activity
20212026
most citedSearching for changing-state AGNs in massive datasets -- I: applying deep learning and anomaly detection techniques to find AGNs with anomalous variability behaviours

48 citations · 59 across the 6 of their papers we have counts for

collaborators

6 papers

astro-ph.IM2026

The Galaxy's Guide to the Tokenizer: A Benchmark for Scientific Foundation Models

Sogol Sanjaripour, Michael J. Smith, Manuel Pérez-Carrasco +3

Tokenization is central to adapting scientific data for transformer-based foundation models, yet its impact on learned representations remains poorly understood. We compare four to…

astro-ph.IM2023★ 2 cited

Domain Adaptation via Minimax Entropy for Real/Bogus Classification of Astronomical Alerts

Guillermo Cabrera-Vives, César Bolivar, Francisco Förster +3

Time domain astronomy is advancing towards the analysis of multiple massive datasets in real time, prompting the development of multi-stream machine learning models. In this work,…

astro-ph.IM2023★ 3 cited

Positional Encodings for Light Curve Transformers: Playing with Positions and Attention

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

We conducted empirical experiments to assess the transferability of a light curve transformer to datasets with different cadences and magnitude distributions using various position…

cs.LG2023

Multi-Class Deep SVDD: Anomaly Detection Approach in Astronomy with Distinct Inlier Categories

Manuel Pérez-Carrasco, Guillermo Cabrera-Vives, Lorena Hernández-García +8

With the increasing volume of astronomical data generated by modern survey telescopes, automated pipelines and machine learning techniques have become crucial for analyzing and ext…

cs.LG2022★ 6 cited

ConDA: Simplifying Semi-supervised Domain Adaptation by Learning Consistent and Contrastive Feature Representations

Manuel Pérez-Carrasco, Pavlos Protopapas, Guillermo Cabrera-Vives

In this work, we present ConDA, a simple framework that extends recent advances in semi-supervised learning to the semi-supervised domain adaptation (SSDA) problem. Our frame…

astro-ph.IM2021★ 48 cited

Searching for changing-state AGNs in massive datasets -- I: applying deep learning and anomaly detection techniques to find AGNs with anomalous variability behaviours

P. Sánchez-Sáez, H. Lira, L. Martí +14

The classic classification scheme for Active Galactic Nuclei (AGNs) was recently challenged by the discovery of the so-called changing-state (changing-look) AGNs (CSAGNs). The phys…