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

Contrastive Graph Modeling for Cross-Domain Few-Shot Medical Image Segmentation

Abstract: Cross-domain few-shot medical image segmentation (CD-FSMIS) offers a promising and data-efficient solution for medical applications where annotations are severely scarce and multimodal ...
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 ...
Frontiers

DWGCN: distance-weighted graph convolutional network for robust spatial domain identification in spatial transcriptomics

Background: Graph Convolutional Networks (GCNs) are widely applied for spatial domain identification in spatial transcriptomics (ST), where node representations are learned by aggregating information ...
GitHub

DeGEM: Out-of-Distribution Node Detection by Graph Energy-based Model

Official implementation for ICLR'25 paper ''Decoupled Graph Energy-based Model for Node Out-of-Distribution Detection on Heterophilic Graphs''[arxiv], by Yuhan Chen*, Yihong Luo*, Yifan Song, Pengwen ...