Search Results for author:
Found 11 papers, 10 papers with code
Bi-fidelity Variational Auto-encoder for Uncertainty Quantification
An effective algorithm is proposed to maximize the variational lower bound of the HF log-likelihood in the presence of limited HF data, resulting in the synthesis of HF realizations with a reduced computational cost.
Sampling-Based Decomposition Algorithms for Arbitrary Tensor Networks
We show how to develop sampling-based alternating least squares (ALS) algorithms for decomposition of tensors into any tensor network (TN) format.
More Efficient Sampling for Tensor Decomposition With Worst-Case Guarantees
Recent papers have developed alternating least squares (ALS) methods for CP and tensor ring decomposition with a per-iteration cost which is sublinear in the number of input tensor entries for low-rank decomposition.
Superresolution photoacoustic tomography using random speckle illumination and second order moments
It is therefore much faster than the iterative method used by Idier et al. We also propose a new representation of the imaged object based on Dirac delta expansion functions.
Binary matrix factorization on special purpose hardware
In this work, we focus on the important binary matrix factorization (BMF) problem which has many applications in data mining.
A Sampling-Based Method for Tensor Ring Decomposition
We provide high-probability relative-error guarantees for the sampled least squares problems.
Numerical Analysis Numerical Analysis
Guarantees for the Kronecker Fast Johnson-Lindenstrauss Transform Using a Coherence and Sampling Argument
In the recent paper [Jin, Kolda & Ward, arXiv:1909. 04801], it is proved that the Kronecker fast Johnson-Lindenstrauss transform (KFJLT) is, in fact, a Johnson-Lindenstrauss transform, which had previously only been conjectured.
Numerical Analysis Numerical Analysis
Dynamic Graph Convolutional Networks Using the Tensor M-Product
In recent years, a variety of graph neural networks (GNNs) have been successfully applied for representation learning and prediction on such graphs.
Randomization of Approximate Bilinear Computation for Matrix Multiplication
We present a method for randomizing formulas for bilinear computation of matrix products.
Data Structures and Algorithms Numerical Analysis Numerical Analysis
Fast Randomized Matrix and Tensor Interpolative Decomposition Using CountSketch
We propose a new fast randomized algorithm for interpolative decomposition of matrices which utilizes CountSketch.
Numerical Analysis Numerical Analysis 15-02
Low-Rank Tucker Decomposition of Large Tensors Using TensorSketch
We propose two randomized algorithms for low-rank Tucker decomposition of tensors.
