7 citations · 10 across the 5 of their papers we have counts for
8 papers
Large-scale bioacoustic detection using semantic segmentation: a deep learning framework applied to fin whale calls in ocean-bottom seismometer recordings
Jocelyn Japnanto, Alex A. Saoulis, Miriam Romagosa +5
Ocean-bottom seismometers (OBS), originally deployed for geophysical research, continuously record low-frequency sound for months to years across broad areas of ocean, offering a l…
Field-level weak lensing cosmology with simulations using multifidelity simulation-based inference
Alex A. Saoulis, Kiyam Lin, Niall Jeffrey +5
We perform a realistic KiDS-Legacy mock analysis with field-level neural compression and simulation-based inference using just 60 -body simulations. The weak lensing shear field…
Improving moment tensor solutions under Earth structure uncertainty with simulation-based inference
A. A. Saoulis, T. -S. Pham, A. M. G. Ferreira
Bayesian inference represents a principled way to incorporate Earth structure uncertainty in full-waveform moment tensor inversions, but traditional approaches generally require si…
Transfer learning for multifidelity simulation-based inference in cosmology
Alex A. Saoulis, Davide Piras, Niall Jeffrey +3
Simulation-based inference (SBI) enables cosmological parameter estimation when closed-form likelihoods or models are unavailable. However, SBI relies on machine learning for neura…
Full-waveform earthquake source inversion using simulation-based inference
A. A. Saoulis, D. Piras, A. Spurio Mancini +2
This paper presents a novel framework for full-waveform seismic source inversion using simulation-based inference (SBI). Traditional probabilistic approaches often rely on simplify…
Sparse Bayesian mass-mapping using trans-dimensional MCMC
Augustin Marignier, Thomas Kitching, Jason D. McEwen +1
Uncertainty quantification is a crucial step of cosmological mass-mapping that is often ignored. Suggested methods are typically only approximate or make strong assumptions of Gaus…