Sparse reconstruction of the dark matter mass map from weak gravitational lensing. (Reconstruction parcimonieuse de la carte de masse de matière noire par effet de lentille gravitationnelle). - François Lanusse (2015)

Enabling Dark Energy Science with Deep Generative Models of Galaxy Images. - Siamak Ravanbakhsh; François Lanusse; Rachel Mandelbaum; Jeff G. Schneider; Barnabás Póczos (AAAI, 2017)

Modelling Data with both Sparsity and a Gaussian Random Field: Application to Dark Matter Mass Mapping in Cosmology. - Konstantinos E. Themelis; François Lanusse; Niall Jeffrey; Austin Peel; Jean-Luc Starck; Filipe B. Abdalla (EUSIPCO, 2018)

Uncertainty Quantification with Generative Models. - Vanessa Böhm; François Lanusse; Uros Seljak (CoRR, 2019)

Hybrid Physical-Deep Learning Model for Astronomical Inverse Problems. - François Lanusse; Peter Melchior; Fred Moolekamp (CoRR, 2019)

Transformation Importance with Applications to Cosmology. - Chandan Singh; Wooseok Ha; François Lanusse; Vanessa Böhm; Jia Liu; Bin Yu 001 (CoRR, 2020)

Bayesian Neural Networks. - Tom Charnock; Laurence Perreault Levasseur; François Lanusse (CoRR, 2020)

Automating Inference of Binary Microlensing Events with Neural Density Estimation. - Keming Zhang; Joshua S. Bloom; B. Scott Gaudi; François Lanusse; Casey Lam; Jessica Lu (CoRR, 2020)

Denoising Score-Matching for Uncertainty Quantification in Inverse Problems. - Zaccharie Ramzi; Benjamin Remy; François Lanusse; Jean-Luc Starck; Philippe Ciuciu (CoRR, 2020)

Adaptive wavelet distillation from neural networks through interpretations. - Wooseok Ha; Chandan Singh; François Lanusse; Srigokul Upadhyayula; Bin Yu 001 (NeurIPS, 2021)

FlowPM: Distributed TensorFlow implementation of the FastPM cosmological N-body solver. - Chirag Modi 002; François Lanusse; Uros Seljak (Astron. Comput., 2021)

Real-Time Likelihood-Free Inference of Roman Binary Microlensing Events with Amortized Neural Posterior Estimation. - Keming Zhang; Joshua S. Bloom; B. Scott Gaudi; François Lanusse; Casey Lam; Jessica Lu (CoRR, 2021)

Adaptive wavelet distillation from neural networks through interpretations. - Wooseok Ha; Chandan Singh; François Lanusse; Eli Song; Song Dang; Kangmin He; Srigokul Upadhyayula; Bin Yu 001 (CoRR, 2021)

Probabilistic Mass Mapping with Neural Score Estimation. - Benjamin Remy; François Lanusse; Niall Jeffrey; Jia Liu; Jean-Luc Starck; Ken Osato; Tim Schrabback (CoRR, 2022)

Hybrid Physical-Neural ODEs for Fast N-body Simulations. - Denise Lanzieri; François Lanusse; Jean-Luc Starck (CoRR, 2022)

xVal: A Continuous Number Encoding for Large Language Models. - Siavash Golkar; Mariel Pettee; Michael Eickenberg; Alberto Bietti; Miles D. Cranmer; Géraud Krawezik; François Lanusse; Michael McCabe; Ruben Ohana; Liam Holden Parker; Bruno Régaldo-Saint Blancard; Tiberiu Tesileanu; et al. (CoRR, 2023)

Multiple Physics Pretraining for Physical Surrogate Models. - Michael McCabe; Bruno Régaldo-Saint Blancard; Liam Holden Parker; Ruben Ohana; Miles D. Cranmer; Alberto Bietti; Michael Eickenberg; Siavash Golkar; Géraud Krawezik; François Lanusse; Mariel Pettee; Tiberiu Tesileanu; et al. (CoRR, 2023)

AstroCLIP: Cross-Modal Pre-Training for Astronomical Foundation Models. - François Lanusse; Liam Holden Parker; Siavash Golkar; Miles D. Cranmer; Alberto Bietti; Michael Eickenberg; Géraud Krawezik; Michael McCabe; Ruben Ohana; Mariel Pettee; Bruno Régaldo-Saint Blancard; Tiberiu Tesileanu; et al. (CoRR, 2023)

Unified Framework for Diffusion Generative Models in SO(3): Applications in Computer Vision and Astrophysics. - Yesukhei Jagvaral; François Lanusse; Rachel Mandelbaum (AAAI, 2024)

The Multimodal Universe: Enabling Large-Scale Machine Learning with 100 TB of Astronomical Scientific Data. - Eirini Angeloudi; Jeroen Audenaert; Micah Bowles; Benjamin M. Boyd; David Chemaly; Brian Cherinka; Ioana Ciuca; Miles D. Cranmer; Aaron Do; Matthew Grayling; Erin E. Hayes; Tom Hehir; et al. (NeurIPS, 2024)

Multiple Physics Pretraining for Spatiotemporal Surrogate Models. - Michael McCabe; Bruno Régaldo-Saint Blancard; Liam Holden Parker; Ruben Ohana; Miles D. Cranmer; Alberto Bietti; Michael Eickenberg; Siavash Golkar; Géraud Krawezik; François Lanusse; Mariel Pettee; Tiberiu Tesileanu; et al. (NeurIPS, 2024)

Learning Diffusion Priors from Observations by Expectation Maximization. - François Rozet; Gérôme Andry; François Lanusse; Gilles Louppe (NeurIPS, 2024)

Geometric deep learning for galaxy-halo connection: a case study for galaxy intrinsic alignments. - Yesukhei Jagvaral; François Lanusse; Rachel Mandelbaum (CoRR, 2024)

Lost in Latent Space: An Empirical Study of Latent Diffusion Models for Physics Emulation. - François Rozet; Ruben Ohana; Michael McCabe; Gilles Louppe; François Lanusse; Shirley Ho (CoRR, 2025)