Multi-stage dual-domain progressive network enhances sparse-view CT reconstruction

A research team led by Professor Wang Hongzhi from the Hefei Institute of Physical Science of the Chinese Academy of Sciences has developed a multi-stage, dual-domain, progressive network with ...
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 ...
IEEE

Rough-Fuzzy Graph Learning Domain Adaptation for Fake News Detection

Abstract: The widespread dissemination of fake news across the internet has profound detrimental consequences for society, governments, and citizens. To address this pressing issue, numerous machine ...
GitHub

Aggregate to Adapt: Node-Centric Aggregation for Multi-Source-Free Graph Domain Adaptation (WWW-2025).

This is a PyTorch implementation of the GraphATA algorithm, which tries to address the multi-source domain adaptation problem without accessing the labelled source graph. Unlike previous multi-source ...
San Francisco Fed

Supply- and Demand-Driven PCE Inflation

Supply- and Demand-Driven PCE Inflation updates data on the contributions to personal consumption expenditures (PCE) inflation from supply-driven versus demand-driven components. This tool is intended ...
GitHub

Towards Unsupervised Open-Set Graph Domain Adaptation via Dual Reprogramming (NeurIPS 2025)

This is a PyTorch implementation of the GraphRTA algorithm, which tries to address the open-set graph domain adaptation problem, where the goal is to not only correctly classify target nodes into the ...