10 citations · 23 across the 7 of their papers we have counts for
8 papers
Knowledgebra: An Algebraic Learning Framework for Knowledge Graph
Tong Yang, Yifei Wang, Long Sha +2
Knowledge graph (KG) representation learning aims to encode entities and relations into dense continuous vector spaces such that knowledge contained in a dataset could be consisten…
Algebraic Learning: Towards Interpretable Information Modeling
Tong Owen Yang
Along with the proliferation of digital data collected using sensor technologies and a boost of computing power, Deep Learning (DL) based approaches have drawn enormous attention i…
Spectral Clustering with Smooth Tiny Clusters
Hengrui Wang, Yubo Zhang, Mingzhi Chen +1
Spectral clustering is one of the most prominent clustering approaches. The distance-based similarity is the most widely used method for spectral clustering. However, people have a…
Variance Regularization for Accelerating Stochastic Optimization
Tong Yang, Long Sha, Pengyu Hong
While nowadays most gradient-based optimization methods focus on exploring the high-dimensional geometric features, the random error accumulated in a stochastic version of any algo…
A Deep Learning Approach for COVID-19 Trend Prediction
Tong Yang, Long Sha, Justin Li +1
In this work, we developed a deep learning model-based approach to forecast the spreading trend of SARS-CoV-2 in the United States. We implemented the designed model using the Unit…
NagE: Non-Abelian Group Embedding for Knowledge Graphs
Tong Yang, Long Sha, Pengyu Hong
We demonstrated the existence of a group algebraic structure hidden in relational knowledge embedding problems, which suggests that a group-based embedding framework is essential f…