François Lanusse

34 papers A* 6Journal 26Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration. CoRR LSST Dark Energy Science Collaboration, Eric Aubourg, Camille Avestruz, Matthew R. Becker, Biswajit Biswas, Rahul Biswas, Boris Bolliet, Adam S. Bolton, Clecio R. Bom, Raphaël Bonnet-Guerrini, Alexandre Boucaud, Jean-Eric Campagne, Chihway Chang, Aleksandra Ciprijanovic, Johann Cohen-Tanugi, Michael W. Coughlin, John Franklin Crenshaw, Juan C. Cuevas-Tello, Juan de Vicente, Seth W. Digel, Steven Dillmann, Mariano Javier de León Dominguez Romero, Alex Drlica-Wagner, Sydney Erickson, Alexander T. Gagliano, Christos Georgiou, Aritra Ghosh, Matthew Grayling, Kirill A. Grishin, Alan Heavens, Lindsay R. House, Mustapha Ishak, Wassim Kabalan, Arun Kannawadi, François Lanusse, C. Danielle Leonard, Pierre-François Léget, Michelle Lochner, Yao-Yuan Mao, Peter Melchior, Grant Merz, Martin Millon, Anais Möller, Gautham Narayan, Yuuki Omori, Hiranya Peiris, Laurence Perreault Levasseur, Andrés Alejandro Plazas Malagón, Nesar Ramachandra, Benjamin Remy, Cécile Roucelle, Jaime Ruiz-Zapatero, Stefan Schuldt, Ignacio Sevilla-Noarbe, Ved G. Shah, Tjitske Starkenburg, Stephen Thorp, Laura Toribio San Cipriano, Tilman Tröster, Roberto Trotta, Padma Venkatraman, Amanda Wasserman, Tim White, Justine Zeghal, Tianqing Zhang, Yuanyuan Zhang
2026	J	`jnl`	Representation Learning for Spatiotemporal Physical Systems. CoRR Helen Qu, Rudy Morel, Michael McCabe, Alberto Bietti, François Lanusse, Shirley Ho, Yann LeCun
2025	J	`jnl`	Hybrid Physical-Neural Simulator for Fast Cosmological Hydrodynamics. CoRR Arne Thomsen, Tilman Tröster, François Lanusse
2025	J	`jnl`	Lost in Latent Space: An Empirical Study of Latent Diffusion Models for Physics Emulation. CoRR François Rozet, Ruben Ohana, Michael McCabe, Gilles Louppe, François Lanusse, Shirley Ho
2025	J	`jnl`	Predicting partially observable dynamical systems via diffusion models with a multiscale inference scheme. CoRR Rudy Morel, Francesco Pio Ramunno, Jeff Shen, Alberto Bietti, Kyunghyun Cho, Miles D. Cranmer, Siavash Golkar, Olexandr Gugnin, Géraud Krawezik, Tanya Marwah, Michael McCabe, Lucas Meyer, Payel Mukhopadhyay, Ruben Ohana, Liam Holden Parker, Helen Qu, François Rozet, K. D. Leka, François Lanusse, David Fouhey, Shirley Ho
2025	J	`jnl`	Semantic search for 100M+ galaxy images using AI-generated captions. CoRR Nolan Koblischke, Liam Holden Parker, François Lanusse, Irina Espejo Morales, Jo Bovy, Shirley Ho
2025	J	`jnl`	Universal Spectral Tokenization via Self-Supervised Panchromatic Representation Learning. CoRR Jeff Shen, François Lanusse, Liam Holden Parker, Ollie Liu, Tom Hehir, Leopoldo Sarra, Lucas Meyer, Micah Bowles, Sebastian Wagner-Carena, Helen Qu, Siavash Golkar, Alberto Bietti, Hatim Bourfoune, Nathan Cassereau, Pierre Cornette, Keiya Hirashima, Géraud Krawezik, Ruben Ohana, Nicholas Lourie, Michael McCabe, Rudy Morel, Payel Mukhopadhyay, Mariel Pettee, Bruno Régaldo-Saint Blancard, Kyunghyun Cho, Miles D. Cranmer, Shirley Ho
2025	J	`jnl`	Walrus: A Cross-Domain Foundation Model for Continuum Dynamics. CoRR Michael McCabe, Payel Mukhopadhyay, Tanya Marwah, Bruno Régaldo-Saint Blancard, François Rozet, Cristiana Diaconu, Lucas Meyer, Kaze W. K. Wong, Hadi Sotoudeh, Alberto Bietti, Irina Espejo, Rio Fear, Siavash Golkar, Tom Hehir, Keiya Hirashima, Géraud Krawezik, François Lanusse, Rudy Morel, Ruben Ohana, Liam Holden Parker, Mariel Pettee, Jeff Shen, Kyunghyun Cho, Miles D. Cranmer, Shirley Ho
2024	J	`jnl`	Geometric deep learning for galaxy-halo connection: a case study for galaxy intrinsic alignments. CoRR Yesukhei Jagvaral, François Lanusse, Rachel Mandelbaum
2024	A*	`conf`	Learning Diffusion Priors from Observations by Expectation Maximization. NeurIPS François Rozet, Gérôme Andry, François Lanusse, Gilles Louppe
2024	J	`jnl`	Learning Diffusion Priors from Observations by Expectation Maximization. CoRR François Rozet, Gérôme Andry, François Lanusse, Gilles Louppe
