Measuring Performance of Big Learning Workloads

Abstract

Problem: Over 1,000 papers are published each year in Machine Learning. Most are empirical studies and few (if any) provide enough detail to reproduce the results. Complexity of systems begets complexity in metrics - often partially reported. Slows adoption by DoD of new advances in Machine Learning. Solution: Facilitate consistent research comparisons and advancements of Big Learning systems by providing sound reproducible ways to measure and report performance.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2017
Accession Number
AD1088219

Entities

People

  • Scott McMillan

Organizations

  • Carnegie Mellon University

Tags

DTIC Thesaurus Topics

  • Big Data
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  • Machine Learning
  • Materials
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Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Neural Network Machine Learning.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks