François Lanusse

Orcid: 0000-0001-7956-0542

Affiliations:
  • Paris-Saclay University, AIM Lab, Paris, France (PhD 2015)
  • Carnegie Mellon University, McWilliams Center for Cosmology, Pittsburgh, PA, USA


According to our database1, François Lanusse authored at least 19 papers between 2015 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2024
Unified Framework for Diffusion Generative Models in SO(3): Applications in Computer Vision and Astrophysics.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
AstroCLIP: Cross-Modal Pre-Training for Astronomical Foundation Models.
CoRR, 2023

Multiple Physics Pretraining for Physical Surrogate Models.
CoRR, 2023

xVal: A Continuous Number Encoding for Large Language Models.
CoRR, 2023

2022
Hybrid Physical-Neural ODEs for Fast N-body Simulations.
CoRR, 2022

Probabilistic Mass Mapping with Neural Score Estimation.
CoRR, 2022

2021
Adaptive wavelet distillation from neural networks through interpretations.
CoRR, 2021

Real-Time Likelihood-Free Inference of Roman Binary Microlensing Events with Amortized Neural Posterior Estimation.
CoRR, 2021

FlowPM: Distributed TensorFlow implementation of the FastPM cosmological N-body solver.
Astron. Comput., 2021

Adaptive wavelet distillation from neural networks through interpretations.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020
Denoising Score-Matching for Uncertainty Quantification in Inverse Problems.
CoRR, 2020

Automating Inference of Binary Microlensing Events with Neural Density Estimation.
CoRR, 2020

Bayesian Neural Networks.
CoRR, 2020

Transformation Importance with Applications to Cosmology.
CoRR, 2020

2019
Hybrid Physical-Deep Learning Model for Astronomical Inverse Problems.
CoRR, 2019

Uncertainty Quantification with Generative Models.
CoRR, 2019

2018
Modelling Data with both Sparsity and a Gaussian Random Field: Application to Dark Matter Mass Mapping in Cosmology.
Proceedings of the 26th European Signal Processing Conference, 2018

2017
Enabling Dark Energy Science with Deep Generative Models of Galaxy Images.
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2015
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).
PhD thesis, 2015


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