Researchers have developed a novel deep learning framework that leverages the complex interplay of fractal patterns and chaotic dynamics to improve the accuracy of financial volatility predictions.
New research details a framework for simulating how advanced AI systems could inadvertently trigger or exacerbate psychological vulnerabilities in users.
Researchers have developed a multi-period learning framework to improve the accuracy and efficiency of financial forecasting, tackling the challenges of redundant data and complex model design.
As AI systems gain more autonomy, ensuring their consistent performance in unpredictable real-world scenarios is paramount.
Researchers have developed a collection of powerful language models specifically adapted to understand and process complex financial data and tasks.
New research reveals that the post-crisis drive to consolidate financial institutions inadvertently increased systemic risk by creating more interconnected and vulnerable networks.
A new approach leverages tensor networks to accurately forecast the long-term behavior of complex, chaotic dynamics.
A new approach leverages federated learning and explainable AI to identify financial risks across U.S. states without sharing sensitive data.
Researchers have developed a system that allows large language models to dynamically improve their defenses against evolving adversarial attacks.
A new deep learning system is now operational, using data from multiple satellites to identify and quantify methane point sources across the globe.