most citedTransformer-Based Astronomical Time Series Model with Uncertainty Estimation for Detecting Misclassified Instances

1 citations · 1 across the 2 of their papers we have counts for

collaborators

6 papers

astro-ph.IM2025

ASTROCO: Self-Supervised Conformer-Style Transformers for Light-Curve Embeddings

Antony Tan, Pavlos Protopapas, Martina Cádiz-Leyton +3

We present AstroCo, a Conformer-style encoder for irregular stellar light curves. By combining attention with depthwise convolutions and gating, AstroCo captures both global depend…

astro-ph.IM2025

Astro-MoE: Mixture of Experts for Multiband Astronomical Time Series

Martina Cádiz-Leyton, Guillermo Cabrera-Vives, Pavlos Protopapas +2

Multiband astronomical time series exhibit heterogeneous variability patterns, sampling cadences, and signal characteristics across bands. Standard transformers apply shared parame…

astro-ph.IM2025

Leveraging pre-trained vision Transformers for multi-band photometric light curve classification

Daniel Moreno-Cartagena, Pavlos Protopapas, Guillermo Cabrera-Vives +3

This study investigates the potential of a pre-trained vision Transformer (VT) model, specifically the Swin Transformer V2 (SwinV2), to classify photometric light curves without th…

astro-ph.IM2025

Astromer 2

Cristobal Donoso-Oliva, Ignacio Becker, Pavlos Protopapas +3

Foundational models have emerged as a powerful paradigm in deep learning field, leveraging their capacity to learn robust representations from large-scale datasets and effectively…

astro-ph.IM2024

Uncertainty estimation for time series classification: Exploring predictive uncertainty in transformer-based models for variable stars

Martina Cádiz-Leyton, Guillermo Cabrera-Vives, Pavlos Protopapas +3

Classifying variable stars is key for understanding stellar evolution and galactic dynamics. With the demands of large astronomical surveys, machine learning models, especially att…

astro-ph.IM20241 cited

Transformer-Based Astronomical Time Series Model with Uncertainty Estimation for Detecting Misclassified Instances

Martina Cádiz-Leyton, Guillermo Cabrera-Vives, Pavlos Protopapas +2

In this work, we present a framework for estimating and evaluating uncertainty in deep-attention-based classifiers for light curves for variable stars. We implemented three techniq…