Researchers have developed a novel framework that allows graph neural networks to learn new tasks without losing previously acquired knowledge, while also safeguarding data privacy.
This review examines how incorporating curvature information and adaptive techniques can significantly improve the training of large neural networks.
New research reveals that normalizing order flow data by market capitalization-rather than trading volume-significantly improves the ability to identify genuine trading signals.
New research suggests that trade isn’t solely driven by information gaps, but can emerge from the computational constraints and strategic choices of even highly capable artificial intelligence.
New research shows large language models can infer the subtle forces of dealer hedging, even when stripped of identifying data, suggesting a deeper understanding of market mechanics.
A new approach leverages the power of network analysis to identify coordinated trading activity among corporate insiders, revealing patterns hidden from traditional surveillance.
A new approach integrates geostatistical principles into transformer networks to improve the accuracy and efficiency of predicting events across space and time.
Researchers are leveraging deep learning to optimize power grid control, enabling faster and more reliable responses to changing demand and conditions.
As deepfake technology advances, a new ethical framework is needed to address the potential for misinformation and harm, and this paper proposes Islamic principles as a uniquely effective solution.
This review explores how artificial intelligence can anticipate future radar returns, enabling autonomous vessels to navigate more effectively in complex maritime environments.