| Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective |
ICLR |
- |
2021 |
| when NAS Meets Robustness：In Search of Robust Architectures against Adversarial Attacks |
CVPR |
robustness |
2020 |
| On Robustness of Neural Architecture Search Under Label Noise |
- |
robustness |
2020 |
| Evolving Robust Neural Architectures to Defend from Adversarial Attacks |
- |
robustness |
2020 |
| Is Neural Architecture Search A Way Forward to Develop Robust Neural Networks? |
- |
robustness |
2020 |
| AutoHAS: Differentiable Hyper-parameter and Architecture Search |
- |
HAO |
2020 |
| FBNetV3: Joint Architecture-Recipe Search using Neural Acquisition Function |
- |
HAO |
2020 |
| NAS-BENCH-201: Extending the Scope of Reproducible Neural Architecture Search |
ICLR |
benchmark |
2020 |
| NAS-Bench-NLP: Neural Architecture Search Benchmark for Natural Language Processing |
- |
benchmark |
2020 |
| NAS-Bench-1Shot1: Benchmarking and Dissecting One-shot Neural Architecture Search |
- |
benchmark |
2020 |
| Overcoming Multi-Model Forgetting in One-Shot NAS with Diversity Maximization |
CVPR |
GD;one-shot;kendall tau |
2020 |
| FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions |
- |
GD |
2020 |
| Semi-Supervised Neural Architecture Search |
- |
semi-supervised NAS |
2020 |
| Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization |
- |
HAO |
2019 |
| NAS-Bench-101: Towards Reproducible Neural Architecture Search |
ICML |
benchmark |
2019 |
| Densely Connected Search Space for More Flexible Neural Architecture Search |
- |
GD |
2019 |
| sharpDARTS: Faster and More Accurate Differentiable Architecture Search |
- |
|
2019 |
| Multi-Fidelity Automatic Hyper-Parameter Tuning via Transfer Series Expansion |
- |
|
2019 |
| Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search |
- |
HAO;Benchmark |
2018 |
| Auto-Meta: Automated Gradient Based Meta Learner Search |
NeurIPS |
meta-learning NAS |
2018 |
| Meta-Learning of Neural Architectures for Few-Shot Learning |
- |
meta-learning NAS |
2018 |
| Towards Fast Adaptation of Neural Architectures with Meta Learning |
- |
meta-learning NAS |
2018 |
| Efficient neural architecture search via parameter sharing |
ICML |
|
2018 |
| Regularized evolution for image classifier architecture search |
- |
|
2018 |
| Hierarchical representations for efficient architecture search |
ICLR |
|
2018 |
| Efficient multi-objective neural architecture search via lamarckian evolution |
- |
|
2018 |
| Efficient architecture search by network transformation |
AAAI |
|
2018 |
| Efficient multi-objective neural architecture search via lamarckian evolution |
- |
|
2018 |
| BOHB: Robust and efficient hyperparameter optimization at scale |
- |
|
2018 |
| DARTS: Differentiable architecture search |
- |
|
2018 |
| Differentiable neural network architecture search |
- |
|
2018 |
| ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware |
ICLR |
|
2018 |
| Regularized evolution for image classifier architecture search |
- |
|
2018 |
| Towards automated deep learning: Efficient joint neural architecture and hyperparameter search |
- |
|
2018 |
| Transfer learning with neural automl |
- |
|
2018 |
| Learning transferable architectures for scalable image recognition |
CVPR |
|
2017 |
| Practical block-wise neural network architecture generation |
CVPR |
|
2017 |
| Large-scale evolution of image classifiers |
- |
|
2017 |
| Genetic CNN |
ICCV |
|
2017 |
| A genetic programming approach to designing convolutional neural network architectures |
- |
|
2017 |
| Large-scale evolution of image classifiers |
- |
|
2017 |
| Connectivity learning in multi-branch networks |
- |
|
2017 |
| Learning transferable architectures for scalable image recognition |
- |
|
2017 |
| Surrogate-based methods for black-box optimization |
- |
|
2017 |
| Progressive neural architecture search |
- |
|
2017 |
| Peephole: Predicting network performance before training |
- |
|
2017 |
| Early stopping without a validation set |
- |
|
2017 |
| Neural architecture search with reinforcement learning |
- |
|
2016 |
| Designing neural network architectures using reinforcement learning |
- |
|
2016 |
| Hyperband: A novel bandit-based approach to hyperparameter optimization |
- |
|
2016 |
| Gpyopt: A bayesian optimization framework in python |
- |
|
2016 |
| Fast bayesian optimization of machine learning hyperparameters on large datasets |
- |
|
2016 |
| Convolutional neural fabrics |
NIPS |
|
2016 |
| Hyperparameter optimization with approximate gradient |
- |
|
2016 |
| Fast bayesian optimization of machine learning hyperparameters on large datasets |
- |
|
2016 |
| Network morphism |
- |
|
2016 |
| Learning curve prediction with bayesian neural networks |
- |
|
2016 |
| Net2net: Accelerating learning via knowledge transfer |
- |
|
2015 |
| Gradient-based hyperparameter optimization through reversible learning |
- |
|
2015 |
| Net2net: Accelerating learning via knowledge transfer |
- |
|
2015 |
| Efficient benchmarking of hyperparameter optimizers via surrogates |
- |
|
2015 |
| Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves |
- |
|
2015 |
| Surrogate benchmarks for hyperparameter optimization |
ECAI |
|
2014 |
| An evaluation of adaptive surrogate modeling based optimization with two benchmark problems |
- |
|
2014 |
| Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures |
- |
|
2013 |
| Random search for hyper-parameter optimization |
JMLR |
|
2012 |
| Practical bayesian optimization of machine learning algorithms |
- |
|
2012 |
| Automated Algorithm Configuration and Parameter Tuning |
- |
|
2011 |
| Sequential model-based optimization for general algorithm configuration |
- |
|
2011 |
| Maximum-likelihood estimation with a contracting-grid search algorithm |
- |
|
2010 |
| An empirical evaluation of deep architectures on problems with many factors of variation |
ICML |
|
2007 |
| Response surface methodology for optimizing hyper parameters |
- |
|
2006 |
| A practical guide to support vector classification |
- |
|
2003 |
| Evolving neural networks through augmenting topologies |
- |
|
2002 |