Fast automated analysis of strong gravitational lenses with convolutional neural networks

Finding strong lenses in CFHTLS using convolutional neural networks

(2017) C. Jacobs et al.   MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY

Galaxy Zoo: morphological classifications for 120 000 galaxies inHSTlegacy imaging

(2016) Kyle W. Willett et al.   MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY

Unreasonable effectiveness of learning neural networks: From accessible states and robust ensembles to basic algorithmic schemes

(2016) Carlo Baldassi et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

ImageNet Large Scale Visual Recognition Challenge

(2015) Olga Russakovsky et al.   INTERNATIONAL JOURNAL OF COMPUTER VISION

THE THIRD GRAVITATIONAL LENSING ACCURACY TESTING (GREAT3) CHALLENGE HANDBOOK

(2014) Rachel Mandelbaum et al.   ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES

THE SL2S GALAXY-SCALE LENS SAMPLE. III. LENS MODELS, SURFACE PHOTOMETRY, AND STELLAR MASSES FOR THE FINAL SAMPLE

(2013) Alessandro Sonnenfeld et al.   ASTROPHYSICAL JOURNAL

A systematic review of strong gravitational lens modeling software

(2013) Alan T. Lefor et al.   NEW ASTRONOMY REVIEWS

The Sloan Lens ACS Survey. V. The Full ACS Strong‐Lens Sample

(2008) Adam S. Bolton et al.   ASTROPHYSICAL JOURNAL