2024	A*	`conf`	Multiple Physics Pretraining for Spatiotemporal Surrogate Models. NeurIPS 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
2024	A*	`conf`	The Multimodal Universe: Enabling Large-Scale Machine Learning with 100 TB of Astronomical Scientific Data. NeurIPS 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, Shirley Ho, Marc Huertas-Company, Kartheik Iyer, Maja Jablonska, François Lanusse, Henry Leung, Kaisey Mandel, Rafael Martínez-Galarza, Peter Melchior, Lucas Meyer, Liam Holden Parker, Helen Qu, Jeff Shen, Michael J. Smith, Connor Stone, Mike Walmsley, John F. Wu
2024	A*	`conf`	Unified Framework for Diffusion Generative Models in SO(3): Applications in Computer Vision and Astrophysics. AAAI Yesukhei Jagvaral, François Lanusse, Rachel Mandelbaum
2023	J	`jnl`	AstroCLIP: Cross-Modal Pre-Training for Astronomical Foundation Models. CoRR 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
2023	J	`jnl`	Multiple Physics Pretraining for Physical Surrogate Models. CoRR 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
2023	J	`jnl`	Unified framework for diffusion generative models in SO(3): applications in computer vision and astrophysics. CoRR Yesukhei Jagvaral, François Lanusse, Rachel Mandelbaum
2023	J	`jnl`	xVal: A Continuous Number Encoding for Large Language Models. CoRR 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
2022	J	`jnl`	Hybrid Physical-Neural ODEs for Fast N-body Simulations. CoRR Denise Lanzieri, François Lanusse, Jean-Luc Starck
2022	J	`jnl`	Probabilistic Mass Mapping with Neural Score Estimation. CoRR Benjamin Remy, François Lanusse, Niall Jeffrey, Jia Liu, Jean-Luc Starck, Ken Osato, Tim Schrabback
2021	A*	`conf`	Adaptive wavelet distillation from neural networks through interpretations. NeurIPS Wooseok Ha, Chandan Singh, François Lanusse, Srigokul Upadhyayula, Bin Yu
2021	J	`jnl`	Adaptive wavelet distillation from neural networks through interpretations. CoRR Wooseok Ha, Chandan Singh, François Lanusse, Eli Song, Song Dang, Kangmin He, Srigokul Upadhyayula, Bin Yu
2021	J	`jnl`	FlowPM: Distributed TensorFlow implementation of the FastPM cosmological N-body solver. Astron. Comput. Chirag Modi, François Lanusse, Uros Seljak
2021	J	`jnl`	Real-Time Likelihood-Free Inference of Roman Binary Microlensing Events with Amortized Neural Posterior Estimation. CoRR Keming Zhang, Joshua S. Bloom, B. Scott Gaudi, François Lanusse, Casey Lam, Jessica Lu
2020	J	`jnl`	Automating Inference of Binary Microlensing Events with Neural Density Estimation. CoRR Keming Zhang, Joshua S. Bloom, B. Scott Gaudi, François Lanusse, Casey Lam, Jessica Lu
2020	J	`jnl`	Bayesian Neural Networks. CoRR Tom Charnock, Laurence Perreault Levasseur, François Lanusse
2020	J	`jnl`	Denoising Score-Matching for Uncertainty Quantification in Inverse Problems. CoRR Zaccharie Ramzi, Benjamin Remy, François Lanusse, Jean-Luc Starck, Philippe Ciuciu
2020	J	`jnl`	Transformation Importance with Applications to Cosmology. CoRR Chandan Singh, Wooseok Ha, François Lanusse, Vanessa Böhm, Jia Liu, Bin Yu
2019	J	`jnl`	Hybrid Physical-Deep Learning Model for Astronomical Inverse Problems. CoRR François Lanusse, Peter Melchior, Fred Moolekamp
2019	J	`jnl`	Uncertainty Quantification with Generative Models. CoRR Vanessa Böhm, François Lanusse, Uros Seljak
2018	—	`conf`	Modelling Data with both Sparsity and a Gaussian Random Field: Application to Dark Matter Mass Mapping in Cosmology. EUSIPCO Konstantinos E. Themelis, François Lanusse, Niall Jeffrey, Austin Peel, Jean-Luc Starck, Filipe B. Abdalla
2017	A*	`conf`	Enabling Dark Energy Science with Deep Generative Models of Galaxy Images. AAAI Siamak Ravanbakhsh, François Lanusse, Rachel Mandelbaum, Jeff G. Schneider, Barnabás Póczos
2016	J	`jnl`	Enabling Dark Energy Science with Deep Generative Models of Galaxy Images. CoRR Siamak Ravanbakhsh, François Lanusse, Rachel Mandelbaum, Jeff G. Schneider, Barnabás Póczos
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). François Lanusse