Technology
Supplementary Materials for " Efficient Test-Time Adaptation for Super-Resolution with Second-Order Degradation and Reconstruction " Contents
Low-complexity single-image super-resolution based on nonnegative neighbor embedding.
Efficient Test-Time Adaptation for Super-Resolution with Second-Order Degradation and Reconstruction Zeshuai Deng 1 Zhuokun Chen
Image super-resolution (SR) aims to learn a mapping from low-resolution (LR) to high-resolution (HR) using paired HR-LR training images.
Unconstrained Dynamic Regret via Sparse Coding
Nonstationarity is prevalent in sequential decision making, which poses a critical challenge to the vast majority of existing approaches developed offline.
Distributed-Order Fractional Graph Operating Network
We introduce the Distributed-order fRActional Graph Operating Network (DRAGON), a novel continuous Graph Neural Network (GNN) framework that incorporates distributed-order fractional calculus.
ebfabf372037aaa4a8d92c9b457ece3e-Paper-Conference.pdf
BRMs thus naturally induce strategic creators' collective behaviors
Title
A common approach to create more expressive GNNs is to change the message passing function of MPNNs. If a GNN is more expressive than MPNNs by adapting the message passing function, we call this non-standard message passing . Examples of this are message passing variants that operate on subgraphs [Frasca et al., 2022, Bevilacqua