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References¶
This page lists the papers that these docs cite.
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. Layer normalization. 2016. arXiv:1607.06450, doi:10.48550/arXiv.1607.06450.
Sven Buder et al. The GALAH+ survey: third data release. MNRAS, 506:150–201, 2021. arXiv:2011.02505, doi:10.1093/mnras/stab1242.
Sven Buder et al. The GALAH survey: data release 4. PASA, 42:e051, 2025. arXiv:2409.19858, doi:10.1017/pasa.2025.26.
Xiang-Qun Cui et al. The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). RAA, 12:1197–1242, 2012. doi:10.1088/1674-4527/12/9/003.
Kyle S. Dawson et al. The Baryon Oscillation Spectroscopic Survey of SDSS-III. AJ, 145:10, 2013. arXiv:1208.0022, doi:10.1088/0004-6256/145/1/10.
Ana E. García Pérez et al. ASPCAP: the APOGEE stellar parameter and chemical abundances pipeline. AJ, 151:144, 2016. arXiv:1510.07635, doi:10.3847/0004-6256/151/6/144.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Identity mappings in deep residual networks. In European Conference on Computer Vision, 630–645. 2016. arXiv:1603.05027, doi:10.1007/978-3-319-46493-0_38.
Dan Hendrycks and Kevin Gimpel. Gaussian error linear units (GELUs). 2016. arXiv:1606.08415, doi:10.48550/arXiv.1606.08415.
Pavel Izmailov et al. Averaging weights leads to wider optima and better generalization. In Conference on Uncertainty in Artificial Intelligence (UAI). 2018. URL: http://auai.org/uai2018/proceedings/papers/313.pdf, arXiv:1803.05407.
Henry W. Leung and Jo Bovy. Deep learning of multi-element abundances from high-resolution spectroscopic data. MNRAS, 483:3255–3277, 2019. arXiv:1808.04428, doi:10.1093/mnras/sty3217.
Chao Liu et al. LAMOST Medium-Resolution Spectroscopic Survey (LAMOST-MRS): scientific goals and survey plan. 2020. arXiv:2005.07210, doi:10.48550/arXiv.2005.07210.
Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In International Conference on Learning Representations. 2019. URL: https://openreview.net/forum?id=Bkg6RiCqY7, arXiv:1711.05101.
Steven R. Majewski et al. The Apache Point Observatory Galactic Evolution Experiment (APOGEE). AJ, 154:94, 2017. arXiv:1509.05420, doi:10.3847/1538-3881/aa784d.
M. Ness et al. The Cannon: a data-driven approach to stellar label determination. ApJ, 808:16, 2015. arXiv:1501.07604, doi:10.1088/0004-637X/808/1/16.
Maximilian Seitzer et al. On the pitfalls of heteroscedastic uncertainty estimation with probabilistic neural networks. In International Conference on Learning Representations. 2022. URL: https://openreview.net/forum?id=aPOpXlnV1T, arXiv:2203.09168.
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. Deep inside convolutional networks: visualising image classification models and saliency maps. In Workshop Proceedings of the International Conference on Learning Representations (ICLR). 2014. URL: https://iclr.cc/archive/2014/workshop-proceedings/, arXiv:1312.6034.
Stephen A. Smee et al. The multi-object, fiber-fed spectrographs for the Sloan Digital Sky Survey and the Baryon Oscillation Spectroscopic Survey. AJ, 146:32, 2013. arXiv:1208.2233, doi:10.1088/0004-6256/146/2/32.
Leslie N. Smith and Nicholay Topin. Super-convergence: very fast training of neural networks using large learning rates. In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, volume 11006 of Proceedings of the SPIE, 1100612. 2019. arXiv:1708.07120, doi:10.1117/12.2520589.
Yuan-Sen Ting et al. The Payne: self-consistent ab initio fitting of stellar spectra. ApJ, 879:69, 2019. arXiv:1804.01530, doi:10.3847/1538-4357/ab2331.
Matthew D. Zeiler and Rob Fergus. Visualizing and understanding convolutional networks. In Computer Vision – ECCV 2014, volume 8689 of Lecture Notes in Computer Science, 818–833. 2014. arXiv:1311.2901, doi:10.1007/978-3-319-10590-1_53.
Gang Zhao et al. LAMOST spectral survey — An overview. RAA, 12:723–734, 2012. arXiv:1206.3569, doi:10.1088/1674-4527/12/7/002.
Abdurro'uf et al. The seventeenth data release of the Sloan Digital Sky Surveys: complete release of MaNGA, MaStar, and APOGEE-2 data. ApJS, 259:35, 2022. arXiv:2112.02026, doi:10.3847/1538-4365/ac4414.
DESI Collaboration et al. Overview of the instrumentation for the Dark Energy Spectroscopic Instrument. AJ, 164:207, 2022. arXiv:2205.10939, doi:10.3847/1538-3881/ac882b.
DESI Collaboration et al. Data Release 1 of the Dark Energy Spectroscopic Instrument. AJ, 171:285, 2026. arXiv:2503.14745, doi:10.3847/1538-3881/ae4c43.
Gaia Collaboration and A. Vallenari et al. Gaia Data Release 3. Summary of the content and survey properties. A&A, 674:A1, 2023. arXiv:2208.00211, doi:10.1051/0004-6361/202243940.