92 citations · 189 across the 14 of their papers we have counts for
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FAIR AI Models in High Energy Physics
Javier Duarte, Haoyang Li, Avik Roy +14
The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, and improving how data is shared to facilitate scientifi…
FAIR Principles for data and AI models in high energy physics research and education
Avik Roy
In recent years, digital object management practices to support findability, accessibility, interoperability, and reusability (FAIR) have begun to be adopted across a number of dat…
Deep Learning for the Matrix Element Method
Matthew Feickert, Mihir Katare, Mark Neubauer +1
Extracting scientific results from high-energy collider data involves the comparison of data collected from the experiments with synthetic data produced from computationally-intens…
A Detailed Study of Interpretability of Deep Neural Network based Top Taggers
Ayush Khot, Mark S. Neubauer, Avik Roy
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 input…
Explainable AI for High Energy Physics
Mark S. Neubauer, Avik Roy
Neural Networks are ubiquitous in high energy physics research. However, these highly nonlinear parameterized functions are treated as \textit{black boxes}- whose inner workings to…