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Towards Leveraging AutoML for Sustainable Deep Learning: A
  Multi-Objective HPO Approach on Deep Shift Neural Networks

Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks

2 April 2024
Leona Hennig
Tanja Tornede
Marius Lindauer
    AI4CE
ArXivPDFHTML

Papers citing "Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks"

4 / 4 papers shown
Title
Tuning for Trustworthiness -- Balancing Performance and Explanation Consistency in Neural Network Optimization
Tuning for Trustworthiness -- Balancing Performance and Explanation Consistency in Neural Network Optimization
Alexander Hinterleitner
T. Bartz-Beielstein
21
0
0
12 May 2025
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter
  Optimization
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
Marius Lindauer
Katharina Eggensperger
Matthias Feurer
André Biedenkapp
Difan Deng
C. Benjamins
Tim Ruhopf
René Sass
Frank Hutter
85
330
0
20 Sep 2021
Hyperparameter Optimization: Foundations, Algorithms, Best Practices and
  Open Challenges
Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges
B. Bischl
Martin Binder
Michel Lang
Tobias Pielok
Jakob Richter
...
Theresa Ullmann
Marc Becker
A. Boulesteix
Difan Deng
Marius Lindauer
85
455
0
13 Jul 2021
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision
  Applications
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Andrew G. Howard
Menglong Zhu
Bo Chen
Dmitry Kalenichenko
Weijun Wang
Tobias Weyand
M. Andreetto
Hartwig Adam
3DH
950
20,599
0
17 Apr 2017
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