Web: http://arxiv.org/abs/2207.04198

Sept. 23, 2022, 1:12 a.m. | Xin Cao

cs.LG updates on arXiv.org arxiv.org

A new gradient-based optimization approach by automatically scheduling the
learning rate has been proposed recently, which is called Binary Forward
Exploration (BFE). The Adaptive version of BFE has also been discussed
thereafter. In this paper, the improved algorithms based on them will be
investigated, in order to optimize the efficiency and robustness of the new
methodology. This improved approach provides a new perspective to scheduling
the update of learning rate and will be compared with the stochastic gradient
descent, aka …

arxiv binary exploration optimization rate scheduling stochastic

More from arxiv.org / cs.LG updates on arXiv.org

Postdoctoral Fellow: ML for autonomous materials discovery

@ Lawrence Berkeley National Lab | Berkeley, CA

Research Scientists

@ ODU Research Foundation | Norfolk, Virginia

Embedded Systems Engineer (Robotics)

@ Neo Cybernetica | Bedford, New Hampshire

2023 Luis J. Alvarez and Admiral Grace M. Hopper Postdoc Fellowship in Computing Sciences

@ Lawrence Berkeley National Lab | San Francisco, CA

Senior Manager Data Scientist

@ NAV | Remote, US

Senior AI Research Scientist

@ Earth Species Project | Remote anywhere

Research Fellow- Center for Security and Emerging Technology (Multiple Opportunities)

@ University of California Davis | Washington, DC

Staff Fellow - Data Scientist

@ U.S. FDA/Center for Devices and Radiological Health | Silver Spring, Maryland

Staff Fellow - Senior Data Engineer

@ U.S. FDA/Center for Devices and Radiological Health | Silver Spring, Maryland

Research Engineer - VFX, Neural Compositing

@ Flawless | Los Angeles, California, United States

[Job-TB] Senior Data Engineer

@ CI&T | Brazil

Data Analytics Engineer

@ The Fork | Paris, France