The unprecedented progress in computational technologies led to a substantial proliferation
of artificial intelligence applications, notably in the era of big data and IoT devices. In the …

Simultaneously considering multiple objectives in machine learning has been a popular
approach for several decades, with various benefits for multi-task learning, the consideration …

Hyperspectral image (HSI) denoising has been regarded as an effective and economical
preprocessing step in data subsequent applications. Recent nonlocal low-rank …

What predicts cross-country differences in the recovery of socioeconomic activity from the
COVID-19 pandemic? To answer this question, we examined how quickly countries' …

We propose a learning framework based on stochastic Bregman iterations, also known as
mirror descent, to train sparse neural networks with an inverse scale space approach. We …

PCB board defect detection is a necessary part of the PCB manufacturing process and
needs to be repeated several times to ensure the quality of the PCB board. However …

As an effective learning paradigm against insufficient training samples, multi-task learning
(MTL) encourages knowledge sharing across multiple related tasks so as to improve the …

This study focuses on optimizing federated learning in heterogeneous data environments.
We implement the FedProx and a baseline algorithm (ie, the FedAvg) with advanced …

We propose a novel strategy for Neural Architecture Search (NAS) based on Bregman
iterations. Starting from a sparse neural network our gradient-based one-shot algorithm …

As a popular channel pruning method for convolutional neural networks (CNNs), network
slimming (NS) has a three-stage process:(1) it trains a CNN with ℓ 1 regularization applied to …