• Parameterizing Stellar Spectra Using Deep Neural Networks
• Classifying Radio Galaxies with Convolutional Neural Network
• Photometric redshift estimation via deep learning: Generalized and pre-classification-less, image based, fully probabilistic redshifts
• Deep Galaxy: Classification of Galaxies based on Deep Convolutional Neural Networks
• An Application of Deep Learning in the Analysis of Stellar Spectra
• Estimating Photometric Redshifts for X-ray sources in the X-ATLAS field, using machine-learning techniques
• Effective Image Differencing with ConvNets for Real-time Transient Hunting
• Classifying Complex Faraday Spectra with Convolutional Neural Networks
• Assessing the Performance of a Machine Learning Algorithm in Identifying Bubbles in Dust Emission
• Improving galaxy morphologies for SDSS with Deep Learning
• Glitch Classification and Clustering for LIGO with Deep Transfer Learning
• Single-epoch supernova classification with deep convolutional neural networks
• A Method Of Detecting Gravitational Wave Based On Time-frequency Analysis And Convolutional Neural Networks
• Towards understanding feedback from supermassive black holes using convolutional neural networks
• Supervised detection of exoplanets in high-contrast imaging sequences
• Painting galaxies into dark matter halos using machine learning
• Matching matched filtering with deep networks for gravitational-wave astronomy
• Improving science yield for NASA Swift with automated planning technologies
• Radio Galaxy Zoo: Compact and extended radio source classification with deep learning
• Reionization Models Classifier using 21cm Map Deep Learning
• Fast Cosmic Web Simulations with Generative Adversarial Networks
• Automatic physical inference with information maximising neural networks
• Deep Learning Classification in Asteroseismology Using an Improved Neural Network: Results on 15000 Kepler Red Giants and Applications to K2 and TESS Data
• Integrating human and machine intelligence in galaxy morphology classification tasks
• Lunar Crater Identification via Deep Learning
• Applying Deep Learning to Fast Radio Burst Classification
• Predicting the Neutral Hydrogen Content of Galaxies From Optical Data Using Machine Learning
• Classification of simulated radio signals using Wide Residual Networks for use in the search for extra-terrestrial intelligence
• A high-bias, low-variance introduction to Machine Learning for physicists
• Dissecting stellar chemical abundance space with t-SNE
• Image-based deep learning for classification of noise transients in gravitational wave detectors
• Return of the features: Efficient feature selection and interpretation for photometric redshifts
• GAME: GAlaxy Machine learning for Emission lines
• Application of Deep Learning methods to analysis of Imaging Atmospheric Cherenkov Telescopes data
• Detecting Solar-like Oscillations in Red Giants with Deep Learning
• MADE: A spectroscopic Mass, Age, and Distance Estimator for red giant stars with Bayesian machine learning
• Deep learning from 21-cm tomography of the Cosmic Dawn and Reionization
• A volumetric deep Convolutional Neural Network for simulation of mock dark matter halo catalogues
• Machine-learning identification of extragalactic objects in the optical-infrared all-sky surveys
• Habitability Classification of Exoplanets: A Machine Learning Insight
• Using transfer learning to detect galaxy mergers
• Radio Galaxy Zoo: ClaRAN | a deep learning classifier for radio morphologies
• SBAF: A New Activation Function for Artificial Neural Net based Habitability Classification
• ExoGAN: Retrieving Exoplanetary Atmospheres Using Deep Convolutional Generative Adversarial Networks
• Supervised Machine Learning for Analysing Spectra of Exoplanetary Atmospheres
• Photometric redshifts from SDSS images using a Convolutional Neural Network
• A Machine-learning Method for Identifying Multiwavelength Counterparts of Submillimeter Galaxies: Training and Testing Using AS2UDS and ALESS
• Real-time multiframe blind deconvolution of solar images
• Resolution and accuracy of non-linear regression of PSF with artificial neural networks
• Transfer learning for galaxy morphology from one survey to another
• Weak-lensing shear measurement with machine learning: Teaching artificial neural networks about feature noise
• Identifying Reionization Sources from 21cm Maps using Convolutional Neural Networks
• Complex Fully Convolutional Neural Networks for MR Image Reconstruction
• Deep Learning for Image Sequence Classification of Astronomical Events
• Visualizing the Hidden Features of Galaxy Morphology with Machine Learning
• A novel single-pulse search approach to detection of dispersed radio pulses using clustering and supervised machine learning
