no code implementations • 25 Oct 2024 • Zelin Zang, Yuhao Wang, Jinlin Wu, Hong Liu, Yue Shen, Stan. Z Li, Zhen Lei
DMT-HI enhances DR accuracy by leveraging hyperbolic embeddings to represent the hierarchical nature of data, while also improving interpretability by explicitly linking input data, embedding outcomes, and key features through the MOE structure.
1 code implementation • 10 Sep 2023 • Zelin Zang, Hao Luo, Kai Wang, Panpan Zhang, Fan Wang, Stan. Z Li, Yang You
With the help of iterative training of the semantic encoder and diffusion model, DiffAug improves the representation ability in an uninterrupted and unsupervised manner.
Ranked #1 on Data Augmentation on GA1457
4 code implementations • 4 Oct 2021 • Zhangyang Gao, Haitao Lin, Cheng Tan, Lirong Wu, Stan. Z Li
\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
no code implementations • 28 Dec 2020 • Zhangyang Gao, Haitao Lin, Stan. Z Li
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.