Sparse identification of nonlinear dynamical systems is an important project, directly addressing the physics community’s long-standing goal of data-driven discovery. Although many effective methods ...

The behavior of language models is influenced by the prior context provided in prompts. Depending on whether you pick synthesis or shake, the next row looks very different — Vishal Misra Contextual ...

This R-package contains various functions that extend the functionality of the Matrix package for using sparse matrices. Some of the functions are very general, while other are highly specific for ...

Abstract: We present a new Gaussian process (GP) inference algorithm, called online sparse matrix Gaussian processes (OSMGP), and demonstrate its merits by applying it to the problems of head pose ...

Abstract: Sparse matrix–matrix multiplication (SpMM) is an important kernel in multiple areas, e.g., data analytics and machine learning. Due to the low on-chip memory requirement, the consistent data ...

Sparse data can impact the effectiveness of machine learning models. As students and experts alike experiment with diverse datasets, sparse data poses a challenge. The Leeds Master’s in Business ...

Non-negative matrix factorization, which decomposes the input non-negative matrix into product of two non-negative matrices, has been widely used in the neuroimaging field due to its flexible ...