Hyperparameter optimization of deep neural networks: combining Hperband with Bayesian model selection

By Hadrien Bertrand, Roberto Ardon, Matthieu Perrot, Isabelle Bloch. 2017

41 citations recorded by the source corpus.

How researchers see this paper

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

1. Stefan Falkner
2. Aaron Klein
3. Tong Yu
4. Yimin Huang
5. Hong Zhu
6. Zhenguo Li
7. Yujun Li
8. Frank Hutter

Citations in the corpus

• BOHB: Robust and Efficient Hyperparameter Optimization at Scale (2018, methodology): “Concurrently to our work, two other groups (Bertrand et al., 2017; Wang et al., 2018) also attempted to combine Bayesian optimization with Hyperband.”
• Hyper-Parameter Optimization: A Review of Algorithms and Applications (2020, background): “Several approaches combining BO and HB have been proposed in recent years (Bertrand et al., 2017; Falkner et al., 2018; Wang et al., 2018), to solve the native problem with HB.”
• An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter Optimization (2020, methodology): “Bertrand et al. (2017) used a Gaussian process with a squared exponential kernel as the Bayesian surrogate model instead of the TPE method of BOHB and dealt with model selection tasks.”