Supplementary material for DEHB: Evolutionary Hyperband for Scalable, Robust and Efficient Hyperparameter Optimization

By Noor H. Awad, Neeratyoy Mallik, Frank Hutter. 2021

Subfields: Neural Architecture Search & AutoML

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Neural Architecture Search & AutoML

1. Frank Hutter
2. Marius Thomas Lindauer
3. Matthias Feurer
4. Katharina Eggensperger
5. André Biedenkapp
6. Difan Deng
7. Carolin Benjamins
8. René Sass

Citations in the corpus

• ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models (2022, methodology): “We follow CLIP [66] to implement a simple grid-search style tuning pipeline, and leave more sophisticated methods like BOHB [20] and DEHB [3] as future work.”
• SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization (2021, background): “In addition, evolutionary algorithms are also known as efficient black-box optimizers (Fortin et al., 2012; Olson et al., 2016; Loshchilov and Hutter, 2016; Rapin and Teytaud, 2018; Awad et al., 2021).”