6 papers
Revisiting OmniAnomaly for Anomaly Detection: performance metrics and comparison with PCA-based models
Bruna Alves, Ana Martins, Armando J. Pinho +1
Deep learning models have become the dominant approach for multivariate time series anomaly detection (MTSAD), often reporting substantial performance improvements over classical s…
Cosmoglobe: Mapping the Universe from the Milky Way to the Big Bang
Ana Isabel Silva Martins, Cosmoglobe Collaboration
The Cosmoglobe project is a global effort to jointly analyze complementary cosmological and astrophysical datasets, in order to better understand our Universe and its evolution. Th…
A two-step sequential approach for hyperparameter selection in finite context models
José Contente, Ana Martins, Armando J. Pinho +1
Finite-context models (FCMs) are widely used for compressing symbolic sequences such as DNA, where predictive performance depends critically on the context length k and smoothing p…
Fast and Interpretable Autoregressive Estimation with Neural Network Backpropagation
AnaÃsa Lucena, Ana Martins, Armando J. Pinho +1
Autoregressive (AR) models remain widely used in time series analysis due to their interpretability, but convencional parameter estimation methods can be computationally expensive…
Ameliorating transient noise bursts in gravitational-wave searches for intermediate-mass black holes
Melissa Lopez, Giada Caneva, Ana Martins +5
The direct observation of intermediate-mass black holes (IMBH) populations would not only strengthen the possible evolutionary link between stellar and supermassive black holes, bu…
Improving early detection of gravitational waves from binary neutron stars using CNNs and FPGAs
Ana Martins, Melissa Lopez, Quirijn Meijer +4
The detection of gravitational waves (GWs) from binary neutron stars (BNSs) with possible telescope follow-ups opens a window to ground-breaking discoveries in the field of multi-m…