Loss functions and constraints improve sea surface height prediction

In order to understand currents, tides and other ocean dynamics, scientists need to accurately capture sea surface height, or ...
IEEE

Joint Client and Cross-Client Edge Selection for Cost-Efficient Federated Learning of Graph Convolutional Networks

Abstract: Graph-structured data applications promote the development of Graph Neural Networks (GNN) in recent years. Due to privacy concerns, collecting graph data stored in massive client devices for ...
IEEE

Graph Convolutional Mixture-of-Experts Learner Network for Long-Tailed Domain Generalization

Abstract: The goal of single domain generalization is to use data from a single domain (source domain) to train a model, which is then deployed over several unknown domains for testing (target domains ...

WiMi Releases Next-Generation Quantum Convolutional Neural Network Technology for Multi-Channel Supervised Learning

Beijing, Jan. 05, 2026 (GLOBE NEWSWIRE) -- WiMi Releases Next-Generation Quantum Convolutional Neural Network Technology for Multi-Channel Supervised Learning ...