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2020 – today
- 2024
[i8]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)- 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)
Coauthor Index
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