| An application of deep learning in the analysis of stellar spectra S Fabbro, KA Venn, T O'Briain, S Bialek, CL Kielty, F Jahandar, S Monty Monthly Notices of the Royal Astronomical Society 475 (3), 2978-2993, 2018 | 114 | 2018 |
| The Pristine survey–X. A large population of low-metallicity stars permeates the Galactic disc F Sestito, NF Martin, E Starkenburg, A Arentsen, RA Ibata, N Longeard, ... Monthly Notices of the Royal Astronomical Society: Letters 497 (1), L7-L12, 2020 | 80 | 2020 |
| The Pristine survey–XII. Gemini-GRACES chemo-dynamical study of newly discovered extremely metal-poor stars in the Galaxy CL Kielty, KA Venn, F Sestito, E Starkenburg, NF Martin, DS Aguado, ... Monthly Notices of the Royal Astronomical Society 506 (1), 1438-1461, 2021 | 36 | 2021 |
| Cycle-starnet: Bridging the gap between theory and data by leveraging large data sets T O’Briain, YS Ting, S Fabbro, MY Kwang, K Venn, S Bialek The Astrophysical Journal 906 (2), 130, 2021 | 27 | 2021 |
| Assessing the performance of LTE and NLTE synthetic stellar spectra in a machine learning framework S Bialek, S Fabbro, KA Venn, N Kumar, T O’Briain, KM Yi Monthly Notices of the Royal Astronomical Society 498 (3), 3817-3834, 2020 | 21 | 2020 |
| A 3D printed modular phantom for quality assurance of image‐guided small animal irradiators: Design, imaging experiments, and Monte Carlo simulations DY Breitkreutz, S Bialek, B Vojnovic, A Kavanagh, CD Johnstone, ... Medical Physics 46 (5), 2015-2024, 2019 | 9 | 2019 |
| Starnet: A deep learning analysis of infrared stellar spectra CL Kielty, S Bialek, S Fabbro, KA Venn, T O'Briain, F Jahandar, S Monty Software and cyberinfrastructure for astronomy v 10707, 814-824, 2018 | 4 | 2018 |
| StarUnLink: identifying and mitigating signals from communication satellites in stellar spectral surveys S Bialek, S Lucatello, S Fabbro, KM Yi, KA Venn Monthly Notices of the Royal Astronomical Society 524 (1), 529-541, 2023 | 2 | 2023 |
| Interpreting Stellar Spectra with Unsupervised Domain Adaptation T O'Briain, YS Ting, S Fabbro, KM Yi, K Venn, S Bialek arXiv preprint arXiv:2007.03112, 2020 | 2 | 2020 |
| Skyward AI: Advancing Astronomy with Intelligent Machines S Bialek | 1 | 2023 |
| Deep learning analyses of synthetic spectral libraries with an application to the Gaia-ESO database S Bialek | 1 | 2019 |
| StarNet: An application of deep learning in the analysis of stellar spectra C Kielty, S Bialek, S Fabbro, K Venn, T O'Briain, F Jahandar, S Monty American Astronomical Society Meeting Abstracts# 232 232, 223.09, 2018 | 1 | 2018 |
| An Application of Deep Neural Networks in the Analysis of Stellar Spectra S Fabbro, K Venn, T O'Briain, S Bialek, C Kielty, F Jahandar, S Monty arXiv preprint arXiv:1709.09182, 2017 | 1 | 2017 |
| DanceCam: atmospheric turbulence mitigation in wide-field astronomical images with short-exposure video streams S Bialek, E Bertin, S Fabbro, H Bouy, JP Rivet, O Lai, JC Cuillandre Monthly Notices of the Royal Astronomical Society 531 (1), 403-421, 2024 | | 2024 |
| VizieR Online Data Catalog: Galactic disc Pristine low-metallicity stars (Sestito+, 2020) F Sestito, NF Martin, E Starkenburg, A Arentsen, RA Ibata, N Longeard, ... VizieR Online Data Catalog, J/MNRAS/497/L7, 2023 | | 2023 |
| Stellar Parameters with Deep Learning S Fabbro, K Venn, T O'Briain, S Bialek, C Kielty, F Jahandar, S Monty Astronomical Data Analysis Software and Systems XXVII 522, 393, 2020 | | 2020 |
| Interpreting Stellar Spectra with Unsupervised Domain Adaptation YS Ting, S Fabbro, KM Yi, K Venn, S Bialek ML Interpretability for Scientific Discovery Workshop: International …, 2020 | | 2020 |
| 3D Printed Phantom for MicroCT Imaging QA S Bialek, B Vojanovic, A Kavanagh, C Johnstone, T Kanesalingam, ... MEDICAL PHYSICS 45 (6), E421-E421, 2018 | | 2018 |
Kwang Moo YiAssistant Professor of Computer Science at the University of British Columbia, Haiper AI在 cs.ubc.ca 的电子邮件经过验证
Kwang Moo YiAssistant Professor of Computer Science at the University of British Columbia, Haiper AI在 cs.ubc.ca 的电子邮件经过验证