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Found 8 papers, 2 papers with code
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.
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.
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
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.
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.
Explainable AI for High Energy Physics
Neural Networks are ubiquitous in high energy physics research.
Explainable Artificial Intelligence (XAI)
Vocal Bursts Intensity Prediction
Recipes for when Physics Fails: Recovering Robust Learning of Physics Informed Neural Networks
Physics-informed Neural Networks (PINNs) have been shown to be effective in solving partial differential equations by capturing the physics induced constraints as a part of the training loss function.
Invariance-based Multi-Clustering of Latent Space Embeddings for Equivariant Learning
Variational Autoencoders (VAEs) have been shown to be remarkably effective in recovering model latent spaces for several computer vision tasks.
