# NAS-HPO-Bench-II: A Benchmark Dataset on Joint Optimization of Convolutional Neural Network Architecture and Training Hyperparameters

arXiv: https://arxiv.org/abs/2110.10165 · HTML: https://mlsanity.com/paper/2110.10165/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 45 references that are in the map.
- First posted on arXiv 19 Oct 2021.

## Builds on (most cited first, 30 of 34)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018)
- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://mlsanity.com/paper/1706.02677/) (2017)
- [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://mlsanity.com/paper/1807.11626/) (2018)
- [Regularized Evolution for Image Classifier Architecture Search](https://mlsanity.com/paper/1802.01548/) (2018)
- [Efficient Neural Architecture Search via Parameter Sharing](https://mlsanity.com/paper/1802.03268/) (2018)
- [BOHB: Robust and Efficient Hyperparameter Optimization at Scale](https://mlsanity.com/paper/1807.01774/) (2018)
- [NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search](https://mlsanity.com/paper/2001.00326/) (2020)
- [Large-Scale Evolution of Image Classifiers](https://mlsanity.com/paper/1703.01041/) (2017)
- [NAS-Bench-101: Towards Reproducible Neural Architecture Search](https://mlsanity.com/paper/1902.09635/) (2019)
- [Don't Decay the Learning Rate, Increase the Batch Size](https://mlsanity.com/paper/1711.00489/) (2017)
- [Designing Neural Network Architectures using Reinforcement Learning](https://mlsanity.com/paper/1611.02167/) (2016)
- [One weird trick for parallelizing convolutional neural networks](https://mlsanity.com/paper/1404.5997/) (2014)
- [Neural Architecture Search with Bayesian Optimisation and Optimal Transport](https://mlsanity.com/paper/1802.07191/) (2018)
- [The Evolved Transformer](https://mlsanity.com/paper/1901.11117/) (2019)
- [BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search](https://mlsanity.com/paper/1910.11858/) (2019)
- [NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size](https://mlsanity.com/paper/2009.00437/) (2020)
- [HW-NAS-Bench:Hardware-Aware Neural Architecture Search Benchmark](https://mlsanity.com/paper/2103.10584/) (2021)
- [NAS evaluation is frustratingly hard](https://mlsanity.com/paper/1912.12522/) (2019)
- [NAS-Bench-1Shot1: Benchmarking and Dissecting One-shot Neural Architecture Search](https://mlsanity.com/paper/2001.10422/) (2020)
- [NAS-Bench-NLP: Neural Architecture Search Benchmark for Natural Language Processing](https://mlsanity.com/paper/2006.07116/) (2020)
- [A Genetic Programming Approach to Designing Convolutional Neural Network Architectures](https://mlsanity.com/paper/1704.00764/) (2017)

## Built on by (most cited first, 9 of 9)

- [NAS-Bench-Graph: Benchmarking Graph Neural Architecture Search](https://mlsanity.com/paper/2206.09166/) (2022)
- [Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML](https://mlsanity.com/paper/2303.08485/) (2023)
- [Robust NAS under adversarial training: benchmark, theory, and beyond](https://mlsanity.com/paper/2403.13134/) (2024)
- [When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development](https://mlsanity.com/paper/2209.01083/) (2022)
- [B2EA: An Evolutionary Algorithm Assisted by Two Bayesian Optimization Modules for Neural Architecture Search](https://mlsanity.com/paper/2202.03005/) (2022)
- [Surrogate Benchmarks for Model Merging Optimization](https://mlsanity.com/paper/2509.02555/) (2025)
- [The BUTTER Zone: An Empirical Study of Training Dynamics in Fully Connected Neural Networks](https://mlsanity.com/paper/2207.12547/) (2022)
- [On the use of LLMs to generate a dataset of Neural Networks](https://mlsanity.com/paper/2602.04388/) (2026)
- [ScAn-Bench: Evaluating Scaling Analysis Methodology](https://mlsanity.com/paper/2609.35707/) (2026)

## Abstract (arXiv)

The benchmark datasets for neural architecture search (NAS) have been developed to alleviate the computationally expensive evaluation process and ensure a fair comparison. Recent NAS benchmarks only focus on architecture optimization, although the training hyperparameters affect the obtained model performances. Building the benchmark dataset for joint optimization of architecture and training hyperparameters is essential to further NAS research. The existing NAS-HPO-Bench is a benchmark for joint optimization, but it does not consider the network connectivity design as done in modern NAS algorithms. This paper introduces the first benchmark dataset for joint optimization of network connections and training hyperparameters, which we call NAS-HPO-Bench-II. We collect the performance data of 4K cell-based convolutional neural network architectures trained on the CIFAR-10 dataset with different learning rate and batch size settings, resulting in the data of 192K configurations. The dataset includes the exact data for 12 epoch training. We further build the surrogate model predicting the accuracies after 200 epoch training to provide the performance data of longer training epoch. By analyzing NAS-HPO-Bench-II, we confirm the dependency between architecture and training hyperparameters and the necessity of joint optimization. Finally, we demonstrate the benchmarking of the baseline optimization algorithms using NAS-HPO-Bench-II.