Accès aux contributions > Par Session


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
Stratified Learning: A general-purpose method for learning under covariate shift with applications to observational cosmology Roberto Trotta, Maximilian Autenrieth, David van Dyk, David Stenning sciencesconf.org:ml-iap2021:366014
Bridging the gap between simulations and survey data - domain adaptation for deep learning in astronomy Aleksandra Ciprijanovic, Diana Kafkes, Kathryn Downey, Sydney Jenkins, Gabriel Nathan Perdue, Sandeep Madireddy, Gregory Snyder, Brian Nord, Travis Johnston sciencesconf.org:ml-iap2021:366128
Automated methods to find high-redshift quasars Lena Lenz, Daniel Mortlock, Boris Leistedt, Stephen Warren, Rhys Barnett sciencesconf.org:ml-iap2021:366348
Considerations for optimizing photometric classification of supernovae from the Rubin Observatory Catarina S. Alves, Hiranya V. Peiris, Michelle Lochner, Jason D. McEwen, Tarek Allam Jr, Rahul Biswas sciencesconf.org:ml-iap2021:366394
Real-time science-driven follow-up of survey transients: Optimally augmenting ZTF SN Ia light curves for maximizing SALT2 parameter constraints Niharika Sravan sciencesconf.org:ml-iap2021:367435
Using machine learning methods in interstellar medium studies Christophe Morisset sciencesconf.org:ml-iap2021:367553
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
Inferring the assembly and merger histories of galaxies with the IllustrisTNG simulations and machine learning Lukas Eisert, Annalisa Pillepich, Dylan Nelson, Ralf Klessen sciencesconf.org:ml-iap2021:367649
Search for galaxy-scale strong lenses in DES and CFIS Frédéric Courbin sciencesconf.org:ml-iap2021:367683
Using Machine Learning to Identify Periodicity. Niall Miller, Phil Lucas, Yi Sun sciencesconf.org:ml-iap2021:367797
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
Multifrequency Point Source detection with Fully-Convolutional Networks: Performance in realistic microwave sky simulations José Manuel Casas, Joaquín González-Nuevo, Laura Bonavera sciencesconf.org:ml-iap2021:367860
Applying neural networks for the chemical analysis of star forming regions. Vital Fernández sciencesconf.org:ml-iap2021:367861
Machine Learning Calibration of Cosmic Shear Redshift Distributions Angus Wright sciencesconf.org:ml-iap2021:367923
Investigations for LSST with Machine Learning: Strong Lens Mass Modeling and Photometric Redshift estimation Stefan Schuldt, Sherry Suyu, Raoul Canameras, Yiping Shu, Stefan Taubenberger sciencesconf.org:ml-iap2021:367956
Machine Learning for Noise Correction and Classification Strategies for Astronomical Sources Nina Hernitschek, Keivan Stassun sciencesconf.org:ml-iap2021:367957
Single frequency CMB polarized foreground marginalization with a single training image Niall Jeffrey sciencesconf.org:ml-iap2021:367971
What drives the scatter in the BPT diagrams? A Machine Learning based analysis Mirko Curti sciencesconf.org:ml-iap2021:367995
Reconstructing blended galaxies with Machine Learning Lavanya Nemani, Emiliano Merlin, Adriano Fontana sciencesconf.org:ml-iap2021:368006
Photometric classification of compact galaxies, stars and quasars using multiple neural networks Siddharth Chaini, Atharva Bagul, Anish Deshpande, Rishi Gondkar, M Vivek, Kaushal Sharma, Ajit Kembhavi sciencesconf.org:ml-iap2021:368024
hyphy: Mapping Dark Matter to Hydrodynamics with Posterior Inference Benjamin A Horowitz sciencesconf.org:ml-iap2021:368033
Dark Substructure Sensitivity in the Euclid Survey with Machine Learning Conor O'Riordan sciencesconf.org:ml-iap2021:368064
Use data not models -- Lensing of '69 as an example for data-driven inference Jenny Wagner sciencesconf.org:ml-iap2021:368087
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
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
Machine-learning for real, messy data Sotiria Fotopoulou sciencesconf.org:ml-iap2021:368108
Parameter Estimation with Physics Informed Neural Networks Alex Lague, J. Richard Bond, Renee Hlozek sciencesconf.org:ml-iap2021:368111
‘Deep' vs. 'Shallow' Learning in Cosmological Surveys Ofer Lahav sciencesconf.org:ml-iap2021:368114
The scattering transform in cosmology, or, a CNN without Training Sihao Cheng, Brice Ménard, Yuan-Sen Ting, Joan Bruna sciencesconf.org:ml-iap2021:368116
A neural network for simultaneous classification and redshift estimate of galaxies José A. de Diego sciencesconf.org:ml-iap2021:368126
Source detection through semantic segmentation with convolutional neural networks Maxime Paillassa, Emmanuel Bertin, Herve Bouy sciencesconf.org:ml-iap2021:368135
Deep Neural Networks for detection of sources in radio astronomical maps: main challenges and current achievements. Renato Sortino, Daniel Magro, Carmelo Pino, Eva Sciacca, Simone Riggi, Giuseppe Fiameni sciencesconf.org:ml-iap2021:368150
Quantifying uncertainty in deep-learning classification of radio galaxies Fiona Porter, Anna Scaife sciencesconf.org:ml-iap2021:368195
Inference and probabilistic modelling with machine learning for LISA data analysis Natalia Korsakova sciencesconf.org:ml-iap2021:368198
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
Cosmological Forward Modeling with Adjoint Method Yin Li sciencesconf.org:ml-iap2021:368265
MINERVA team: Winning the SKA Science Data Challenge 2 with fast dedicated CNN architectures David Cornu, Benoit Semelin, Stephane Aicardi, Xuezhou Lu, Philippe Salomè, Antoine Marchal, Jonathan Freundlich, Françoise Combes sciencesconf.org:ml-iap2021:368267
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
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
CosmicRIM : Reconstructing Early Universe by Combining Differentiable Simulations with Recurrent Inference Machines Chirag Modi, François Lanusse, Uros Seljak, David Spergel, Laurence Perreault-Levasseur sciencesconf.org:ml-iap2021:368289
Photometric redshift calibration with simulated annealing and self-organising maps Benjamin Stölzner sciencesconf.org:ml-iap2021:368290
Self-supervised learning for sky surveys George Stein sciencesconf.org:ml-iap2021:368294
Gaia variable star classification: evolution and lessons learned Berry Holl, Lorenzo Rimoldini, Krzysztof Nienartowicz, Marc Audard, Panaiotis Gavras, Laurent Eyer, Nami Mowlavi, Gregory Jeverdat-de-Fombelle, Isabelle Lecoeur-Taibi sciencesconf.org:ml-iap2021:368302
Semi-Supervised Learning for Probabilistic Photometric Redshifts Rory Coles, Sébastien Fabbro, Christopher Murray, Kwang Moo Yi sciencesconf.org:ml-iap2021:368312
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
From machine learning to human understanding Daniel Masters sciencesconf.org:ml-iap2021:368330
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
Letting the Universe Speak for Itself Nima Sedaghat, Martino Romaniello, Felix Stoehr sciencesconf.org:ml-iap2021:368445
CosmoPower: deep learning emulation of cosmological power spectra for accelerated Bayesian inference from next-generation surveys Alessio Spurio Mancini sciencesconf.org:ml-iap2021:368826
Simulation and Segmentation with Deep Learning for Euclid Hubert Bretonnière sciencesconf.org:ml-iap2021:369290
Review on generative models in Machine Learning Gilles Louppe sciencesconf.org:ml-iap2021:383244

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