Contributions > By Session


Unifying large-scale spectroscopy, astrometry, and photometry with Convolutional Neural-Networks. Guillaume Guiglion sciencesconf.org:ml-iap2021:362163
Rediscovering Newton's gravity and Solar System properties using deep learning and inductive biases Pablo Lemos, Niall Jeffrey, Miles Cranmer, Shirley Ho, Peter Battaglia sciencesconf.org:ml-iap2021:365634
Gaussian Process Regression: An Application to Radio Cosmology Paula Soares sciencesconf.org:ml-iap2021:365641
Unknown Unknowns: Hybrid machine learning and template based photometric redshifts Peter Hatfield sciencesconf.org:ml-iap2021:367577
Pix2Prof: fast extraction of sequential information from galaxy imagery via a deep natural language 'captioning' model Michael Smith, Nikhil Arora, Connor Stone, Stéphane Courteau, James Geach sciencesconf.org:ml-iap2021:367614
Forecasting the power of Higher Order Weak Lensing Statistics with automatically differentiable simulations Denise lanzieri, François Lanusse, Chirag Modi sciencesconf.org:ml-iap2021:367889
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
Tidal stream detection in HSC-SSP with Deep Learning Helena Domínguez Sánchez sciencesconf.org:ml-iap2021:367994
Accelerating the modeling of HI on cosmological scales via Deep Learning Robert Feldmann, Mauro Bernardini, Joachim Stadel, Lucio Mayer, James Bullock, Daniel Anglés-Alcázar, Michael Boylan-Kolchin sciencesconf.org:ml-iap2021:368090
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
Cosmological constraints from galaxy-galaxy lensing and galaxy clustering with HSC-Y1 and BOSS data: the first application of emulator-based halo model to cosmology analysis Hironao Miyatake, Sunao Sugiyama, Masahiro Takada, Takahiro Nishimichi, Yosuke Kobayashi, Surhud More, Naoki Yoshida sciencesconf.org:ml-iap2021:368192
Machine learning for experimental design: stress-testing redshift uncertainty quantification and propagation with Redshift Assessment Infrastructure Layers (RAIL) Alex Malz sciencesconf.org:ml-iap2021:368273
Beyond the Hubble Sequence - Exploring galaxy morphology with unsupervised machine learning Ting-Yun Cheng, Marc Huertas-Company, Christopher Conselice, Alfonso Aragon-Salamanca, Brant Robertson, Nesar Ramachandra sciencesconf.org:ml-iap2021:368288
Photometric redshift calibration with simulated annealing and self-organising maps Benjamin Stölzner sciencesconf.org:ml-iap2021:368290
Detection of the Damped Lyman-alpha systems in quasar spectra with machine learning algorithms ting tan, Yuankang Liu, Christophe Balland sciencesconf.org:ml-iap2021:368304
Semi-Supervised Learning for Probabilistic Photometric Redshifts Rory Coles, Sébastien Fabbro, Christopher Murray, Kwang Moo Yi sciencesconf.org:ml-iap2021:368312
Galaxy Cluster Mass Estimation Using Deep Learning Matthew Ho, Michelle Ntampaka, Markus Michael Rau, Arya Farahi, Hy Trac, Barnabas Poczos sciencesconf.org:ml-iap2021:368314
Manifold learning to explore the galaxy parameter space Iary Davidzon, Keerthana Jegatheesan, Olivier Ilbert, Clotilde Laigle sciencesconf.org:ml-iap2021:368317
Time Delay Estimation in Unresolved Lensed Quasars via Convolutioanl Neural Networks Luca Biggio, Silvano Tosi, Alba Domi, Luca Paganin, Georgios Vernardos, Davide Ricci sciencesconf.org:ml-iap2021:368319
Bayesian Inference Acceleration: a Deep Learning Approach Marco Bonici sciencesconf.org:ml-iap2021:368320
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
Applying Deep Neural Network to dark matter halo catalogues to constrain the dark energy equation-of-state parameters Farida Farsian, Lauro Moscardini, Federico Marulli, Carlo Giocoli sciencesconf.org:ml-iap2021:368405
Emulating the void density profile and the void-size function Giorgia Pollina sciencesconf.org:ml-iap2021:368424

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