901
Earlier work this paper cites.
Cosmological constraints with deep learning from KiDS-450 weak lensing maps
Fluri, J., Kacprzak, T., Lucchi, A., Refregier, A., Amara, A., Hofmann, T., Schneider, A., 2019 · 1906
Earlier work this paper cites.
Galaxy morphology and star formation in the Illustris Simulation at z = 0
Snyder, G.F., Torrey, P., Lotz, J.M., Genel, S., McBride, C.K., Vogelsberger, M., Pillepich, A., Nelson, D., et al., 2015 · 1908
Earlier work this paper cites.
Efficient gravitational-wave glitch identification from environmental data through machine learning
Colgan, R.E., Corley, K.R., Lau, Y., Bartos, I., Wright, J.N., Márka, Z., Márka, S., 2020 · 1911
Earlier work this paper cites.
On information and sufficiency
Kullback, S., Leibler, R.A., 1951 · 1951
Earlier work this paper cites.
Variance formulas for the mean difference and coefficient of concentration
Glasser, G.J., 1962 · 1962
Earlier work this paper cites.
Influence of the atmospheric and instrumental dispersion on the brightness distribution in a galaxy
Sérsic, J.L., 1963 · 1963
Earlier work this paper cites.
Divergence measures based on the shannon entropy
Lin, J., 1991 · 1991
Earlier work this paper cites.
Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces
Storn, R., Price, K., 1997 · 1997
Earlier work this paper cites.
Nonlinear dimensionality reduction by locally linear embedding
Roweis, S.T., Saul, L.K., 2000 · 2000
Earlier work this paper cites.
A Global Geometric Framework for Nonlinear Dimensionality Reduction
Tenenbaum, J.B., de Silva, V., Langford, J.C., 2000 · 2000
Earlier work this paper cites.
Universal Domain Adaptation through Self Supervision
Saito, K., Kim, D., Sclaroff, S., Saenko, K., 2020 · 2002
Earlier work this paper cites.
A Direct Measurement of Major Galaxy Mergers at z¡ ~
Conselice, C.J., Bershady, M.A., Dickinson, M., Papovich, C., 2003 · 2003
Earlier work this paper cites.
DeepMerge: Classifying high-redshift merging galaxies with deep neural networks
Ćiprijanović, A., Snyder, G.F., Nord, B., Peek, J.E.G., 2020b · 2004
Earlier work this paper cites.
A new nonparametric approach to galaxy morphological classification
Lotz, J.M., Primack, J., Madau, P., 2004 · 2004
Earlier work this paper cites.
Modern Multidimensional Scaling: Theory and Applications (Springer Series in Statistics)
Borg, I., Groenen, P., 2005 · 2005
Earlier work this paper cites.
A kernel method for the two-sample-problem, in: Advances in Neural Information Processing Systems 19, MIT Press. pp. 513–520
Gretton, A., Borgwardt, K., Rasch, M., Schölkopf, B., Smola, A., 2007 · 2007
Earlier work this paper cites.
A Hilbert space embedding for distributions, in: Algorithmic Learning Theory, Lecture Notes in Computer Science 4754, Springer. pp. 13–31
Smola, A., Gretton, A., Song, L., Schölkopf, B., 2007 · 2007
Earlier work this paper cites.
Galaxy zoo: Morphologies derived from visual inspection of galaxies from the Sloan digital sky survey
Lintott, C.J., Schawinski, K., Slosar, A., Land, K., Bamford, S., Thomas, D., Raddick, M.J., Nichol, R.C., et. al., 2008 · 2008
Earlier work this paper cites.
Visualizing data using t-SNE
van der Maaten, L., Hinton, G., 2008 · 2008
Earlier work this paper cites.
