ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2301.05102
  4. Cited By
Improvement of Computational Performance of Evolutionary AutoML in a
  Heterogeneous Environment

Improvement of Computational Performance of Evolutionary AutoML in a Heterogeneous Environment

12 January 2023
Nikolay O. Nikitin
Sergey Teryoshkin
Valerii Pokrovskii
Sergey Pakulin
D. Nasonov
ArXivPDFHTML

Papers citing "Improvement of Computational Performance of Evolutionary AutoML in a Heterogeneous Environment"

2 / 2 papers shown
Title
AutoML to Date and Beyond: Challenges and Opportunities
AutoML to Date and Beyond: Challenges and Opportunities
Shubhra (Santu) Karmaker
Md. Mahadi Hassan
Micah J. Smith
Lei Xu
Chengxiang Zhai
K. Veeramachaneni
66
222
0
21 Oct 2020
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
Nick Erickson
Jonas W. Mueller
Alexander Shirkov
Hang Zhang
Pedro Larroy
Mu Li
Alex Smola
LMTD
84
576
0
13 Mar 2020
1