Web: http://arxiv.org/abs/2209.07326

Sept. 16, 2022, 1:15 a.m. | Andrea Gesmundo

cs.CV updates on arXiv.org arxiv.org

The traditional Machine Learning (ML) methodology requires to fragment the
development and experimental process into disconnected iterations whose
feedback is used to guide design or tuning choices. This methodology has
multiple efficiency and scalability disadvantages, such as leading to spend
significant resources into the creation of multiple trial models that do not
contribute to the final solution.The presented work is based on the intuition
that defining ML models as modular and extensible artefacts allows to introduce
a novel ML development …

arxiv continual development methodology scale systems

More from arxiv.org / cs.CV 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