Science

A new framework leverages neural networks to dramatically improve the accuracy and speed of calibrating complex agent-based models for financial market forecasting.

As generative AI rapidly advances, researchers are developing innovative techniques to detect and mitigate the risks of malicious content and reputational damage.

A new approach to graph neural networks focuses on strategically sampling edges to improve fraud detection accuracy and efficiency.

New research demonstrates that artificially generated data can deliver traffic prediction accuracy on par with real-world datasets, offering a path to more private and efficient wireless network management.

New research reveals the counterintuitive effects of information asymmetry and verification costs in machine learning-driven trading environments.

New research details a machine learning framework designed to minimize missed fraudulent transactions in real-time online banking.

New research reveals that basic data normalization consistently outperforms sophisticated machine learning models when predicting investor behavior.

This review examines the Prophet forecasting framework, highlighting its strengths in building reliable and understandable time series models.

A new analysis reveals fundamental limits to self-improvement in current AI systems, demonstrating why true artificial general intelligence remains a distant prospect.

This review introduces a novel neural network framework for efficiently solving multi-period martingale optimal transport problems, accelerating the pricing and risk management of complex financial derivatives.