Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past ...

Abstract: Online learning is a well established learning paradigm which has both theoretical and practical appeals. The goal of online learning is to make a sequence of accurate predictions given ...

Abstract: In this monograph, the authors present an introduction to the framework of variational autoencoders (VAEs) that provides a principled method for jointly learning deep latent-variable models ...

Abstract: Motivated by diverse secure requirements of multiuser in unmanned aerial vehicle (UAV) systems, we propose a collaborative secret and covert transmission method for multi-antenna ground ...

Abstract: Large language models (LLMs) have garnered unprecedented advancements across diverse fields, ranging from natural language processing to computer vision and beyond. The prowess of LLMs is ...

Abstract: This paper proposes a novel distributionally robust energy and reserve dispatch model with distributed renewable predictions. Through leveraging the prediction information from both the ...

Abstract: This paper presents a hybrid Energy Storage System (ESS) for DC microgrids, highlighting its potential for supporting future grid functions with high Renewable Energy Sources (RESs) ...

Abstract: Velostat is a conductive material that can be used as sensing media for robotic tactile perception. However, the performance of the Velostat-based sensors is suppressed by its intrinsic ...

Abstract: The adoption of voluntary environmental standards has emerged as a promising approach to coping with climate change and achieving sustainable development. While prior research has ...

Abstract: This paper explores the integration of Artificial Intelligence into 6G networks, focusing on optimizing communication, resource allocation, and enhancing security. As communication systems ...

Abstract: Due to the wide existence of unlabeled graph-structured data (e.g. molecular structures), the graph-level clustering has recently attracted increasing attention, whose goal is to divide the ...

Abstract: Deep learning offers efficient solutions for drug-target interaction prediction, but current methods often fail to capture the full complexity of multi-modal data (i.e. sequence, graphs, and ...