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Found 15 papers, 6 papers with code
Distinguishing a planetary transit from false positives: a Transformer-based classification for planetary transit signals
Models, such as the Transformer architecture, were recently proposed for sequential data with successful results.
Informative regularization for a multi-layer perceptron RR Lyrae classifier under data shift
Consequently, we propose a scalable and easily adaptable approach based on an informative regularization and an ad-hoc training procedure to mitigate the shift problem during the training of a multi-layer perceptron for RR Lyrae classification.
Error-Aware B-PINNs: Improving Uncertainty Quantification in Bayesian Physics-Informed Neural Networks
Physics-Informed Neural Networks (PINNs) are gaining popularity as a method for solving differential equations.
Uncertainty Quantification in Neural Differential Equations
Uncertainty quantification (UQ) helps to make trustworthy predictions based on collected observations and uncertain domain knowledge.
Classifying CMB time-ordered data through deep neural networks
Finally, we performed a cross-season test over 148 GHz data from 2009 and 2010 for which our model reaches a precision of 99. 8% and 99. 5%, respectively.
Scalable End-to-end Recurrent Neural Network for Variable star classification
Our method uses minimal data preprocessing, can be updated with a low computational cost for new observations and light curves, and can scale up to massive datasets.
Streaming Classification of Variable Stars
Naively re-training from scratch is not an option in streaming settings, mainly because of the expensive pre-processing routines required to obtain a vector representation of light curves (features) each time we include new observations.
An Information Theory Approach on Deciding Spectroscopic Follow Ups
We propose a methodology in a probabilistic setting that determines a-priory which objects are worth taking spectrum to obtain better insights, where we focus 'insight' as the type of the object (classification).
An Algorithm for the Visualization of Relevant Patterns in Astronomical Light Curves
Within the Machine Learning literature, dictionary-based methods have been widely used to encode relevant parts of image data.
BIG-bench Machine Learning
Classification Of Variable Stars
+3
A Full Probabilistic Model for Yes/No Type Crowdsourcing in Multi-Class Classification
In this work, we provide a full probabilistic model for a shorter type of queries.
Deep multi-survey classification of variable stars
In this work, we present a novel Deep Learning model for light curve classification, mainly based on convolutional units.
Clustering Based Feature Learning on Variable Stars
Representatives of these patterns, called exemplars, are then used to transform lightcurves of a labeled set into a new representation that can then be used to train an automatic classifier.
Supervised detection of anomalous light-curves in massive astronomical catalogs
With the aim of taking full advantage of all the information we have about known objects, our method is based on a supervised algorithm.
Automatic Classification of Variable Stars in Catalogs with missing data
We present an automatic classification method for astronomical catalogs with missing data.
An improved quasar detection method in EROS-2 and MACHO LMC datasets
We present a new classification method for quasar identification in the EROS-2 and MACHO datasets based on a boosted version of Random Forest classifier.
