89 citations · 430 across the 37 of their papers we have counts for
7 papers · 1 filter
A Graph Sufficiency Perspective for Neural Networks
Cencheng Shen, Yuexiao Dong
This paper analyzes neural networks through graph variables and statistical sufficiency. We interpret neural network layers as graph-based transformations, where neurons act as pai…
Graph Neural Networks Powered by Encoder Embedding for Improved Node Learning
Shiyu Chen, Cencheng Shen, Youngser Park +1
Graph neural networks (GNNs) have emerged as a powerful framework for a wide range of node-level graph learning tasks. However, their performance typically depends on random or min…
Efficient Graph Encoder Embedding for Large Sparse Graphs in Python
Xihan Qin, Cencheng Shen
Graph is a ubiquitous representation of data in various research fields, and graph embedding is a prevalent machine learning technique for capturing key features and generating fix…
Fast and Scalable Multi-Kernel Encoder Classifier
Cencheng Shen
This paper introduces a new kernel-based classifier by viewing kernel matrices as generalized graphs and leveraging recent progress in graph embedding techniques. The proposed meth…
One-Hot Graph Encoder Embedding
Cencheng Shen, Qizhe Wang, Carey E. Priebe
In this paper we propose a lightning fast graph embedding method called one-hot graph encoder embedding. It has a linear computational complexity and the capacity to process billio…
Random Forests for Adaptive Nearest Neighbor Estimation of Information-Theoretic Quantities
Ronan Perry, Ronak Mehta, Richard Guo +6
Information-theoretic quantities, such as conditional entropy and mutual information, are critical data summaries for quantifying uncertainty. Current widely used approaches for co…
Sparse Representation Classification via Screening for Graphs
Cencheng Shen, Li Chen, Yuexiao Dong +1
The sparse representation classifier (SRC) is shown to work well for image recognition problems that satisfy a subspace assumption. In this paper we propose a new implementation of…