Neural network-based branch prediction techniques represent a significant advancement in processor architecture, where machine learning models replace traditional, heuristic-based mechanisms to ...

The spatio-temporal evolution of wall-bounded turbulence is characterized by high nonlinearity, multi-scale dynamics, and chaotic nature, making its accurate prediction a significant challenge for ...

The digital economy is increasingly driven by intelligent systems that process enormous volumes of behavioral information. Platforms across entertainment, finance, and iGaming rely on machine learning ...

Researchers combine numerical modeling with neural networks to show how nanodiamond aggregation, magnetic fields, and surface ...

Increasingly, AI models are able make short-term weather forecasts with surprising accuracy. But neural networks only predict based on patterns from the past—what happens when the weather does ...

Accurate stock trend forecasting is a central challenge in financial economics due to the highly nonlinear and interdependent nature of market dynamics. Traditional statistical and machine learning ...

All of the baseline models achieve excellent performance in predicting high speed while performing extremely poorly in predicting lower ones. Specifically, even if the prediction horizon is 60 mins, a ...

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...

Transformer in Artificial Intelligence powers over 90% of modern AI models today. Introduced by researchers at Google in 2017, the Transformer architecture changed machine learning forever. It helps ...