Towards Green Automated Machine Learning: Status Quo and Future
Directions

Towards Green Automated Machine Learning: Status Quo and Future Directions

10 November 2021

Alexander Tornede

Eyke Hüllermeier

Papers citing "Towards Green Automated Machine Learning: Status Quo and Future Directions"

7 / 7 papers shown

Title
Improvement of Computational Performance of Evolutionary AutoML in a Heterogeneous Environment Nikolay O. Nikitin Sergey Teryoshkin Valerii Pokrovskii Sergey Pakulin D. Nasonov 26 1 0 12 Jan 2023
AutoML for Climate Change: A Call to Action Renbo Tu Nicholas Roberts Vishak Prasad Sibasis Nayak P. Jain Frederic Sala Ganesh Ramakrishnan Ameet Talwalkar W. Neiswanger Colin White 20 6 0 07 Oct 2022
Fair and Green Hyperparameter Optimization via Multi-objective and Multiple Information Source Bayesian Optimization Antonio Candelieri Andrea Ponti F. Archetti 17 15 0 18 May 2022
Carbon Emissions and Large Neural Network Training David A. Patterson Joseph E. Gonzalez Quoc V. Le Chen Liang Lluís-Miquel Munguía D. Rothchild David R. So Maud Texier J. Dean AI4CE 239 626 0 21 Apr 2021
NAS-Bench-1Shot1: Benchmarking and Dissecting One-shot Neural Architecture Search Arber Zela Julien N. Siems Frank Hutter 72 146 0 28 Jan 2020
Efficient Multi-objective Neural Architecture Search via Lamarckian Evolution T. Elsken J. H. Metzen Frank Hutter 117 498 0 24 Apr 2018
Neural Architecture Search with Reinforcement Learning Barret Zoph Quoc V. Le 264 5,290 0 05 Nov 2016