Machine learning plays a role in many deployed decision systems, often in ways that are
difficult or impossible to understand by human stakeholders. Explaining, in a human …

The advent of deep learning has brought a revolutionary transformation to image denoising
techniques. However, the persistent challenge of acquiring noise-clean pairs for supervised …

Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and
connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger …

This work provides a novel rare-earth-based catalyst of N-regulated lanthanum phosphate
(N-LaPO 4/C) and the corresponding rational measurement of catalytic activity for the …

Textual backdoor attacks pose significant security threats. Current detection approaches,
typically relying on intermediate feature representation or reconstructing potential triggers …

Self-supervised representation learning methods have achieved significant success in
computer vision and natural language processing (NLP), where data samples exhibit explicit …

Deep learning-based recommender systems have become an integral part of several online
platforms. However, their black-box nature emphasizes the need for explainable artificial …

Recently, there has been a growing demand for the deployment of Explainable Artificial
Intelligence (XAI) algorithms in real-world applications. However, traditional XAI methods …

The use of centralized training and decentralized execution for value function factorization
demonstrates the potential for addressing cooperative multi-agent reinforcement tasks …

Financial time-series prediction has been an important topic in deep learning, and the
prediction of financial time series is of great importance to investors, commercial banks and …