Science

As generative AI rapidly evolves, a novel continual learning framework is needed to reliably identify synthetic images and combat the spread of misinformation.

A new study reveals the surprising effectiveness of transfer learning for image classification tasks using datasets sourced from Bangladesh.

Researchers have developed a novel method combining graph neural networks and transformers to more accurately identify communities within complex social networks.

New research reveals that current evaluations of graph neural networks’ ability to handle missing data are misleading, potentially overstating their robustness in real-world scenarios.

Researchers have developed a sophisticated system that combines the power of deep learning and artificial intelligence to reliably identify manipulated images.

Researchers have developed a real-time system to detect voice conversion deepfakes, aiming to safeguard live communication from malicious impersonation.

Researchers have developed a novel deep learning model that leverages graph neural networks to dynamically capture complex relationships in traffic patterns, promising more accurate and reliable forecasts.

A new adaptive deep learning algorithm demonstrates high accuracy in solving highly oscillatory Fredholm integral equations, a notoriously difficult problem in applied mathematics.

New research explores how to detect and prevent ‘hallucinations’ – incorrect tool selections – in AI agents powered by large language models, ensuring more reliable automated workflows.

New research tackles the problem of ‘reward hacking’ in complex AI systems, enabling more robust and accurate reasoning capabilities.