Publications en collaboration avec des chercheurs 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