Publicaciones en colaboración con investigadores/as de Centre National de la Recherche Scientifique (23)

2024

  1. Artificial Intelligence for the Electron Ion Collider (AI4EIC)

    Computing and Software for Big Science, Vol. 8, Núm. 1

2023

  1. An Introduction to Machine Learning: a perspective from Statistical Physics

    Physica A: Statistical Mechanics and its Applications, Vol. 631

  2. Study of N= 50 gap evolution around Z= 32 : new structure information for 82 Ge

    European Physical Journal A, Vol. 59, Núm. 7

2022

  1. New horizons for fundamental physics with LISA

    Living Reviews in Relativity, Vol. 25, Núm. 1

  2. Regularization of Mixture Models for Robust Principal Graph Learning

    IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 44, Núm. 12, pp. 9119-9130

  3. Using host galaxy spectroscopy to explore systematics in the standardization of Type Ia supernovae

    Monthly Notices of the Royal Astronomical Society, Vol. 517, Núm. 3, pp. 4291-4304

  4. Velocity dispersions of clusters in the Dark Energy Survey Y3 redMaPPer catalogue

    Monthly Notices of the Royal Astronomical Society, Vol. 514, Núm. 4, pp. 4696-4717

2021

  1. Rates and delay times of Type Ia supernovae in the Dark Energy Survey

    Monthly Notices of the Royal Astronomical Society, Vol. 506, Núm. 3, pp. 3330-3348

  2. The first Hubble diagram and cosmological constraints using superluminous supernovae

    Monthly Notices of the Royal Astronomical Society, Vol. 504, Núm. 2, pp. 2535-2549

2019

  1. Learning a local symmetry with neural networks

    Physical Review E, Vol. 100, Núm. 5

  2. On the critical exponent α of the 5D random-field Ising model

    Journal of Statistical Mechanics: Theory and Experiment, Vol. 2019, Núm. 9

2017

  1. An Ising model for metal-organic frameworks

    Journal of Chemical Physics, Vol. 147, Núm. 8

2014

  1. Belief-propagation-guided Monte-Carlo sampling

    Physical Review B - Condensed Matter and Materials Physics, Vol. 89, Núm. 21

2011

  1. Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications

    Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, Vol. 84, Núm. 6

  2. Inference and phase transitions in the detection of modules in sparse networks

    Physical Review Letters, Vol. 107, Núm. 6