9 citations · 51 across the 24 of their papers we have counts for
8 papers · 1 filter
Wasserstein Adversarially Regularized Graph Autoencoder
Huidong Liang, Junbin Gao
This paper introduces Wasserstein Adversarially Regularized Graph Autoencoder (WARGA), an implicit generative algorithm that directly regularizes the latent distribution of node em…
Graph Denoising with Framelet Regularizer
Bingxin Zhou, Ruikun Li, Xuebin Zheng +2
As graph data collected from the real world is merely noise-free, a practical representation of graphs should be robust to noise. Existing research usually focuses on feature smoot…
How Neural Processes Improve Graph Link Prediction
Huidong Liang, Junbin Gao
Link prediction is a fundamental problem in graph data analysis. While most of the literature focuses on transductive link prediction that requires all the graph nodes and majority…
Neural Ordinary Differential Equation Model for Evolutionary Subspace Clustering and Its Applications
Mingyuan Bai, S. T. Boris Choy, Junping Zhang +1
The neural ordinary differential equation (neural ODE) model has attracted increasing attention in time series analysis for its capability to process irregular time steps, i.e., da…
Differentiable Neural Architecture Search with Morphism-based Transformable Backbone Architectures
Renlong Jie, Junbin Gao
This study aims at making the architecture search process more adaptive for one-shot or online training. It is extended from the existing study on differentiable neural architectur…
A Discussion On the Validity of Manifold Learning
Dai Shi, Andi Han, Yi Guo +1
Dimensionality reduction (DR) and manifold learning (ManL) have been applied extensively in many machine learning tasks, including signal processing, speech recognition, and neuroi…