Science

This review explores the principles and applications of attention mechanisms, a core component in modern neural networks that allows models to prioritize relevant information.

A new approach to portfolio management directly links learning objectives to investment decisions, delivering consistently improved performance and resilience.

Researchers have developed a unified approach to deepfake detection that leverages both spatial and frequency domain analysis, achieving state-of-the-art performance and improved robustness.

Researchers have developed a new system that translates the complex decision-making processes of deep neural networks into human-readable logic programs, offering insights into their inner workings.

A new framework integrates incentive design from economic theory with multi-agent reinforcement learning, creating AI systems that prioritize social welfare in complex strategic environments.

New research reveals that companies embracing artificial intelligence are communicating skill requirements with greater clarity in job postings, offering a window into evolving labor market demands.

A novel method leverages feature space analysis and targeted masking to reliably identify images created by artificial intelligence.

A new framework verifies the logic behind AI trading decisions to combat market manipulation and improve performance across diverse conditions.

This review examines the diverse strategies used to replicate financial indexes, offering a critical comparison of traditional and modern approaches.

As artificial intelligence writing tools become increasingly sophisticated, accurately identifying AI-generated text is a growing challenge.