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The prevalent paradigm of machine learning today is to use past observations to predict future ones.
The Luminosity Function and Stellar Evolution
Salpeter, E. E. 1955 · 1955
Earlier work this paper cites.
The discrete wavelet transform: wedding the a trous and Mallat algorithms
Shensa, M. J. 1992 · 1992
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Advances in kernel methods: support vector learning
Soentpiet, R.; et al. 1999 · 1999
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On the variation of the initial mass function
Kroupa, P. 2001 · 2001
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Stellar population synthesis at the resolution of 2003
Bruzual, G.; and Charlot, S. 2003 · 2003
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Galactic Stellar and Substellar Initial Mass Function
Chabrier, G. 2003 · 2003
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The Origin of the Mass-Metallicity Relation: Insights from 53,000 Star-forming Galaxies in the Sloan Digital Sky Survey
Tremonti, C. A.; Heckman, T. M.; Kauffmann, G.; Brinchmann, J.; Charlot, S.; White, S. D. M.; Seibert, M.; Peng, E. W.; Schlegel, D. J.; Uomoto, A.; Fukugita, M.; and Brinkmann, J. 2004 · 2004
Earlier work this paper cites.
Adversarial weighting for domain adaptation in regression
de Mathelin, A.; Richard, G.; Mougeot, M.; and Vayatis, N. 2020 · 2006
Earlier work this paper cites.
Boosting for Transfer Learning
Dai, W.; Yang, Q.; Xue, G.-R.; and Yu, Y. 2007 · 2007
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Correcting Sample Selection Bias by Unlabeled Data
Huang, J.; Gretton, A.; Borgwardt, K.; Schölkopf, B.; and Smola, A. J. 2007 · 2007
Earlier work this paper cites.
Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation
Sugiyama, M.; Nakajima, S.; Kashima, H.; Bünau, P. v.; and Kawanabe, M. 2007 · 2007
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A simple model to interpret the ultraviolet, optical and infrared emission from galaxies
da Cunha, E.; Charlot, S.; and Elbaz, D. 2008 · 2008
Earlier work this paper cites.
Domain Adaptation: Learning Bounds and Algorithms
Mansour, Y.; Mohri, M.; and Rostamizadeh, A. 2009 · 2009
Earlier work this paper cites.
Analysis of galaxy spectral energy distributions from far-UV to far-IR with CIGALE: studying a SINGS test sample
Noll, S.; Burgarella, D.; Giovannoli, E.; Buat, V.; Marcillac, D.; and Muñoz-Mateos, J. C. 2009 · 2009
Earlier work this paper cites.
Co-regularization based semi-supervised domain adaptation
Kumar, A.; Saha, A.; and Daume, H. 2010 · 2010
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Galaxy and mass assembly (GAMA): the connection between metals, specific SFR and hi gas in galaxies: the Z-SSFR relation
Lara-Lopez, M. A.; Hopkins, A. M.; Lopez-Sanchez, A. R.; Brough, S.; Colless, M.; Bland-Hawthorn, J.; Driver, S.; Foster, C.; Liske, J.; Loveday, J.; Robotham, A. S. G.; Sharp, R. G.; Steele, O.; and Taylor, E. N. 2013 · 2013
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Domain adaptation and sample bias correction theory and algorithm for regression
Cortes, C.; and Mohri, M. 2014 · 2014
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Cosmic Star-Formation History
Madau, P.; and Dickinson, M. 2014 · 2014
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Introducing the Illustris Project: simulating the coevolution of dark and visible matter in the Universe
Vogelsberger, M.; Genel, S.; Springel, V.; Torrey, P.; Sijacki, D.; Xu, D.; Snyder, G.; Nelson, D.; and Hernquist, L. 2014 · 2014
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First results from the IllustrisTNG simulations: the stellar mass content of groups and clusters of galaxies
Pillepich, A.; Nelson, D.; Hernquist, L.; Springel, V.; Pakmor, R.; Torrey, P.; Weinberger, R.; Genel, S.; Naiman, J. P.; Marinacci, F.; and Vogelsberger, M. 2018 · 2018
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Maximum classifier discrepancy for unsupervised domain adaptation
Saito, K.; Watanabe, K.; Ushiku, Y.; and Harada, T. 2018 · 2018
