Science

A new study pits traditional machine learning approaches against neuro-symbolic methods in the task of classifying gender based on blog post content.

Researchers have developed a self-supervised learning method that leverages the frequency of user actions to build more accurate models of online behavior.

New research reveals how the frequency of information traveling across user networks impacts the effectiveness of recommendation algorithms.

A new deep-learning framework is helping scientists identify and analyze subtle distortions in gravitational wave signals caused by the bending of spacetime, as demonstrated by a re-analysis of the GW231123 event.

As AI image generation becomes more sophisticated, existing detection methods are being challenged by subtle, localized manipulations like inpainting.

Researchers are exploring techniques to dramatically accelerate the decision-making process of language-based AI agents by predicting and pre-calculating potential actions.

A new co-training framework uses an adversarial approach to refine the reasoning process of large language models, leading to more accurate and efficient problem-solving.

A novel framework reconciles the benefits of model-independent pricing with the practical demands of implementation for complex financial instruments.

This in-depth review charts the progression of reranking techniques, from early algorithmic approaches to the transformative impact of deep learning and large language models.

This review examines how leveraging graph-based approaches can improve the discovery of relevant research papers and enhance academic assistance tools.