This paper explores the use of a lightweight and scalable EfficientNet architecture, combined with global jet features, for the computationally inexpensive yet competitive classification of top-quark jets. It aims to address the high computational demands of current state-of-the-art jet tagging methods like Transformers and GNNs.

This tutorial provides comprehensive lecture notes on regression analysis, covering fundamental concepts from linear models to deep learning. It aims to equip students with a solid understanding of various regression models, including linear, logistic, and Softmax regression, along with essential methodologies like loss function design, parameter estimation, and regularization techniques, bridging classical statistics and modern machine learning practices.

This paper demonstrates that deep neural networks, despite being trained on diverse tasks and initializations, converge to remarkably similar low-dimensional parametric subspaces. This finding offers significant implications for model reusability, multi-task learning, and reducing the computational and environmental costs of large-scale neural models.

Structured Uncertainty Similarity Score (SUSS) is a new perceptual image similarity metric that models images through structured multivariate Normal distributions for interpretable, human-aligned assessments. It achieves strong perceptual calibration and localized explanations, making it suitable as a robust loss function for computer vision tasks.