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Adversarial Multiple Source Domain Adaptation
Zhao, H.; Zhang, S.; Wu, G.; Moura, J. M. F.; Costeira, J. P.; and Gordon, G. J. 2018 · 2018
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Adaptation Based on Generalized Discrepancy
Cortes, C.; Mohri, M.; and Medina, A. M. n. 2019 · 2019
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SIMBA: Cosmological simulations with black hole growth and feedback
Davé, R.; Anglés-Alcázar, D.; Narayanan, D.; Li, Q.; Rafieferantsoa, M. H.; and Appleby, S. 2019 · 2019
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The Gas Phase Mass Metallicity Relation for Dwarf Galaxies: Dependence on Star Formation Rate and H I Gas Mass
Jimmy; Tran, K.-V.; Saintonge, A.; Accurso, G.; Brough, S.; and Oliva-Altamirano, P. 2015 · 2015
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Adam: A Method for Stochastic Optimization
Kingma, D. P.; and Ba, J. 2015 · 2015
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The EAGLE simulations of galaxy formation: the importance of the hydrodynamics scheme
Schaller, M.; Dalla Vecchia, C.; Schaye, J.; Bower, R. G.; Theuns, T.; Crain, R. A.; Furlong, M.; and McCarthy, I. G. 2015 · 2015
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Simultaneous Deep Transfer Across Domains and Tasks
Tzeng, E.; Hoffman, J.; Darrell, T.; and Saenko, K. 2015 · 2015
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The EAGLE simulations of galaxy formation: Public release of halo and galaxy catalogues
McAlpine, S.; Helly, J. C.; Schaller, M.; Trayford, J. W.; Qu, Y.; Furlong, M.; Bower, R. G.; Crain, R. A.; Schaye, J.; Theuns, T.; Dalla Vecchia, C.; Frenk, C. S.; McCarthy, I. G.; Jenkins, A.; Rosas-Guevara, Y.; White, S. D. M.; Baes, M.; Camps, P.; and Lemson, G. 2016 · 2016
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Bd-J/Prospector: Initial Release
Johnson, B.; and Leja, J. 2017 · 2017
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Inferring the star formation histories of massive quiescent galaxies with BAGPIPES: evidence for multiple quenching mechanisms
Carnall, A. C.; McLure, R. J.; Dunlop, J. S.; and Davé, R. 2018 · 2018
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Le Guen, V.; and Thome, N. 2019 · 2019
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Semi-Supervised Domain Adaptation via Minimax Entropy
Saito, K.; Kim, D.; Sclaroff, S.; Darrell, T.; and Saenko, K. 2019 · 2019
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Galaxy And Mass Assembly (GAMA): A forensic SED reconstruction of the cosmic star formation history and metallicity evolution by galaxy type
Bellstedt, S.; Robotham, A. S. G.; Driver, S. P.; Thorne, J. E.; Davies, L. J. M.; Lagos, C. d. P.; Stevens, A. R. H.; Taylor, E. N.; Baldry, I. K.; Moffett, A. J.; Hopkins, A. M.; and Phillipps, S. 2020 · 2020
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Unsupervised Multi-source Domain Adaptation for Regression
Richard, G.; de Mathelin, A.; Hébrail, G.; Mougeot, M.; and Vayatis, N. 2020 · 2020
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ProSpect: generating spectral energy distributions with complex star formation and metallicity histories
Robotham, A. S. G.; Bellstedt, S.; Lagos, C. d. P.; Thorne, J. E.; Davies, L. J.; Driver, S. P.; and Bravo, M. 2020 · 2020
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mirkwood: Fast and Accurate SED Modeling Using Machine Learning
Gilda, S.; Lower, S.; and Narayanan, D. 2021 · 2021
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Unsupervised Multi-source Domain Adaptation for Regression , 395–411
Richard, G.; Mathelin, A.; Hébrail, G.; Mougeot, M.; and Vayatis, N. 2021 · 2021
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Domain-adversarial Training of Neural Networks
Ganin, Y.; Ustinova, E.; Ajakan, H.; Germain, P.; Larochelle, H.; Laviolette, F.; Marchand, M.; and Lempitsky, V. 2016 · 2030
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Principal component analysis: a review and recent developments
Jolliffe, I. T.; and Cadima, J. 2016 · 2065
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