Science

Researchers have developed a novel framework, Belief Net, for learning the underlying dynamics of sequential data with improved speed and accuracy.

A novel denoising technique, DenoGrad, leverages deep learning to refine data and boost the performance of AI models where understanding how decisions are made is critical.

A new review challenges the prevailing narrative around machine learning privacy risks, suggesting current concerns may be exaggerated and hindering innovation.

Researchers are developing a formal method to identify how AI systems inadvertently reveal sensitive information through their decisions.

A new deep learning approach unlocks chemical abundances from stellar spectra using unsupervised learning, paving the way for automated identification of rare stars.

A new approach combines generative networks with differential privacy to create highly realistic datasets without revealing sensitive information.

New research reveals that active gradient inversion attacks, designed to steal data in collaborative learning, aren’t as hidden as attackers believe.

A new framework, TermGPT, addresses the challenges of ambiguous and sparse data in legal and financial texts to improve large language models’ understanding of specialized vocabulary.

A new approach leverages principles of kinematics to refine neural network predictions, aiming for more stable and accurate long-term stock market forecasts.

A new decision support system leverages artificial intelligence to forecast content virality and market growth with unprecedented accuracy.