Tabular foundation models are the next major unlock for AI adoption, especially in industries sitting on massive databases of ...

Machine learning is reshaping the way portfolios are built, monitored, and adjusted. Investors are no longer limited to ...

Abstract: Hyperparameter optimization plays a pivotal role in the reliability and generalization of machine-learning models for software quality prediction. This paper presents a comparative ...

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 ...

Hyperparameter tuning is critical to the success of cross-device federated learning applications. Unfortunately, federated networks face issues of scale, heterogeneity, and privacy; addressing these ...

Abstract: Automatic performance tuning (auto-tuning) is widely used to optimize performance-critical applications across many scientific domains by finding the best program variant among many choices.

AI-powered search experiences from Google, Anthropic’s Claude, OpenAI’s ChatGPT, and Perplexity are reshaping how users find information online. Traditional SEO strategies are no longer enough. Brands ...

I've been working with the code and noticed that the current model (RandomForestRegressor) could benefit from hyperparameter tuning. The current setup uses default parameters, which may not be optimal ...

1 Department of Electrical and Computer Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Astana, Kazakhstan 2 School of Information Technology and Systems, University of ...

Rotary Positional Embedding (RoPE) is a widely used technique in Transformers, influenced by the hyperparameter theta (θ). However, the impact of varying *fixed* theta values, especially the trade-off ...