21 citations · 21 across the 2 of their papers we have counts for
6 papers
From Simulated Fluids to Living Tissue: Zero-Shot Foundation Model Transfer to Perturbed Embryos
Louisa Cornelis, Haewon Jeong, Irina Espejo +1
Morphogenesis is driven by the interplay of genetic regulation and tissue mechanics. In vivo video data is limited, particularly under genetic mutations and environmental perturbat…
Semantic search for 100M+ galaxy images using AI-generated captions
Nolan Koblischke, Liam Parker, Francois Lanusse +3
Finding scientifically interesting phenomena through slow manual labeling campaigns severely limits our ability to explore the billions of galaxy images produced by telescopes. In…
Walrus: A Cross-Domain Foundation Model for Continuum Dynamics
Michael McCabe, Payel Mukhopadhyay, Tanya Marwah +22
Foundation models have transformed machine learning for language and vision, but achieving comparable impact in physical simulation remains a challenge. Data heterogeneity and unst…
Scaling MadMiner with a deployment on REANA
Irina Espejo, Sinclert Pérez, Kenyi Hurtado +2
MadMiner is a Python package that implements a powerful family of multivariate inference techniques that leverage matrix element information and machine learning. This multivariate…
MadMiner: Machine learning-based inference for particle physics
Johann Brehmer, Felix Kling, Irina Espejo +1
Precision measurements at the LHC often require analyzing high-dimensional event data for subtle kinematic signatures, which is challenging for established analysis methods. Recent…
Effective LHC measurements with matrix elements and machine learning
Johann Brehmer, Kyle Cranmer, Irina Espejo +3
One major challenge for the legacy measurements at the LHC is that the likelihood function is not tractable when the collected data is high-dimensional and the detector response ha…