Abstract: As an emerging machine learning task, high-dimensional hyperparameter optimization (HO) aims at enhancing traditional deep learning models by simultaneously optimizing the neural networks’ ...

Abstract: Time series classification is a prevalent research topic with wide applications, and Imbalanced Time Series Classification (ITSC) has emerged with high importance due to the nature of ...

In this tutorial, we shift from traditional prompt crafting to a more systematic, programmable approach by treating prompts as tunable parameters rather than static text. Instead of guessing which ...

This project implements a state-of-the-art CNN architecture for CIFAR-10 image classification, achieving 88.82% accuracy through systematic hyperparameter optimization. The implementation includes GPU ...