@louisserrano.bsky.social
Reposted
- "Zebra: In-Context Generative Pretraining for Solving Parametric PDEs" from Louis Serrano, @armandkassai.bsky.social, Thomas Wang, Pierre ERBACHER, Patrick Gallinari
📄 arxiv.org/abs/2410.03437
🖥️ github.com/LouisSerrano...
👤 Poster: Thu 17 Jul 11 a.m. PDT — 1:30 p.m. PDT
📄 arxiv.org/abs/2410.03437
🖥️ github.com/LouisSerrano...
👤 Poster: Thu 17 Jul 11 a.m. PDT — 1:30 p.m. PDT
July 9, 2025 at 9:21 AM
- "Zebra: In-Context Generative Pretraining for Solving Parametric PDEs" from Louis Serrano, @armandkassai.bsky.social, Thomas Wang, Pierre ERBACHER, Patrick Gallinari
📄 arxiv.org/abs/2410.03437
🖥️ github.com/LouisSerrano...
👤 Poster: Thu 17 Jul 11 a.m. PDT — 1:30 p.m. PDT
📄 arxiv.org/abs/2410.03437
🖥️ github.com/LouisSerrano...
👤 Poster: Thu 17 Jul 11 a.m. PDT — 1:30 p.m. PDT
Reposted
Leveraging LLMs' in-context learning for modeling physical dynamics. Paper #ICML2025.
"Zebra: In-Context Generative Pretraining for Solving Parametric PDEs", Louis Serrano, Armand Kassai, Thomas Wang, Pierre ERBACHER, Patrick Gallinari
📄 arxiv.org/abs/2410.03437
🖥️ github.com/LouisSerrano...
"Zebra: In-Context Generative Pretraining for Solving Parametric PDEs", Louis Serrano, Armand Kassai, Thomas Wang, Pierre ERBACHER, Patrick Gallinari
📄 arxiv.org/abs/2410.03437
🖥️ github.com/LouisSerrano...
Zebra: In-Context Generative Pretraining for Solving Parametric PDEs
Solving time-dependent parametric partial differential equations (PDEs) is challenging for data-driven methods, as these models must adapt to variations in parameters such as coefficients, forcing ter...
arxiv.org
July 21, 2025 at 8:52 AM
Leveraging LLMs' in-context learning for modeling physical dynamics. Paper #ICML2025.
"Zebra: In-Context Generative Pretraining for Solving Parametric PDEs", Louis Serrano, Armand Kassai, Thomas Wang, Pierre ERBACHER, Patrick Gallinari
📄 arxiv.org/abs/2410.03437
🖥️ github.com/LouisSerrano...
"Zebra: In-Context Generative Pretraining for Solving Parametric PDEs", Louis Serrano, Armand Kassai, Thomas Wang, Pierre ERBACHER, Patrick Gallinari
📄 arxiv.org/abs/2410.03437
🖥️ github.com/LouisSerrano...
Reposted
🚀Thrilled to introduce JAFAR—a lightweight, flexible, plug-and-play module that upsamples features from any Foundation Vision Encoder to any desired output resolution (1/n)
Paper : arxiv.org/abs/2506.11136
Project Page: jafar-upsampler.github.io
Github: github.com/PaulCouairon...
Paper : arxiv.org/abs/2506.11136
Project Page: jafar-upsampler.github.io
Github: github.com/PaulCouairon...
June 16, 2025 at 1:59 PM
🚀Thrilled to introduce JAFAR—a lightweight, flexible, plug-and-play module that upsamples features from any Foundation Vision Encoder to any desired output resolution (1/n)
Paper : arxiv.org/abs/2506.11136
Project Page: jafar-upsampler.github.io
Github: github.com/PaulCouairon...
Paper : arxiv.org/abs/2506.11136
Project Page: jafar-upsampler.github.io
Github: github.com/PaulCouairon...
Reposted
Two papers #NeurIPS2024 from my group on physics-aware ML
AROMA: Preserving Spatial Structure for Latent PDE Modeling with Local Neural Fields
openreview.net/pdf?id=Aj8RK...
Boosting Generalization in Parametric PDE Neural Solvers through Adaptive Conditioning
openreview.net/pdf?id=GuY0z...
AROMA: Preserving Spatial Structure for Latent PDE Modeling with Local Neural Fields
openreview.net/pdf?id=Aj8RK...
Boosting Generalization in Parametric PDE Neural Solvers through Adaptive Conditioning
openreview.net/pdf?id=GuY0z...
openreview.net
December 13, 2024 at 5:17 PM
Two papers #NeurIPS2024 from my group on physics-aware ML
AROMA: Preserving Spatial Structure for Latent PDE Modeling with Local Neural Fields
openreview.net/pdf?id=Aj8RK...
Boosting Generalization in Parametric PDE Neural Solvers through Adaptive Conditioning
openreview.net/pdf?id=GuY0z...
AROMA: Preserving Spatial Structure for Latent PDE Modeling with Local Neural Fields
openreview.net/pdf?id=Aj8RK...
Boosting Generalization in Parametric PDE Neural Solvers through Adaptive Conditioning
openreview.net/pdf?id=GuY0z...