Search Results for author:
Found 2 papers, 0 papers with code
Uncertainty Quantification From Scaling Laws in Deep Neural Networks
Quantifying the uncertainty from machine learning analyses is critical to their use in the physical sciences.
Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics
These networks can serve as fast and computationally light methods for particle shower simulation and reconstruction for current and future experiments at particle colliders.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
