The following list considers papers related to neural architecture search. It is by no means a complete list. If you miss a paper on this list, please let us know.
- Architecture Search (and Hyperparameter Optimization):
- Memetic Evolution of Deep Neural Networks
https://dl.acm.org/citation.cfm?id=3205631 - Understanding and Simplifying One-Shot Architecture Search (Bender et al. 2018)
http://proceedings.mlr.press/v80/bender18a/bender18a.pdf - Lamarckian Evolution of Convolutional Neural Networks (Prellberg and Kramer. 2018)
https://arxiv.org/abs/1806.08099 - DARTS: Differentiable Architecture Search (Liu et al. 2018)
https://arxiv.org/abs/1806.09055 - Differentiable Neural Network Architecture Search (Shin et al. 2018)
https://openreview.net/pdf?id=BJ-MRKkwG - Efficient Neural Architecture Search with Network Morphism (Jin et al. 2018)
https://arxiv.org/abs/1806.10282 - Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search (Zela et al. 2018)
https://ml.informatik.uni-freiburg.de/papers/18-AUTOML-EfficientNAS.pdf - Path-Level Network Transformation for Efficient Architecture Search (Cai et al. 2018)
https://arxiv.org/abs/1806.02639 - Neural Architecture Search with Bayesian Optimisation and Optimal Transport (Kandasamy et al. 2018)
https://arxiv.org/abs/1802.07191 - Efficient Neural Architecture Search via Parameter Sharing (Pham et al. 2018)
https://arxiv.org/abs/1802.03268 - Neural Architecture Construction using EnvelopeNets (Kamath et al. 2018)
https://arxiv.org/abs/1803.06744 - Regularized Evolution for Image Classifier Architecture Search (Real et al. 2018)
https://arxiv.org/abs/1802.01548 - AdaNet: Adaptive Structural Learning of Artificial Neural Networks (Cortes et al. 2017)
https://arxiv.org/abs/1607.01097 - Accelerating Neural Architecture Search using Performance Prediction (Baker et al. 2017)
https://arxiv.org/abs/1705.10823
- Large-Scale Evolution of Image Classifiers (Real et al. 2017)
https://arxiv.org/abs/1703.01041 - Hierarchical Representations for Efficient Architecture Search (Liu et al. 2017)
https://arxiv.org/abs/1711.00436 - Neural Optimizer Search with Reinforcement Learning (Bello et al. 2017)
https://arxiv.org/abs/1709.07417 - Progressive Neural Architecture Search (Liu et al. 2017)
https://arxiv.org/abs/1712.00559 - Learning Transferable Architectures for Scalable Image Recognition (Zoph et al. 2017)
https://arxiv.org/abs/1707.07012 - Simple And Efficient Architecture Search for Convolutional Neural Networks (Elsken et al. 2017)
https://arxiv.org/abs/1711.04528 - Finding Competitive Network Architectures Within a Day Using UCT (Wistuba 2017)
https://arxiv.org/abs/1712.07420 - Hyperparameter Optimization: A Spectral Approach (Hazan et al. 2017)
https://arxiv.org/abs/1706.00764 - Population Based Training of Neural Networks (Jaderberg et al. 2017)
https://arxiv.org/abs/1711.09846 - SMASH: One-Shot Model Architecture Search through HyperNetworks (Brock et al. 2017)
https://arxiv.org/abs/1708.05344 - Efficient Architecture Search by Network Transformation (Cai et al. 2017)
https://arxiv.org/abs/1707.04873 - Towards Automatically-Tuned Neural Networks (Mendoza et al. 2016)
http://proceedings.mlr.press/v64/mendoza_towards_2016.html - Convolutional Neural Fabrics (Saxena and Verbeek 2016)
https://arxiv.org/abs/1606.02492 - CMA-ES for Hyperparameter Optimization of Deep Neural Networks (Loshchilov and Hutter 2016)
https://arxiv.org/abs/1604.07269
- Designing Neural Network Architectures using Reinforcement Learning (Baker et al. 2016)
https://arxiv.org/abs/1611.02167 - Neural Architecture Search with Reinforcement Learning (Zoph and Le. 2016)
https://arxiv.org/abs/1611.01578 - Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization (Li et al. 2016)
https://arxiv.org/abs/1603.06560 - Optimizing deep learning hyper-parameters through an evolutionary algorithm (Young et al. 2015)
https://dl.acm.org/citation.cfm?id=2834896 - Practical Bayesian Optimization of Machine Learning Algorithms (Snoek et al. 2012)
https://papers.nips.cc/paper/4522-practical-bayesian-optimization-of-machine-learning-algorithms.pdf
- Memetic Evolution of Deep Neural Networks
- Make it more efficient
- Speeding up the Hyperparameter Optimization of Deep Convolutional Neural Networks (Hinz et al. 2018)
https://www.worldscientific.com/doi/abs/10.1142/S1469026818500086 - Gitgraph – From Computational Subgraphs to Smaller Architecture search spaces (Bennani-Smires et al. 2018)
https://openreview.net/pdf?id=rkiO1_1Pz - Transfer Automatic Machine Learning (Wong et al. 2018)
https://arxiv.org/abs/1803.02780 - Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets (Klein et al. 2017)
http://proceedings.mlr.press/v54/klein17a.html - Learning curve prediction with Bayesian Neural Networks (Klein et al. 2017)
http://ml.informatik.uni-freiburg.de/papers/17-ICLR-LCNet.pdf
- Speeding up the Hyperparameter Optimization of Deep Convolutional Neural Networks (Hinz et al. 2018)
- Multi-Objective NAS
- Multi-objective Architecture Search for CNNs (Elsken et al. 2018)
https://arxiv.org/abs/1804.09081 - MONAS: Multi-Objective Neural Architecture Search using Reinforcement Learning (Hsu et al. 2018)
https://arxiv.org/abs/1806.10332 - PPP-Net: Platform-aware progressive search for pareto-optimal neural architectures (Dong et al. 2018)
https://openreview.net/pdf?id=B1NT3TAIM - Neural Networks Designing Neural Networks: Multi-Objective Hyper-Parameter Optimization (Smithson et al. 2016)
https://arxiv.org/abs/1611.02120
- Multi-objective Architecture Search for CNNs (Elsken et al. 2018)
Maintained by Marius Lindauer