2 citations · 2 across the 8 of their papers we have counts for
3 papers · 1 filter
OmniCosmos: Transferring Particle Physics Knowledge Across the Cosmos
Vinicius Mikuni, Ibrahim Elsharkawy, Benjamin Nachman
Foundation models build an effective representations of data that can be deployed on diverse downstream tasks. Previous research developed the OmniLearned foundation model for coll…
Contrastive Normalizing Flows for Uncertainty-Aware Parameter Estimation
Ibrahim Elsharkawy, Yonatan Kahn
Estimating physical parameters from data is a crucial application of machine learning (ML) in the physical sciences. However, systematic uncertainties, such as detector miscalibrat…
Uncertainty Quantification From Scaling Laws in Deep Neural Networks
Ibrahim Elsharkawy, Yonatan Kahn, Benjamin Hooberman
Quantifying the uncertainty from machine learning analyses is critical to their use in the physical sciences. In this work we focus on uncertainty inherited from the initialization…