| | Sorbonne University / CNRS |
- Transfer learning for jet energy scales
- Denoising diffusion probabilistic models
- Physics Model-Based AI for Rare Events
| |
| | |
- Extrapolation in ML
- Comprehensive uncertainties for generative models
| |
| | |
- General searches with (GNN) AutoEncoders
- Transfer learning for jet energy scales
- Explainable AI for Online and Transferable Learning
| |
| | |
- Foundational models
- General searches with (GNN) AutoEncoders
- New Generative Models for Parton Distributions
| |
Svyatoslav Voloshynovskiy | | |
- Denoising diffusion probabilistic models
- Transfer learning for jet energy scales
- Explainable AI for Online and Transferable Learning
| |
| | |
- New Generative Models for Parton Distributions
| |
| | |
- Explainable AI for Online and Transferable Learning
- General searches with (GNN) AutoEncoders
- Physics Model-Based AI for Rare Events
| |
| | |
- Comprehensive uncertainties for generative models
- Extrapolation in ML
- Foundational models
| |
| | |
- Physics Model-Based AI for Rare Events
- Denoising diffusion probabilistic models
- New Generative Models for Parton Distributions
| |
| | |
- Comprehensive uncertainties for generative models
| |
Johann Ioannou-Nikolaides | | |
- General searches with (GNN) AutoEncoders
| |
| | |
- Denoising diffusion probabilistic models
| |
| | | | |
| | |
- Explainable AI for Online and Transferable Learning
| |
| | |
- Comprehensive uncertainties for generative models
| |
| | |
- Physics Model-Based AI for Rare Events
| |