Accelerating Stochastic Gradient Descent using Predictive Variance Reduction

800 indexed citations

Abstract

loading...

About

This paper, published in 2013, received 800 indexed citations. Written by Rie Johnson and Tong Zhang covering the research area of Artificial Intelligence and Computational Mechanics. It is primarily cited by scholars working on Artificial Intelligence (663 citations), Computational Mechanics (393 citations) and Computer Vision and Pattern Recognition (143 citations). Published in Rare & Special e-Zone (The Hong Kong University of Science and Technology).

In The Last Decade

doi.org/w5632638 →

Countries where authors are citing Accelerating Stochastic Gradient Descent using Predictive Variance Reduction

Specialization
Citations

This map shows the geographic impact of Accelerating Stochastic Gradient Descent using Predictive Variance Reduction. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Accelerating Stochastic Gradient Descent using Predictive Variance Reduction with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Accelerating Stochastic Gradient Descent using Predictive Variance Reduction more than expected).

Fields of papers citing Accelerating Stochastic Gradient Descent using Predictive Variance Reduction

Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Accelerating Stochastic Gradient Descent using Predictive Variance Reduction. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Accelerating Stochastic Gradient Descent using Predictive Variance Reduction.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

This paper is also available at doi.org/w5632638.

Explore hit-papers with similar magnitude of impact

Rankless by CCL
2026