Galaxy Zoo: the fraction of merging galaxies in the SDSS and their morphologies
Darg, D.W., Kaviraj, S., Lintott, C.J., Schawinski, K., Sarzi, M., Bamford, S., Silk, J., Proctor, R., et al., 2010 · 2009
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database, in: 2009 IEEE conference on computer vision and pattern recognition, Ieee. pp. 248–255
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L., 2009 · 2009
Earlier work this paper cites.
Differential evolution: A survey of the state-of-the-art
Das, S., Suganthan, P.N., 2011 · 2010
Earlier work this paper cites.
Galaxy Zoo 1: data release of morphological classifications for nearly 900 000 galaxies*
Lintott, C., Schawinski, K., Bamford, S., Slosar, A., Land, K., Thomas, D., Edmondson, E., Masters, K., et al., 2010 · 2010
Earlier work this paper cites.
Domain adaptation techniques for improved cross-domain study of galaxy mergers
Ćiprijanović, A., Kafkes, D., Jenkins, S., Downey, K., Perdue, G.N., Madireddy, S., Johnston, T., Nord, B., 2020a · 2011
Earlier work this paper cites.
Scikit-learn: Machine learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., et al., 2011 · 2011
Earlier work this paper cites.
DeepShadows: Separating low surface brightness galaxies from artifacts using deep learning
Tanoglidis, D., Ćiprijanović, A., Drlica-Wagner, A., 2021a · 2011
Earlier work this paper cites.
A kernel two-sample test
Gretton, A., Borgwardt, K.M., Rasch, M.J., Schölkopf, B., Smola, A., 2012 · 2012
Earlier work this paper cites.
Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R., 2013 · 2013
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Goodfellow, I.J., Shlens, J., Szegedy, C., 2014 · 2014
Cited alongside, same era.
Towards deep neural network architectures robust to adversarial examples
Gu, S., Rigazio, L., 2014 · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D.P., Ba, J., 2014 · 2014
Cited alongside, same era.
Vogelsberger, M., Genel, S., Springel, V., Torrey, P., Sijacki, D., Xu, D., Snyder, G., Nelson, D., Hernquist, L., 2014 · 2014
Cited alongside, same era.
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
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
Later among the works it cites.
Naiman, J.P., Pillepich, A., Springel, V., Ramirez-Ruiz, E., Torrey, P., Vogelsberger, M., Pakmor, R., Nelson, D., et al., 2018 · 2018
Later among the works it cites.
Pillepich, A., Nelson, D., Hernquist, L., Springel, V., Pakmor, R., Torrey, P., Weinberger, R., Genel, S., et al., 2018 · 2018
Later among the works it cites.
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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Cited alongside, same era.
Sugai, H., Tamura, N., Karoji, H., Shimono, A., Takato, N., Kimura, M., Ohyama, Y., Ueda, A., et al., 2015 · 2015
Cited alongside, same era.
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
Cited alongside, same era.
Understanding How Image Quality Affects Deep Neural Networks
Dodge, S., Karam, L., 2016 · 2016
Cited alongside, same era.
Domain-adversarial training of neural networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., March, M., Lempitsky, V., 2016 · 2016
Cited alongside, same era.
The effect of distortions on the prediction of visual attention
Gide, M.S., Dodge, S.F., Karam, L.J., 2016 · 2016
Cited alongside, same era.
Deep residual learning for image recognition, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778
He, K., Zhang, X., Ren, S., Sun, J., 2016 · 2016
Cited alongside, same era.
Distillation as a defense to adversarial perturbations against deep neural networks
Papernot, N., Mcdaniel, P., Wu, X., Jha, S., Swami, A., 2016 · 2016
Cited alongside, same era.
Later among the works it cites.
Deep learning for galaxy surface brightness profile fitting
Tuccillo, D., Huertas-Company, M., Decencière, E., Velasco-Forero, S., Domínguez Sánchez, H., Dimauro, P., 2018 · 2018
Later among the works it cites.
Deep visual domain adaptation: A survey
Wang, M., Deng, W., 2018 · 2018
Later among the works it cites.