• Cosmological constraints from noisy convergence maps through deep learning
• The FIRST Classifier: Compact and Extended Radio Galaxy Classification using Deep Convolutional Neural Networks
• Galaxy Morphology Classification with Deep Convolutional Neural Networks
• Analyzing interferometric observations of strong gravitational lenses with recurrent and convolutional neural networks
• Enhanced Rotational Invariant Convolutional Neural Network for Supernovae Detection
• CosmoFlow: Using Deep Learning to Learn the Universe at Scale
• Analyzing Inverse Problems with Invertible Neural Networks
• Single-pulse classifier for the LOFAR Tied-Array All-sky Survey
• Machine Learning Classification of Gaia Data Release 2
• Searching for Sub-Second Stellar Variability with Wide-Field Star Trails and Deep Learning
• Weak lensing shear estimation beyond the shape-noise limit: a machine learning approach
• Protostellar classification using supervised machine learning algorithms
• Detecting Radio Frequency Interference in radio-antenna arrays with the Recurrent Neural Network algorithm
• QuasarNET: Human-level spectral classification and redshifting with Deep Neural Networks
• Stellar Cluster Detection using GMM with Deep Variational Autoencoder
• Galaxy detection and identification using deep learning and data augmentation
• Towards online triggering for the radio detection of air showers using deep neural networks
• From FATS to feets: Further improvements to an astronomical feature extraction tool based on machine learning
• Deep Learning Based Detection of Cosmological Diffuse Radio Sources
• Bayesian sparse reconstruction: a brute-force approach to astronomical imaging and machine learning
• Segmentation of coronal holes in solar disk images with a convolutional neural network
• Graph Neural Networks for IceCube Signal Classification
• Galaxy morphology prediction using capsule networks
• TSARDI: a Machine Learning data rejection algorithm for transiting exoplanet light curves
• DeepCMB: Lensing Reconstruction of the Cosmic Microwave Background with Deep Neural Networks
• Scalable Solutions for Automated Single Pulse Identification and Classification in Radio Astronomy
• Multiband galaxy morphologies for CLASH: a convolutional neural network transferred from CANDELS
• Classifying Lensed Gravitational Waves in the Geometrical Optics Limit with Machine Learning
• Deep multi-survey classification of variable stars
• Deblending galaxy superpositions with branched generative adversarial networks
• On the dissection of degenerate cosmologies with machine learning
• Distinguishing standard and modified gravity cosmologies with machine learning
• A hybrid approach to machine learning annotation of large galaxy image databases
• Towards a radially-resolved semi-analytic model for the evolution of disc galaxies tuned with machine learning
• Scientific Domain Knowledge Improves Exoplanet Transit Classification with Deep Learning
• Applying deep neural networks to the detection and space parameter estimation of compact binary coalescence with a network of gravitational wave detectors
• Forging new worlds: high-resolution synthetic galaxies with chained generative adversarial networks
• Deep Learning Applied to the Asteroseismic Modeling of Stars with Coherent Oscillation Modes
• Finding high-redshift strong lenses in DES using convolutional neural networks
• Classification of gravitational-wave glitches via dictionary learning
• Reduced-order modeling with artificial neurons for gravitational-wave inference
• Probabilistic Random Forest: A machine learning algorithm for noisy datasets
• Machine-learning Approaches to Exoplanet Transit Detection and Candidate Validation in Wide-field Ground-based Surveys
• Classification of Multiwavelength Transients with Machine Learning
• Identification of Low Surface Brightness Tidal Features in Galaxies Using Convolutional Neural Networks
• Gamma/Hadron Separation in Imaging Air Cherenkov Telescopes Using Deep Learning Libraries TensorFlow and PyTorch
• Particle identification in ground-based gamma-ray astronomy using convolutional neural networks
• Deep Learning at Scale for the Construction of Galaxy Catalogs in the Dark Energy Survey
• LinKS: Discovering galaxy-scale strong lenses in the Kilo-Degree Survey using Convolutional Neural Networks
• Finding the origin of noise transients in LIGO data with machine learning
• Denoising Weak Lensing Mass Maps with Deep Learning
• Systematic Serendipity: A Test of Unsupervised Machine Learning as a Method for Anomaly Detection
• A Machine Learning Based Morphological Classification of 14,245 Radio AGNs Selected From The Best-Heckman Sample
