Nov 23, 2022 · We thus propose modelling calorimeter showers first by learning their manifold structure, and then estimating the density of data across this manifold.

We thus propose modelling calorimeter showers first by learning their manifold structure, and then estimating the density of data across this manifold. Learning ...

CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds. Humberto Reyes-González. University of Genoa (DiFi UniGe). 1.

We propose modelling calorimeter showers first by learning their manifold structure, and then estimating the density of data across this manifold.

The simple solution is a two-step approach: first learn the data manifold, then estimate the distribution on it. Jesse Cresswell (Layer 6 AI).

We thus propose modelling calorimeter showers first by learning their manifold structure, and then estimating the density of data across this manifold. Learning ...

We thus propose modelling calorimeter showers first by learning their manifold structure, and then estimating the density of data across this manifold. Learning ...

We thus propose a two-step approach for calorimer shower simulation: first we learn a lower-dimensional manifold structure with an auto-encoder, and then ...

We propose modelling calorimeter showers by first learning their manifold structure, then estimating the distribution of data on the manifold.

CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds. 2022·arXiv.