Accès aux contributions > Par Session
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Unifying large-scale spectroscopy, astrometry, and photometry with Convolutional Neural-Networks.
Guillaume Guiglion
sciencesconf.org:ml-iap2021:362163 |
Bringing Machine Learning to the Analysis of IFU Datacubes
Carter Rhea
sciencesconf.org:ml-iap2021:366457 |
Bayes vs. machine learning in large-scale survey demographics with eROSITA
Johannes Buchner
sciencesconf.org:ml-iap2021:367804 |
The miniJPAS survey: emission lines properties and SFR in theAEGIS field for galaxies withz<0.35
Ginés Martínez Solaeche
sciencesconf.org:ml-iap2021:367849 |
Applying neural networks for the chemical analysis of star forming regions.
Vital Fernández
sciencesconf.org:ml-iap2021:367861 |
On the potential of machine learning in the classification of ultracool dwarfs with Euclid
Eduardo Martin
sciencesconf.org:ml-iap2021:367912 |
A Deep Learning Neural Network for Voigt Profile Fitting Quasar Absorption Lines
Bryson Stemock, Christopher Churchill, Caitlin Doughty, Rogelio Ochoa, Sultan Hassan
sciencesconf.org:ml-iap2021:367958 |
STag: Supernova Tagging and Classification
William Davison, David Parkinson, Brad E. Tucker
sciencesconf.org:ml-iap2021:367977 |
What drives the scatter in the BPT diagrams? A Machine Learning based analysis
Mirko Curti
sciencesconf.org:ml-iap2021:367995 |
AstronomicAL: an interactive dashboard for visualisation, integration and classification of data with Active Learning
Grant Stevens, Sotiria Fotopoulou, Malcolm Bremer, Oliver Ray
sciencesconf.org:ml-iap2021:368106 |
‘Deep' vs. 'Shallow' Learning in Cosmological Surveys
Ofer Lahav
sciencesconf.org:ml-iap2021:368114 |
A deep transfer learning approach to photospheric parameters of CARMENES target stars
Antonio Bello-Garcia, Ordieres-Mere Joaquín, Vera Maria Passegger, Jose Antonio Caballero, Andreas Schweitzer, Ana Gonzalez-Marcos
sciencesconf.org:ml-iap2021:368131 |
Machine learning: lessons learnt with the QUBRICS survey
Francesco Guarneri, Giorgio Calderone, Stefano Cristiani, Andrea Grazian, Konstantina Boutsia, Guido Cupani, Fabio Fontanot, Valentina D'Odorico
sciencesconf.org:ml-iap2021:368260 |
Predicting Physical Properties of Lyman Alpha Forest with Deep Learning
Ting-Yun Cheng, Ryan Cooke
sciencesconf.org:ml-iap2021:368298 |
Mapping the stellar photometric metallicity of the Magellanic Clouds with machine learning
Amy Miller, Guillaume Guiglion, Maria-Rosa L. Cioni, David Nidever, Richard de Grijs
sciencesconf.org:ml-iap2021:368318 |
Bridging the gap between synthetic and real data through unsupervised domain adaptation
Spencer Bialek, Sébastien Fabbro, Kim Venn, Yuan-Sen Ting, Guillaume Payeur, Teaghan O'Briain
sciencesconf.org:ml-iap2021:368332 |
Neural spectrum encoding
Peter Melchior
sciencesconf.org:ml-iap2021:368337 |
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