Jan 1, 2015 · In this paper, we develop two database-inspired techniques for efficient model calibration. Speculative parameter testing applies advanced ...

Abstract. Model calibration is a major challenge faced by the plethora of statistical analytics packages that are increasingly.

In this paper, we develop two database-inspired techniques for efficient model calibration. Speculative parameter testing applies advanced parallel multi-query ...

This paper develops two database-inspired techniques for efficient model calibration and applies the proposed techniques to distributed gradient descent ...

Bibliographic details on Speculative Approximations for Terascale Analytics.

Speculative iteration processing allows concurrent evaluation of multiple parameter configurations in a single pass over the training data—without the ...

Model calibration is a major challenge faced by the plethora of statistical analytics packages that are increasingly used in Big Data applications.

ABSTRACT. Model calibration is a major challenge faced by the plethora of sta- tistical analytics packages that are increasingly used in Big Data.

Apr 25, 2024 · Chengjie Qin, Florin Rusu: Speculative Approximations for Terascale Distributed Gradient Descent Optimization.

Cover page: GLADE-ML: A Database For Big Data Analytics. Article; Peer Reviewed. Speculative Approximations for Terascale Analytics · Qin, Chengjie;; Rusu, ...