While multiple machine learning (ML) algorithms offered similar predictive performance, the cost-effective analysis revealed ...

Researchers from Carnegie Mellon University’s School of Computer Science developed a new approach to bridge this gap between available data and actionable insight, creating personalized models to help ...

The prediction of long-term outcomes in ischemic stroke patients may be useful in treatment decisions. Machine learning techniques are being increasingly adapted for use in the medical field because ...

Finally, in order to improve performance even further, we use deep learning to fine-tune the hyperparameters, particularly our proposed weight-tuning one. We perform some experiments on real-world ...

AI’s growth is limited by poor-quality data, not model size. Human expertise in data curation, decentralized feedback and ...

Current care in multiple sclerosis (MS) primarily relies on infrequently obtained data such as magnetic resonance imaging, clinical laboratory tests or clinical history, resulting in subtle changes ...

From deadly floods in Europe to intensifying tropical cyclones around the world, the climate crisis has made timely and precise forecasting more essential than ever. Yet traditional forecasting ...

What is this book about? Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that ...

One common misstep: assuming bigger models and better benchmarks translate to a better user experience. Accuracy may not be enough. If a system is slow or resistant to correction, it alienates users.

How can artificial intelligence (AI) be used to advance mapping and imaging methods on other planets? This is what a study presented at the 56th Lunar and Planetary Science Conference hopes to ...

Open Molecules 2025, an unprecedented dataset of molecular simulations, has been released to the scientific community, paving ...

data-driven functions enabled by the integration of machine learning (ML) notions across the wireless core and edge infrastructure. In this context, this paper provides a comprehensive tutorial that ...