| The quijote simulations F Villaescusa-Navarro, CH Hahn, E Massara, A Banerjee, AM Delgado, ... The Astrophysical Journal Supplement Series 250 (1), 2, 2020 | 194 | 2020 |
| The camels project: Cosmology and astrophysics with machine-learning simulations F Villaescusa-Navarro, D Anglés-Alcázar, S Genel, DN Spergel, ... The Astrophysical Journal 915 (1), 71, 2021 | 120 | 2021 |
| A meta-learning approach to one-step active learning G Contardo, L Denoyer, T Artières AutoML Workshop @ ECML-PKDD, 2017 | 33 | 2017 |
| Evolution of the Exoplanet Size Distribution: Forming Large Super-Earths Over Billions of Years TJ David, G Contardo, A Sandoval, R Angus, YL Lu, M Bedell, JL Curtis, ... The Astronomical Journal 161 (6), 265, 2021 | 32 | 2021 |
| The camels multifield data set: Learning the universe’s fundamental parameters with artificial intelligence F Villaescusa-Navarro, S Genel, D Angles-Alcazar, L Thiele, R Dave, ... The Astrophysical Journal Supplement Series 259 (2), 61, 2022 | 31 | 2022 |
| Recurrent neural networks for adaptive feature acquisition G Contardo, L Denoyer, T Artières Neural Information Processing: 23rd International Conference, ICONIP 2016 …, 2016 | 28 | 2016 |
| Gravitational-wave population inference with deep flow-based generative network KWK Wong, G Contardo, S Ho Physical Review D 101 (12), 123005, 2020 | 27 | 2020 |
| A deep-learning approach for live anomaly detection of extragalactic transients VA Villar, M Cranmer, E Berger, G Contardo, S Ho, G Hosseinzadeh, ... The Astrophysical Journal Supplement Series 255 (2), 24, 2021 | 26 | 2021 |
| From Dark Matter to Galaxies with Convolutional Neural Networks JHT Yip, X Zhang, Y Wang, W Zhang, Y Sun, G Contardo, ... Machine Learning and the Physical Sciences @ NeurIPS, 2019 | 26* | 2019 |
| Sequential cost-sensitive feature acquisition G Contardo, L Denoyer, T Artières Advances in Intelligent Data Analysis XV: 15th International Symposium, IDA …, 2016 | 21 | 2016 |
| The Influence of Age on the Relative Frequency of Super-Earths and Sub-Neptunes A Sandoval, G Contardo, TJ David The Astrophysical Journal 911 (2), 117, 2021 | 18 | 2021 |
| Further Evidence of Modified Spin-down in Sun-like Stars: Pileups in the Temperature–Period Distribution TJ David, R Angus, JL Curtis, JL van Saders, IL Colman, G Contardo, ... The Astrophysical Journal 933 (1), 114, 2022 | 12 | 2022 |
| Representation learning for cold-start recommendation G Contardo, L Denoyer, T Artieres ICLR Workshop 2015, 2014 | 12 | 2014 |
| Finding universal relations in subhalo properties with artificial intelligence H Shao, F Villaescusa-Navarro, S Genel, DN Spergel, D Anglés-Alcázar, ... The Astrophysical Journal 927 (1), 85, 2022 | 11 | 2022 |
| Learning states representations in pomdp G Contardo, L Denoyer, T Artieres, P Gallinari ICLR workshop 2014, 2013 | 9 | 2013 |
| Dalek: A Deep Learning Emulator for TARDIS WE Kerzendorf, C Vogl, J Buchner, G Contardo, M Williamson, ... The Astrophysical Journal Letters 910 (2), L23, 2021 | 8 | 2021 |
| Learning Embeddings for Completion and Prediction of Relationnal Multivariate Time-Series. A Ziat, G Contardo, N Baskiotis, L Denoyer ESANN, 2016 | 7 | 2016 |
| Anomaly Detection for Multivariate Time Series of Exotic Supernovae VA Villar, M Cranmer, G Contardo, S Ho, JYY Lin Machine Learning and the Physical Sciences Workshop at NeurIPS., 2020 | 4 | 2020 |
| Machine learning under budget constraints G Contardo Université Pierre et Marie Curie-Paris VI, 2017 | 3 | 2017 |
| Meta-Learning for One-Class Classification with Few Examples using Order-Equivariant Network: Application in the Milky Way A Oladosu, T Xu, P Ekfeldt, BA Kelly, M Cranmer, S Ho, AM Price-Whelan, ... arXiv preprint arXiv:2007.04459, 2020 | 1* | 2020 |
