Science

This review meticulously unpacks the backpropagation process within transformer networks, offering a clear pathway to understanding and optimizing these powerful architectures.

A critical look at how we evaluate deep learning models reveals that inconsistent practices are obscuring real progress in time series forecasting.

Researchers have developed a novel hybrid neural network, optimized by a bio-inspired algorithm, to forecast gold prices with promising results.

New research reveals how third-party platforms can dynamically adjust service fees to maximize learning and minimize regret in the face of uncertain demand.

A new approach uses the power of generative AI to create realistic defect images, significantly improving the accuracy and efficiency of industrial inspection systems.

Researchers have developed a novel graph neural network that adapts to the unique characteristics of complex networks, dramatically improving its ability to identify subtle anomalies.

A new analysis demonstrates that incorporating image data into valuation models can significantly improve price predictions, especially for previously unseen artworks.

Researchers are exploring the potential of artificial intelligence to dynamically identify and leverage profitable investment factors, moving beyond traditional, static strategies.

This review explores how reinforcement learning is being used to minimize delays and maximize the value of information in increasingly congested wireless environments.

A new generative framework, Dictionary-Transform GANs, leverages linear operators and sparse coding to address the notorious instability problems plaguing traditional generative adversarial networks.