Distributed computing has become a common approach for large-scale computation tasks
due to benefits such as high reliability, scalability, computation speed, and cost …

We consider the problem of massive matrix multiplication, which underlies many data
analytic applications, in a large-scale distributed system comprising a group of worker …

Faced with saturation of Moore's law and increasing size and dimension of data, system
designers have increasingly resorted to parallel and distributed computing to reduce …

We provide novel coded computation strategies for distributed matrix-matrix products that
outperform the recent “Polynomial code” constructions in recovery threshold, ie, the required …

Large matrix multiplications are central to large-scale machine learning applications. These
operations are often carried out on a distributed computing platform with a master server and …

In recent years, electroencephalogram (EEG) e-motion recognition has been becoming an
emerging field in artificial intelligence area, which can reflect the relation between emotional …

The goal of coded distributed computation is to efficiently distribute a computation task, such
as matrix multiplication, N-linear computation, or multivariate polynomial evaluation, across …

We study the numerical stability of polynomial based encoding methods, which has
emerged to be a powerful class of techniques for providing straggler and fault tolerance in …

Machine learning models are becoming the primary work-horses for many applications.
Services deploy models through prediction serving systems that take in queries and return …

A secure multi-party batch matrix multiplication problem (SMBMM) is considered, where the
goal is to allow a master to efficiently compute the pairwise products of two batches of …