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20132022
most citedFAIR for AI: An interdisciplinary and international community building perspective

92 citations · 189 across the 14 of their papers we have counts for

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Showing 2022Show all

8 papers · 1 filter

hep-ex2022★ 10 cited

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…

hep-ex2022★ 2 cited

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…

hep-ex2022★ 1 cited

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…

cs.CY2022★ 92 cited

FAIR for AI: An interdisciplinary and international community building perspective

E. A. Huerta, Ben Blaiszik, L. Catherine Brinson +21

A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with th…

hep-ex2022★ 18 cited

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…

hep-ph2022★ 1 cited

Making Digital Objects FAIR in High Energy Physics: An Implementation for Universal FeynRules Output (UFO) Models

Mark S. Neubauer, Avik Roy, Zijun Wang

Research in the data-intensive discipline of high energy physics (HEP) often relies on domain-specific digital contents. Reproducibility of research relies on proper preservation o…

hep-ex2022★ 9 cited

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…

hep-ph2022★ 6 cited

Non-resonant Diagrams for Single Production of Top and Bottom Partners

Avik Roy, Timothy Andeen

The search for Top and Bottom Partners is a major focus of analyses at both the ATLAS and CMS experiments. When singly produced, these vector-like partners of the Standard Model th…