Science

Researchers have developed a novel model, Dualformer, that analyzes time series data in both the time and frequency domains to significantly improve long-term prediction accuracy.

Researchers have developed a novel generative model to create network traffic that more accurately reflects real-world patterns, improving the effectiveness of security testing and training.

Researchers are applying principles from probability theory to reshape how large language models generate text, aiming for more efficient and reliable reasoning.

Researchers have developed a framework that equips artificial intelligence with the ability to strategically construct rebuttals, mimicking human reasoning in academic discourse.

A new technique adaptively optimizes the verification process during language model inference, significantly boosting speed without sacrificing quality.

Ecologists can now build custom machine learning models for image-based wildlife monitoring without needing extensive coding expertise or massive datasets.

Researchers are pioneering methods to publicly validate the calculations of powerful financial artificial intelligence models without revealing sensitive data.

A new framework intelligently selects and incorporates relevant evidence to improve the accuracy of multimodal fake news detection.

New research shows machine learning models analyzing brain scans can significantly improve the diagnosis of Alzheimer’s disease even before the onset of typical memory loss.

A new training regime leverages counterfactual reasoning to build machine learning models that are not only more robust but also provide clearer, more actionable explanations for their decisions.