Xi Peng

Learning by Sampling and Compressing: Efficient Graph Representation Learning with Extremely Limited Annotations

2020 · arXiv (Cornell University)

Graph convolution network (GCN) attracts intensive research interest with broad applications. While existing work mainly focused on designing novel GCN architectures for better performance, few of them studied a practical yet challenging problem: How to …

XAI Beyond Classification: Interpretable Neural Clustering

2018 · arXiv (Cornell University)

In this paper, we study two challenging problems in explainable AI (XAI) and data clustering. The first is how to directly design a neural network with inherent interpretability, rather than giving post-hoc explanations of a …

TAR: Neural Logical Reasoning across TBox and ABox

2022 · arXiv (Cornell University)

Many ontologies, i.e., Description Logic (DL) knowledge bases, have been developed to provide rich knowledge about various domains. An ontology consists of an ABox, i.e., assertion axioms between two entities or between a concept and …

Semantic Invariant Multi-view Clustering with Fully Incomplete Information

2023 · arXiv (Cornell University)

Robust multi-view learning with incomplete information has received significant attention due to issues such as incomplete correspondences and incomplete instances that commonly affect real-world multi-view applications. Existing approaches heavily rely on paired samples to realign …

Improve Interpretability of Neural Networks via Sparse Contrastive Coding

2022

Although explainable artificial intelligence (XAI) has achieved remarkable developments in recent years, there are few efforts have been devoted to the following problems, namely, i) how to develop an explainable method that could explain the …