• Transfer Learning in Astronomy: A New Machine-Learning Paradigm
• Machine Learning on Difference Image Analysis: A comparison of methods for transient detection
• Gravitational Wave Denoising of Binary Black Hole Mergers with Deep Learning
• deepCool: Fast and Accurate Estimation of Cooling Rates in Irradiated Gas with Artificial Neural Networks
• Star formation rates and stellar masses from machine learning
• Accurate Identification of Galaxy Mergers with Imaging
• Solar-Sail Trajectory Design for Multiple Near-Earth Asteroid Exploration Based on Deep Neural Networks
• A machine learning approach for identification and classification of symbiotic stars using 2MASS and WISE
• Classification and Recovery of Radio Signals from Cosmic Ray Induced Air Showers with Deep Learning
• Classification of Broad Absorption Line Quasars with a Convolutional Neural Network
• AutoRegressive Planet Search: Methodology
• Clustering clusters: unsupervised machine learning on globular cluster structural parameters
• Machine and Deep Learning Applied to Galaxy Morphology - A Comparative Study
• Photometric Redshift Analysis using Supervised Learning Algorithms and Deep Learning
• RADYNVERSION: Learning to Invert a Solar Flare Atmosphere with Invertible Neural Networks
• Towards Machine-assisted Meta-Studies: The Hubble Constant
• Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era
• Machine Vision and Deep Learning for Classification of Radio SETI Signals
• Star Formation Rates for photometric samples of galaxies using machine learning methods
• Deep learning detection of transients
• Can a machine learn the outcome of planetary collisions?
• Convolutional neural networks on the HEALPix sphere: a pixel-based algorithm and its application to CMB data analysis
• AGN selection in the AKARI NEP deep field with the fuzzy SVM algorithm
• Galaxy shape measurement with convolutional neural networks
• Optimizing Sparse RFI Prediction using Deep Learning
• Rapid Classification of TESS Planet Candidates with Convolutional Neural Networks
• Constraining the Thermal Properties of Planetary Surfaces using Machine Learning: Application to Airless Bodies
• Simultaneous calibration of spectro-photometric distances and the Gaia DR2 parallax zero-point offset with deep learning
• Separating the EoR signal with a convolutional denoising autoencoder: a deep-learning-based method
• The Role of Machine Learning in the Next Decade of Cosmology
• Identifying Galaxy Mergers in Observations and Simulations with Deep Learning
• Fast likelihood-free cosmology with neural density estimators and active learning
• A Machine Learning Artificial Neural Network Calibration of the Strong-Line Oxygen Abundance
• HexagDLy - Processing hexagonally sampled data with CNNs in PyTorch
• Deterministic and Bayesian Neural Networks for Low-latency Gravitational Wave Parameter Estimation of Binary Black Hole Mergers
• DASH: Deep Learning for the Automated Spectral Classification of Supernovae and their Hosts
• Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders
• Efficient Selection of Quasar Candidates Based on Optical and Infrared Photometric Data Using Machine Learning
• Mapping neutron star data to the equation of state using the deep neural network
• Classifying the unknown: discovering novel gravitational-wave detector glitches using similarity learning
• Random Forest identification of the thin disk, thick disk and halo Gaia-DR2 white dwarf population
• The Hubble Sequence at z∼0 in the IllustrisTNG simulation with deep learning
• Galaxy classification: A machine learning analysis of GAMA catalogue data
• Modeling with the Crowd: Optimizing the Human-Machine Partnership with Zooniverse
• The Galaxy Cluster `Pypeline' for X-ray Temperature Maps: ClusterPyXT
• Investigating the dark matter signal in the cosmic ray antiproton flux with the machine learning method
• Learning the Relationship between Galaxies Spectra and their Star Formation Histories using Convolutional Neural Networks and Cosmological Simulations
• Identifying Exoplanets with Deep Learning II: Two New Super-Earths Uncovered by a Neural Network in K2 Data
• Machine learning and the physical sciences
• Automatic detection of Interplanetary Coronal Mass Ejections from in-situ data: a deep learning approach
• Accelerated Bayesian inference using deep learning
• Transfer learning for radio galaxy classification
• Painting with baryons: augmenting N-body simulations with gas using deep generative models
• Baryon density extraction and isotropy analysis of Cosmic Microwave Background using Deep Learning
• RAPID: Early Classification of Explosive Transients using Deep Learning
• Stokes inversion based on convolutional neural networks
