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2020 – today
- 2026
[j2]Davide Piras, Alberto Pes, Diego Reforgiato Recupero
, Giuseppe Scarpi:
MAIS: Exploring human-AI interaction in fair and transparent recruitment with a multi-agent LLM-based system. Int. J. Hum. Comput. Stud. 215: 103859 (2026)- 2025
- [c2]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:
Exploring the Early Universe with Deep Learning. EPIA (1) 2025: 426-438 - [c1]Davide Buscaldi, Danilo Dessì, Francesco Osborne, Davide Piras, Diego Reforgiato Recupero:
Evaluating LLMs for Named Entity Recognition in Scientific Domain with Fine-Tuning and Few-Shot Learning. SemTech4STLD@ESWC 2025 - [i13]Davide Piras, Laura Herold, Luisa Lucie-Smith, Eiichiro Komatsu:
ΛCDM and early dark energy in latent space: a data-driven parametrization of the CMB temperature power spectrum. CoRR abs/2502.09810 (2025) - [i12]Davide Piras, Francesco Sorrenti, Ruth Durrer, Martin Kunz:
Anchors no more: Using peculiar velocities to constrain H0 and the primordial Universe without calibrators. CoRR abs/2504.10453 (2025) - [i11]Alex A. Saoulis, Davide Piras, Niall Jeffrey, Alessio Spurio Mancini, Ana M. G. Ferreira, Benjamin Joachimi:
Transfer learning for multifidelity simulation-based inference in cosmology. CoRR abs/2505.21215 (2025) - [i10]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:
Exploring the Early Universe with Deep Learning. CoRR abs/2509.22018 (2025) - 2024
- [i9]Davide Piras, Alicja Polanska, Alessio Spurio Mancini, Matthew A. Price, Jason D. McEwen:
The future of cosmological likelihood-based inference: accelerated high-dimensional parameter estimation and model comparison. CoRR abs/2405.12965 (2024) - [i8]Alex A. Saoulis, Davide Piras, Alessio Spurio Mancini, Benjamin Joachimi, Ana M. G. Ferreira:
Full-waveform earthquake source inversion using simulation-based inference. CoRR abs/2410.23238 (2024) - 2023
- [j1]Davide Piras, Hiranya V. Peiris, Andrew Pontzen, Luisa Lucie-Smith, Ningyuan Guo, Brian Nord:
A robust estimator of mutual information for deep learning interpretability. Mach. Learn. Sci. Technol. 4(2): 25006 (2023) - [i7]Davide Piras, Alessio Spurio Mancini:
CosmoPower-JAX: high-dimensional Bayesian inference with differentiable cosmological emulators. CoRR abs/2305.06347 (2023) - [i6]Davide Piras, Lucas Lombriser:
A representation learning approach to probe for dynamical dark energy in matter power spectra. CoRR abs/2310.10717 (2023) - 2022
- [i5]Luisa Lucie-Smith, Hiranya V. Peiris, Andrew Pontzen, Brian Nord, Jeyan Thiyagalingam, Davide Piras:
Discovering the building blocks of dark matter halo density profiles with neural networks. CoRR abs/2203.08827 (2022) - [i4]Davide Piras, Benjamin Joachimi, Francisco Villaescusa-Navarro:
Fast and realistic large-scale structure from machine-learning-augmented random field simulations. CoRR abs/2205.07898 (2022) - [i3]Davide Piras, Hiranya V. Peiris, Andrew Pontzen, Luisa Lucie-Smith, Ningyuan Guo, Brian Nord:
A robust estimator of mutual information for deep learning interpretability. CoRR abs/2211.00024 (2022) - 2021
- [i2]Davide Piras, Alessio Spurio Mancini, Benjamin Joachimi, Michael P. Hobson:
Towards fast machine-learning-assisted Bayesian posterior inference of realistic microseismic events. CoRR abs/2101.04724 (2021) - 2020
- [i1]Alex Mansbridge, Gregory Barbour, Davide Piras, Christopher Frye, Ilya Feige, David Barber:
Learning to Noise: Application-Agnostic Data Sharing with Local Differential Privacy. CoRR abs/2010.12464 (2020)
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