Science

New research moves beyond simply observing transformer behavior to identify which attention heads are causally responsible for specific functionalities.

A new study reveals that artificial intelligence, specifically deep reinforcement learning, is consistently delivering superior results in navigating complex financial markets.

Researchers have launched a live, multi-agent system to rigorously evaluate the performance of artificial intelligence in real-world financial forecasting scenarios.

A novel model combining Neural Prophet and deep neural networks demonstrates improved accuracy in forecasting stock market prices.

A new framework combines the power of generative adversarial networks with logical reasoning to create more consistent and structurally sound generated content.

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.