Evidential DL for Uncertainties and Anomaly Detection
Current methods commonly used for uncertainty quantification (UQ) in deep learning (DL) models utilize Bayesian methods which are computationally expensive and time-consuming. In …
Smart pixel sensor filtering with deep learning
Highly granular pixel detectors allow for increasingly precise measurements of charged particle tracks. Next-generation detectors require that pixel sizes will be further reduced, …
Jieun Yoo
Explainability of Deep Neural Networks in top quark tagging
Recent developments in the methods of explainable AI (XAI) allow researchers to explore the inner workings of deep neural networks (DNNs), revealing crucial information about …
Graph Neural Networks for Particle Tracking on FPGAs
The determination of charged particle trajectories in collisions at the CERN Large Hadron Collider (LHC) is an important but challenging problem, especially in the high interaction …
Abdelrahman Elabd
Fast Machine Learning for Science
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science—the concept of integrating powerful ML methods into the real-time …
Allison Mccarn Deiana