22 citations · 63 across the 13 of their papers we have counts for
4 papers · 1 filter
Nimble GNN Embedding with Tensor-Train Decomposition
Chunxing Yin, Da Zheng, Israt Nisa +3
This paper describes a new method for representing embedding tables of graph neural networks (GNNs) more compactly via tensor-train (TT) decomposition. We consider the scenario whe…
SUSTain: Scalable Unsupervised Scoring for Tensors and its Application to Phenotyping
Ioakeim Perros, Evangelos E. Papalexakis, Haesun Park +5
This paper presents a new method, which we call SUSTain, that extends real-valued matrix and tensor factorizations to data where values are integers. Such data are common when the…
SPARTan: Scalable PARAFAC2 for Large & Sparse Data
Ioakeim Perros, Evangelos E. Papalexakis, Fei Wang +4
In exploratory tensor mining, a common problem is how to analyze a set of variables across a set of subjects whose observations do not align naturally. For example, when modeling m…
Sparse Hierarchical Tucker Factorization and its Application to Healthcare
Ioakeim Perros, Robert Chen, Richard Vuduc +1
We propose a new tensor factorization method, called the Sparse Hierarchical-Tucker (Sparse H-Tucker), for sparse and high-order data tensors. Sparse H-Tucker is inspired by its na…