3 citations · 3 across the 5 of their papers we have counts for
5 papers
Semi-Implicit Denoising Diffusion Models (SIDDMs)
Yanwu Xu, Mingming Gong, Shaoan Xie +4
Despite the proliferation of generative models, achieving fast sampling during inference without compromising sample diversity and quality remains challenging. Existing models such…
Metric Distribution to Vector: Constructing Data Representation via Broad-Scale Discrepancies
Xue Liu, Dan Sun, Xiaobo Cao +2
Graph embedding provides a feasible methodology to conduct pattern classification for graph-structured data by mapping each data into the vectorial space. Various pioneering works…
Temporal Link Prediction via Adjusted Sigmoid Function and 2-Simplex Sructure
Ruizhi Zhang, Qiaozi Wang, Qiming Yang +1
Temporal network link prediction is an important task in the field of network science, and has a wide range of applications in practical scenarios. Revealing the evolutionary mecha…
A Graph Data Augmentation Strategy with Entropy Preservation
Xue Liu, Dan Sun, Wei Wei
The Graph Convolutional Networks (GCN) proposed by Kipf and Welling is an effective model for semi-supervised learning, but faces the obstacle of over-smoothing, which will weaken…
Graph Classification Based on Skeleton and Component Features
Xue Liu, Wei Wei, Xiangnan Feng +2
Most existing popular methods for learning graph embedding only consider fixed-order global structural features and lack structures hierarchical representation. To address this wea…