• Shaping Asteroid Models Using Genetic Evolution (SAGE)
• TiK-means: Transformation-infused K-means clustering for skewed groups
• Generative deep fields: arbitrarily sized, random synthetic astronomical images through deep learning
• Identifying MgII Narrow Absorption Lines with Deep Learning
• Application of Machine Learning to the Particle Identification of GAPS
• Detecting Exoplanet Transits through Machine Learning Techniques with Convolutional Neural Networks
• Optical Transient Object Classification in Wide Field Small Aperture Telescopes with Neural Networks
• Identification of RR Lyrae stars in multiband, sparsely-sampled data from the Dark Energy Survey using template fitting and Random Forest classification
• Do Androids Dream of Magnetic Fields? Using Neural Networks to Interpret the Turbulent Interstellar Medium
• Identification of Young Stellar Object candidates in the Gaia DR2 x AllWISE catalogue with machine learning methods
• Morphological classification of radio galaxies: Capsule Networks versus Convolutional Neural Networks
• Using Convolutional Neural Networks to identify Gravitational Lenses in Astronomical images
• Deep Neural Network Classifier for Variable Stars with Novelty Detection Capability
• Fast Wiener filtering of CMB maps with Neural Networks
• A deep learning model to emulate simulations of cosmic reionization
• Predicting Solar Flares Using a Long Short-Term Memory Network
• Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning
• Principal component analysis of the primordial tensor power spectrum
• A Bayesian direct method implementation to fit emission line spectra: Application to the primordial He abundance determination
• Projected Pupil Plane Pattern (PPPP) with artificial Neural Networks
• AutoRegressive Planet Search: Application to the Kepler Mission
• Multiwavelength cluster mass estimates and machine learning
• Deconfusing intensity maps with neural networks
• An extended catalog of galaxy-galaxy strong gravitational lenses discovered in DES using convolutional neural networks
• An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval
• Large-Scale Statistical Survey of Magnetopause Reconnection
• Anomaly Detection in the Open Supernova Catalog
• Neural network-based anomaly detection for high-resolution X-ray spectroscopy
• Gaussian-mixture-model-based cluster analysis of gamma-ray bursts in the BATSE catalogue
• KiDS-SQuaD II: Machine learning selection of bright extragalactic objects to search for new gravitationally lensed quasars
• Radio Galaxy Zoo: Unsupervised Clustering of Convolutionally Auto-encoded Radio-astronomical Images
• Learning Radiative Transfer Models for Climate Change Applications in Imaging Spectroscopy
• RadioGAN − Translations between different radio surveys with generative adversarial networks
• Classifying galaxies according to their HI content
• One simulation to have them all: performance of the Bias Assignment Method against N-body simulations
• An interpretable machine learning framework for dark matter halo formation
• General classification of light curves using extreme boosting
• Foreword to the Focus Issue on Machine Learning in Astronomy and Astrophysics
• Automated crater shape retrieval using weakly-supervised deep learning
• Constraining strongly coupled new physics from cosmic rays with machine learning techniques
• A Halo Merger Tree Generation and Evaluation Framework
• Determining surface rotation periods of solar-like stars observed by the Kepler mission using machine learning techniques
• Automatic classification of K2 pulsating stars using machine learning techniques
• A machine learning approach for GRB detection in AstroSat CZTI data
• Advanced Signal Reconstruction in Tunka-Rex with Matched Filtering and Deep Learning
• A Principal Component Analysis-based method to analyse high-resolution spectroscopic data
• Morpheus: A Deep Learning Framework For Pixel-Level Analysis of Astronomical Image Data
• Model Comparison of Dark Energy models Using Deep Network
• A Classifier to Detect Elusive Astronomical Objects through Photometry
• Probing Neural Networks for the Gamma/Hadron Separation of the Cherenkov Telescope Array
• Self-supervised Learning with Physics-aware Neural Networks I: Galaxy Model Fitting
• A convolutional neural network approach for reconstructing polarization information of photoelectric X-ray polarimeters
• Cataloging Accreted Stars within Gaia DR2 using Deep Learning
• Deep learning classification of the continuous gravitational-wave signal candidates from the time-domain F-statistic search
• Kernel ridge Regression
• Mixture Density Networks
• Masked Autoregressive Flow for Density Estimation