RandNLA: randomized numerical linear algebra Page 1 80 COMMUNICATIONS OF THE ACM
| JUNE 2016 | VOL. 59 | NO. 6 review articles DOI:10.1145/2842602 Randomization offers new …

Random matrices now play a role in many areas of theoretical, applied, and computational
mathematics. Therefore, it is desirable to have tools for studying random matrices that are …

We present an efficient coresets-based neural network compression algorithm that sparsifies
the parameters of a trained fully-connected neural network in a manner that provably …

Previous work showed empirically that large neural networks can be significantly reduced in
size while preserving their accuracy. Model compression became a central research topic …

The field of quantum machine learning (QML) produces many proposals for attaining
quantum speedups for tasks in machine learning and data analysis. Such speedups can …

Nota Bene: This manuscript has not yet reached its final form. In particular, I have not had
the opportunity to check all the details carefully and to polish the writing so that it reflects the …

Model compression is crucial for the deployment of neural networks on devices with limited
computational and memory resources. Many different methods show comparable accuracy …

We study the matrix completion problem: an underlying m× n matrix P is low rank, with
incoherent singular vectors, and a random m× n matrix A is equal to P on a (uniformly) …

Many real-world data sets are sparse or almost sparse. One method to measure this for a
matrix $ A\in\mathbb {R}^{n\times n} $ is the\emph {numerical sparsity}, denoted $\mathsf …

In this paper, we obtain improved running times for regression and top eigenvector
computation for numerically sparse matrices. Given a data matrix $\mat {A}\in\R^{n\times d} …