Science

Researchers have developed a semi-supervised method to reliably identify images created by artificial intelligence, even those generated by unfamiliar models.

Researchers have developed a method to dramatically improve the accuracy of large language models when only limited human-labeled data is available.

A novel framework ensures optimal pricing strategies even when customers actively try to game the system.

A new framework uses reinforcement learning to intelligently select image patches for analysis, dramatically improving the detection of even the most minor anomalies.

New research demonstrates the power of gradient boosting models to accurately forecast Bitcoin volatility, offering insights for traders and investors.

A new framework leverages the power of generative AI and active learning to pinpoint threats, even with limited labeled data.

A new approach leverages sequential data analysis to forecast tourist movement patterns and improve destination management.

A new approach combines the power of machine learning with established algorithmic techniques to tackle complex graph optimization problems.

A new approach to acquiring labeled data uses active learning to optimize costs and improve model performance in forecasting applications.

New research reveals a fundamental flaw in how deep learning models identify image forgeries, stemming from inconsistencies in the frequency spectra of real and synthetic images.