Neural networks revolutionized machine learning for classical computers: self-driving cars, language translation and even artificial intelligence software were all made possible. It is no wonder, then ...

The Demonstrated Proprietary Technology for Converting Continuous Data into Quantum-Compatible Energy Maps and Achieves More Than 10x Faster ...

Abstract: Effective cooperation is pivotal in distributed learning for multi-agent systems, where the interplay between the quantity and quality of the machine learning models is crucial. This paper ...

Abstract: Federated learning is an important distributed machine learning paradigm. This study proposes a privacy-preserving data augmentation model for federated learning of heterogeneous data, which ...

Most of the latest studies on detection models for DoS or DDoS have been applied in general networks. Therefore, no dataset of DoS or DDoS in electric vehicle (EV) charging infrastructure exists. In ...

Geographical Gaussian Process Regression (GGPR): A Spatial Machine Learning Model Based on Spatial Similarity: defined as A-Xing Zhu et al. (2018): "The more similar geographic configurations of two ...

Anyone following the AI space is by now familiar with lofty claims that AI models will soon be better than humans at ...

Latest AI advancements are leaning toward the development of tabular foundational models (TFMs). These are extremely useful.

Nvidia has folded its PhysicsNeMo physics-AI libraries and a set of GPU math libraries into the NVIDIA Agent Toolkit, making ...

AI weather models have crossed from research into routine operations at major forecasting centers. The European Centre for ...

Researchers from the National University of Singapore (NUS) have developed artificial intelligence (AI) methods that learn the large-scale behavior of complex materials from microscopic data. By ...