22 citations · 35 across the 5 of their papers we have counts for
5 papers
Partition Speeds Up Learning Implicit Neural Representations Based on Exponential-Increase Hypothesis
Ke Liu, Feng Liu, Haishuai Wang +3
(INRs) aim to learn a (i.e., a neural network) to represent an image, where the input and output of the fu…
Homophily-enhanced Structure Learning for Graph Clustering
Ming Gu, Gaoming Yang, Sheng Zhou +5
Graph clustering is a fundamental task in graph analysis, and recent advances in utilizing graph neural networks (GNNs) have shown impressive results. Despite the success of existi…
Semi-Supervised Hypothesis Transfer for Source-Free Domain Adaptation
Ning Ma, Jiajun Bu, Lixian Lu +4
Domain Adaptation has been widely used to deal with the distribution shift in vision, language, multimedia etc. Most domain adaptation methods learn domain-invariant features with…
Uncertainty-Guided Mixup for Semi-Supervised Domain Adaptation without Source Data
Ning Ma, Jiajun Bu, Zhen Zhang +1
Present domain adaptation methods usually perform explicit representation alignment by simultaneously accessing the source data and target data. However, the source data are not al…
Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification
Ning Ma, Jiajun Bu, Jieyu Yang +5
Graph classification aims to extract accurate information from graph-structured data for classification and is becoming more and more important in graph learning community. Althoug…