Untangling the Signals: A New Approach to Overfitting in Brain Data

Researchers have developed a novel method to improve the reliability of brain-computer interfaces by addressing a hidden cause of performance decline in deep learning models.

Researchers have developed a novel method to improve the reliability of brain-computer interfaces by addressing a hidden cause of performance decline in deep learning models.

A new review highlights the limitations of current methods for evaluating graph generative models, revealing a need for benchmarks that go beyond simple statistical comparisons.

A new review assesses the power of artificial intelligence to improve seasonal precipitation forecasting across the diverse landscapes of South America.

Researchers have developed a novel framework that actively seeks out anomalous patterns to identify deepfake videos, even those never seen before.

New research explores whether large language models can accurately simulate how consumers perceive and anticipate changes in pricing, offering insights into economic modeling and the potential biases of AI.

Researchers have created a benchmark dataset and automated method to identify common performance issues within computer vision models, making optimization more accessible.

A new study reveals that common text embedding techniques struggle to accurately gauge market sentiment from limited financial news data.
New research exposes how sophisticated price manipulation and network routing are exploiting global aluminium trade to obscure illicit financial flows, bypassing traditional smuggling methods.

A new review examines the growing, yet still nascent, field of using artificial intelligence to automate and improve cybersecurity’s crucial red-teaming exercises.

A new analysis reveals how decisions made by AI developers and platforms are predictably linked to the growing problem of misused video deepfakes, particularly non-consensual intimate imagery.