10 citations · 12 across the 4 of their papers we have counts for
4 papers
Unifying gradient regularization for Heterogeneous Graph Neural Networks
Xiao Yang, Xuejiao Zhao, Zhiqi Shen
Heterogeneous Graph Neural Networks (HGNNs) are a class of powerful deep learning methods widely used to learn representations of heterogeneous graphs. Despite the fast development…
Common Diffusion Noise Schedules and Sample Steps are Flawed
Shanchuan Lin, Bingchen Liu, Jiashi Li +1
We discover that common diffusion noise schedules do not enforce the last timestep to have zero signal-to-noise ratio (SNR), and some implementations of diffusion samplers do not s…
PointShuffleNet: Learning Non-Euclidean Features with Homotopy Equivalence and Mutual Information
Linchao He, Mengting Luo, Dejun Zhang +3
Point cloud analysis is still a challenging task due to the disorder and sparsity of samplings of their geometric structures from 3D sensors. In this paper, we introduce the homoto…
Learning non-parametric Markov networks with mutual information
Janne Leppä-aho, Santeri Räisänen, Xiao Yang +1
We propose a method for learning Markov network structures for continuous data without invoking any assumptions about the distribution of the variables. The method makes use of pre…