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ORCIDFrançois Lanusse
Person information
- affiliation (PhD 2015): Paris-Saclay University, AIM Lab, Paris, France
- affiliation: Carnegie Mellon University, McWilliams Center for Cosmology, Pittsburgh, PA, USA
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2020 – today
- 2024
[c4]Yesukhei Jagvaral, François Lanusse, Rachel Mandelbaum:
Unified Framework for Diffusion Generative Models in SO(3): Applications in Computer Vision and Astrophysics. AAAI 2024: 12754-12762- [i16]François Rozet, Gérôme Andry, François Lanusse, Gilles Louppe:
Learning Diffusion Priors from Observations by Expectation Maximization. CoRR abs/2405.13712 (2024) - 2023
- [i15]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, Kyunghyun Cho, Shirley Ho:
xVal: A Continuous Number Encoding for Large Language Models. CoRR abs/2310.02989 (2023) - [i14]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, Kyunghyun Cho, Shirley Ho:
Multiple Physics Pretraining for Physical Surrogate Models. CoRR abs/2310.02994 (2023) - [i13]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, Kyunghyun Cho, Shirley Ho:
AstroCLIP: Cross-Modal Pre-Training for Astronomical Foundation Models. CoRR abs/2310.03024 (2023) - [i12]Yesukhei Jagvaral, François Lanusse, Rachel Mandelbaum:
Unified framework for diffusion generative models in SO(3): applications in computer vision and astrophysics. CoRR abs/2312.11707 (2023) - 2022
- [i11]Benjamin Remy, François Lanusse, Niall Jeffrey, Jia Liu, Jean-Luc Starck, Ken Osato, Tim Schrabback:
Probabilistic Mass Mapping with Neural Score Estimation. CoRR abs/2201.05561 (2022) - [i10]Denise Lanzieri, François Lanusse, Jean-Luc Starck:
Hybrid Physical-Neural ODEs for Fast N-body Simulations. CoRR abs/2207.05509 (2022) - 2021
- [j1]Chirag Modi, François Lanusse, Uros Seljak:
FlowPM: Distributed TensorFlow implementation of the FastPM cosmological N-body solver. Astron. Comput. 37: 100505 (2021) - [c3]Wooseok Ha, Chandan Singh, François Lanusse, Srigokul Upadhyayula, Bin Yu:
Adaptive wavelet distillation from neural networks through interpretations. NeurIPS 2021: 20669-20682 - [i9]Keming Zhang, Joshua S. Bloom, B. Scott Gaudi, François Lanusse, Casey Lam, Jessica Lu:
Real-Time Likelihood-Free Inference of Roman Binary Microlensing Events with Amortized Neural Posterior Estimation. CoRR abs/2102.05673 (2021) - [i8]Wooseok Ha, Chandan Singh, François Lanusse, Eli Song, Song Dang, Kangmin He, Srigokul Upadhyayula, Bin Yu:
Adaptive wavelet distillation from neural networks through interpretations. CoRR abs/2107.09145 (2021) - 2020
- [i7]Chandan Singh, Wooseok Ha, François Lanusse, Vanessa Böhm, Jia Liu, Bin Yu:
Transformation Importance with Applications to Cosmology. CoRR abs/2003.01926 (2020) - [i6]Tom Charnock, Laurence Perreault Levasseur, François Lanusse:
Bayesian Neural Networks. CoRR abs/2006.01490 (2020) - [i5]Keming Zhang, Joshua S. Bloom, B. Scott Gaudi, François Lanusse, Casey Lam, Jessica Lu:
Automating Inference of Binary Microlensing Events with Neural Density Estimation. CoRR abs/2010.04156 (2020) - [i4]Zaccharie Ramzi, Benjamin Remy, François Lanusse, Jean-Luc Starck, Philippe Ciuciu:
Denoising Score-Matching for Uncertainty Quantification in Inverse Problems. CoRR abs/2011.08698 (2020)
2010 – 2019
- 2019
- [i3]Vanessa Böhm, François Lanusse, Uros Seljak:
Uncertainty Quantification with Generative Models. CoRR abs/1910.10046 (2019) - [i2]François Lanusse, Peter Melchior, Fred Moolekamp:
Hybrid Physical-Deep Learning Model for Astronomical Inverse Problems. CoRR abs/1912.03980 (2019) - 2018
- [c2]Konstantinos E. Themelis, François Lanusse, Niall Jeffrey, Austin Peel, Jean-Luc Starck, Filipe B. Abdalla:
Modelling Data with both Sparsity and a Gaussian Random Field: Application to Dark Matter Mass Mapping in Cosmology. EUSIPCO 2018: 376-379 - 2017
- [c1]Siamak Ravanbakhsh, François Lanusse, Rachel Mandelbaum, Jeff G. Schneider, Barnabás Póczos:
Enabling Dark Energy Science with Deep Generative Models of Galaxy Images. AAAI 2017: 1488-1494 - 2016
- [i1]Siamak Ravanbakhsh, François Lanusse, Rachel Mandelbaum, Jeff G. Schneider, Barnabás Póczos:
Enabling Dark Energy Science with Deep Generative Models of Galaxy Images. CoRR abs/1609.05796 (2016) - 2015
- [b1]François Lanusse:
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). University of Paris-Saclay, France, 2015
Coauthor Index
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last updated on 2024-06-19 21:47 CEST by the dblp team
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