Adversarial examples: Attacks and defenses for deep learning
Yuan, X., He, P., Zhu, Q., Li, X., 2019 · 2018
Later among the works it cites.
Sky Surveys Scheduling Using Reinforcement Learning
Alba Hernandez, A.F., 2019 · 2019
Later among the works it cites.
Transfer learning for galaxy morphology from one survey to another
Domínguez Sánchez, H., Huertas-Company, M., Bernardi, M., Kaviraj, S., Fischer, J.L., Abbott, T.M.C., Abdalla, F.B., Annis, J., et al., 2019 · 2019
Later among the works it cites.
Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D., Dietterich, T., 2019 · 2019
Later among the works it cites.
Adversarial examples are not bugs, they are features, in: Wallach, H., Larochelle, H., Beygelzimer, A., d'Alché-Buc, F., Fox, E., Garnett, R. (Eds.), Advances in Neural Information Processing Systems, Curran Associates, Inc
Ilyas, A., Santurkar, S., Tsipras, D., Engstrom, L., Tran, B., Madry, A., 2019 · 2019
Later among the works it cites.
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
Later among the works it cites.
A Framework for Telescope Schedulers: With Applications to the Large Synoptic Survey Telescope
Naghib, E., Yoachim, P., Vanderbei, R.J., Connolly, A.J., Jones, R.L., 2019 · 2019
Later among the works it cites.
The IllustrisTNG simulations: public data release
Nelson, D., Springel, V., Pillepich, A., Rodriguez-Gomez, V., Torrey, P., Genel, S., Vogelsberger, M., Pakmor, R., et al., 2019 · 2019
Later among the works it cites.
Rodriguez-Gomez, V., Snyder, G.F., Lotz, J.M., Nelson, D., Pillepich, A., Springel, V., Genel, S., Weinberger, R., et al., 2019 · 2019
Later among the works it cites.
One pixel attack for fooling deep neural networks
Su, J., Vargas, D.V., Sakurai, K., 2019 · 2019
Later among the works it cites.
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
Later among the works it cites.
Interpreting robust optimization via adversarial influence functions, in: International Conference on Machine Learning, PMLR. pp. 2464–2473
Deng, Z., Dwork, C., Wang, J., Zhang, L., 2020 · 2020
Later among the works it cites.
Implicit euler skip connections: Enhancing adversarial robustness via numerical stability, in: International Conference on Machine Learning, PMLR. pp. 5874–5883
Li, M., He, L., Lin, Z., 2020 · 2020
Later among the works it cites.
A survey of unsupervised deep domain adaptation
Wilson, G., Cook, D.J., 2020 · 2020
Later among the works it cites.
Fisher deep domain adaptation, in: Proceedings of the 2020 SIAM International Conference on Data Mining (SDM), pp. 469–477
Zhang, Y., Zhang, Y., Wei, Y., Bai, K., Song, Y., Yang, Q., 2020 · 2020
Later among the works it cites.
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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La Plante, P., Williams, P.K.G., Kolopanis, M., Dillon, J.S., Beardsley, A.P., Kern, N.S., Wilensky, M., Ali, Z.S., et al., 2021 · 2021
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DeepSZ: identification of Sunyaev-Zel’dovich galaxy clusters using deep learning
Lin, Z., Huang, N., Avestruz, C., Wu, W.L.K., Trivedi, S., Caldeira, J., Nord, B., 2021 · 2021
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Sanchez, J., Mendoza, I., Kirkby, D.P., Burchat, P.R., LSST Dark Energy Science Collaboration, 2021 · 2021
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Bayesian inference with certifiable adversarial robustness, in: International Conference on Artificial Intelligence and Statistics, PMLR. pp. 2431–2439
Wicker, M., Laurenti, L., Patane, A., Chen, Z., Zhang, Z., Kwiatkowska, M., 2021 · 2021
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