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Found 11 papers, 2 papers with code
A Roadmap for HEP Software and Computing R&D for the 2020s
Particle physics has an ambitious and broad experimental programme for the coming decades.
Computational Physics High Energy Physics - Experiment
A Detailed Study of Interpretability of Deep Neural Network based Top Taggers
We additionally illustrate the activity of hidden layers as Neural Activation Pattern (NAP) diagrams and demonstrate how they can be used to understand how DNNs relay information across the layers and how this understanding can help to make such models significantly simpler by allowing effective model reoptimization and hyperparameter tuning.
Explainable Artificial Intelligence (XAI)
Feature Importance
A FAIR and AI-ready Higgs boson decay dataset
To enable the reusability of massive scientific datasets by humans and machines, researchers aim to adhere to the principles of findability, accessibility, interoperability, and reusability (FAIR) for data and artificial intelligence (AI) models.
Applications and Techniques for Fast Machine Learning in Science
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery.
Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges
Where previously these sets of data have been formulated as series or image data to match the available machine learning architectures, with the advent of graph neural networks (GNNs), these systems can be learned natively as graphs.
Explainable AI for High Energy Physics
Neural Networks are ubiquitous in high energy physics research.
Explainable Artificial Intelligence (XAI)
Vocal Bursts Intensity Prediction
Data Science and Machine Learning in Education
The growing role of data science (DS) and machine learning (ML) in high-energy physics (HEP) is well established and pertinent given the complex detectors, large data, sets and sophisticated analyses at the heart of HEP research.
FAIR for AI: An interdisciplinary and international community building perspective
A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data.
Interpretability of an Interaction Network for identifying $H \rightarrow b\bar{b}$ jets
In recent times, AI models based on deep neural networks are becoming increasingly popular for many of these applications.
FAIR AI Models in High Energy Physics
The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, and improving how data is shared to facilitate scientific discovery.
Low Latency Edge Classification GNN for Particle Trajectory Tracking on FPGAs
In-time particle trajectory reconstruction in the Large Hadron Collider is challenging due to the high collision rate and numerous particle hits.
