Search Results for author:
Found 3 papers, 1 papers with code
Boosting Unsupervised Contrastive Learning Using Diffusion-Based Data Augmentation From Scratch
When applied to biological data, DiffAug improves performance by up to 10. 1%, with an average improvement of 5. 8%.
Git: Clustering Based on Graph of Intensity Topology
\textbf{A}ccuracy, \textbf{R}obustness to noises and scales, \textbf{I}nterpretability, \textbf{S}peed, and \textbf{E}asy to use (ARISE) are crucial requirements of a good clustering algorithm.
Ranked #1 on
Clustering Algorithms Evaluation
on Fashion-MNIST
LookHops: light multi-order convolution and pooling for graph classification
Convolution and pooling are the key operations to learn hierarchical representation for graph classification, where more expressive $k$-order($k>1$) method requires more computation cost, limiting the further applications.
