skip to main content
research-article

Technical Perspective: Query Optimization for Faster Deep CNN Explanations

Published:04 September 2020Publication History
Skip Abstract Section

Abstract

Machine learning (ML) is increasingly used to automate decision making in various domains. In recent years, ML has not only been applied to tasks that use structured input data, but also, tasks that operate on data with less strictly defined structure such as speech, images and videos. Prominent examples are speech recognition for personal assistants or face recognition for boarding airplanes.

References

  1. Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Haichen Shen, Meghan Cowan, Leyuan Wang, Yuwei Hu, Luis Ceze, et al. 2018. TVM: An Automated End-to-End Optimizing Compiler for Deep Learning. OSDI, 578--594. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. HV Jagadish, Francesco Bonchi, Tina Eliassi-Rad, Lise Getoor, Krishna Gummadi, and Julia Stoyanovich. 2019. The Responsibility Challenge for Data. SIGMOD, 412--414. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. Imagenet Classification with Deep Convolutional Neural Networks. NeurIPS, 1097--1105. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Arun Kumar, Robert McCann, Jeffrey Naughton, and Jignesh M Patel. 2016. Model Selection Management Systems: The Next Frontier of Advanced Analytics. SIGMOD Record 44, 4 (2016), 17--22. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Andreas Kunft, Alexander Alexandrov, Asterios Katsifodimos, and Volker Markl. 2016. Bridging the gap: towards optimization across linear and relational algebra. Workshop on Algorithms and Systems for MapReduce and Beyond at SIGMOD, 1--4. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Neoklis Polyzotis, Sudip Roy, Steven Euijong Whang, and Martin Zinkevich. 2018. Data Lifecycle Challenges in Production Machine Learning: A Survey. SIGMOD Record 47, 2 (2018), 17--28. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. "Why should I trust you?" Explaining the Predictions of Any Classifier. KDD, 1135--1144. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. David Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-Francois Crespo, and Dan Dennison. 2015. Hidden Technical Debt in Machine Learning Systems. NeurIPS, 2503--2511. Google ScholarGoogle ScholarDigital LibraryDigital Library

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in

Full Access

  • Published in

    cover image ACM SIGMOD Record
    ACM SIGMOD Record  Volume 49, Issue 1
    March 2020
    72 pages
    ISSN:0163-5808
    DOI:10.1145/3422648
    Issue’s Table of Contents

    Copyright © 2020 Copyright is held by the owner/author(s)

    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 4 September 2020

    Check for updates

    Qualifiers

    • research-article
  • Article Metrics

    • Downloads (Last 12 months)5
    • Downloads (Last 6 weeks)1

    Other Metrics

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader