Davide Piras

According to our database, Davide Piras authored at least 15 papers between 2020 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

MAIS: Exploring human-AI interaction in fair and transparent recruitment with a multi-agent LLM-based system.

Davide Piras, Alberto Pes, Diego Reforgiato Recupero, Giuseppe Scarpi

Int. J. Hum. Comput. Stud., 2026

2025

Transfer learning for multifidelity simulation-based inference in cosmology.

A. A. Saoulis, Davide Piras, Niall Jeffrey, Alessio Spurio Mancini, A. M. G. Ferreira, Benjamin Joachimi

CoRR, May, 2025

Anchors no more: Using peculiar velocities to constrain H₀ and the primordial Universe without calibrators.

Davide Piras, Francesco Sorrenti, Ruth Durrer, Martin Kunz

CoRR, April, 2025

ΛCDM and early dark energy in latent space: a data-driven parametrization of the CMB temperature power spectrum.

Davide Piras, Laura Herold, Luisa Lucie-Smith, Eiichiro Komatsu

CoRR, February, 2025

Evaluating LLMs for Named Entity Recognition in Scientific Domain with Fine-Tuning and Few-Shot Learning.

Davide Buscaldi, Danilo Dessì, Francesco Osborne, Davide Piras, Diego Reforgiato Recupero

Proceedings of the Third International Workshop on Semantic Technologies and Deep Learning Models for Scientific, 2025

Exploring the Early Universe with Deep Learning.

Emmanuel de Salis, Massimo De Santis, Davide Piras, Sambit K. Giri, Michele Bianco, Nicolas Cerardi, Philipp Denzel, Merve Selcuk-Simsek, Kelley M. Hess, M. Carmen Toribio, Franz Kirsten, Hatem Ghorbel

Proceedings of the Progress in Artificial Intelligence, 2025

2024

Full-waveform earthquake source inversion using simulation-based inference.

A. A. Saoulis, Davide Piras, Alessio Spurio Mancini, Benjamin Joachimi, A. M. G. Ferreira

CoRR, 2024

The future of cosmological likelihood-based inference: accelerated high-dimensional parameter estimation and model comparison.

Davide Piras, Alicja Polanska, Alessio Spurio Mancini, Matthew A. Price, Jason D. McEwen

CoRR, 2024

2023

A robust estimator of mutual information for deep learning interpretability.

Davide Piras, Hiranya V. Peiris, Andrew Pontzen, Luisa Lucie-Smith, Ningyuan Guo, Brian Nord

Mach. Learn. Sci. Technol., June, 2023

A representation learning approach to probe for dynamical dark energy in matter power spectra.

Davide Piras, Lucas Lombriser

CoRR, 2023

CosmoPower-JAX: high-dimensional Bayesian inference with differentiable cosmological emulators.

Davide Piras, Alessio Spurio Mancini

CoRR, 2023

2022

Fast and realistic large-scale structure from machine-learning-augmented random field simulations.

Davide Piras, Benjamin Joachimi, Francisco Villaescusa-Navarro

CoRR, 2022

Discovering the building blocks of dark matter halo density profiles with neural networks.

Luisa Lucie-Smith, Hiranya V. Peiris, Andrew Pontzen, Brian Nord, Jeyan Thiyagalingam, Davide Piras

CoRR, 2022

2021

Towards fast machine-learning-assisted Bayesian posterior inference of realistic microseismic events.

Davide Piras, Alessio Spurio Mancini, Benjamin Joachimi, Michael P. Hobson

CoRR, 2021

2020

Learning to Noise: Application-Agnostic Data Sharing with Local Differential Privacy.

Alex Mansbridge, Gregory Barbour, Davide Piras, Christopher Frye, Ilya Feige, David Barber

CoRR, 2020