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Machine learning within the THREEHUNDERD simulation project Daniel de Andres, Gustavo Yepes, Weiguang Cui, Marco De Petris, Florian Ruppin sciencesconf.org:ml-iap2021:365878
Unknown Unknowns: Hybrid machine learning and template based photometric redshifts Peter Hatfield sciencesconf.org:ml-iap2021:367577
Search for galaxy-scale strong lenses in DES and CFIS Frédéric Courbin sciencesconf.org:ml-iap2021:367683
A Method to Distinguish Quiescent and Dusty Star-forming Galaxies with t-SNE John Weaver, Charles Steinhardt, Jack Maxfield sciencesconf.org:ml-iap2021:367795
The miniJPAS survey: emission lines properties and SFR in theAEGIS field for galaxies withz<0.35 Ginés Martínez Solaeche sciencesconf.org:ml-iap2021:367849
Pushing automated morphological classifications to their limits with the Dark Energy Survey Jesús Vega Ferrero sciencesconf.org:ml-iap2021:367991
Tidal stream detection in HSC-SSP with Deep Learning Helena Domínguez Sánchez sciencesconf.org:ml-iap2021:367994
Identifying strong gravitational lenses in current and future large-scale imaging surveys Raoul Canameras sciencesconf.org:ml-iap2021:368092
Synthetic Data from Generative Models for Galaxies Benjamin Holzschuh sciencesconf.org:ml-iap2021:368098
‘Deep' vs. 'Shallow' Learning in Cosmological Surveys Ofer Lahav sciencesconf.org:ml-iap2021:368114
Using convolutional neural networks to identify strong lenses in Euclid and J-PAS Alberto Manjon García, Jose María Diego Rodriguez, Diego Herranz Muñoz, Helena Domínguez Sánchez, Jesús Vega Ferrero sciencesconf.org:ml-iap2021:368120
Source detection through semantic segmentation with convolutional neural networks Maxime Paillassa, Emmanuel Bertin, Herve Bouy sciencesconf.org:ml-iap2021:368135
Learning from 3D tomographic 21cm maps Caroline Heneka sciencesconf.org:ml-iap2021:368244
Superresolving Herschel imaging: a proof of concept using Deep Neural Networks Lynge Lauritsen, Hugh Dickinson, Jane Bromley, Stephen Serjeant, Chen-Fatt Lim, Zhen-Kai Gao, Wei-Hao Wang sciencesconf.org:ml-iap2021:368272
Self-supervised learning for sky surveys George Stein sciencesconf.org:ml-iap2021:368294
From machine learning to human understanding Daniel Masters sciencesconf.org:ml-iap2021:368330
Studying Morphology & Quenching of Galaxies in the All Sky-Era using Interpretable Bayesian Convolutional Neural Networks Aritra Ghosh, C. M. Urry sciencesconf.org:ml-iap2021:368331
Simulation and Segmentation with Deep Learning for Euclid Hubert Bretonnière sciencesconf.org:ml-iap2021:369290
Imaging Surveys and ML Elisabeth Krause sciencesconf.org:ml-iap2021:380963

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