Maintained by Difan Deng and Marius Lindauer.
The following list considers papers related to neural architecture search. It is by no means complete. If you miss a paper on the list, please let us know.
Please note that although NAS methods steadily improve, the quality of empirical evaluations in this field are still lagging behind compared to other areas in machine learning, AI and optimization. We would therefore like to share some best practices for empirical evaluations of NAS methods, which we believe will facilitate sustained and measurable progress in the field. If you are interested in a teaser, please read our blog post or directly jump to our checklist.
Transformers have gained increasing popularity in different domains. For a comprehensive list of papers focusing on Neural Architecture Search for Transformer-Based spaces, the awesome-transformer-search repo is all you need.
2018
Efficient Neural Architecture Search via Parameter Sharing Inproceedings
In: pp. 4092-4101, 2018.
Effective Building Block Design for Deep Convolutional Neural Networks using Search Technical Report
2018.
Memetic evolution of deep neural networks Inproceedings
In: pp. 505-512, 2018.
Understanding and Simplifying One-Shot Architecture Search Inproceedings
In: pp. 549-558, 2018.
PPP-Net - Platform-aware Progressive Search for Pareto-optimal Neural Architectures Inproceedings
In: 2018.
Speeding up the Hyperparameter Optimization of Deep Convolutional Neural Networks Journal Article
In: vol. 17, no. 2, pp. 1850008:1-1850008:15, 2018.
GitGraph - from Computational Subgraphs to Smaller Architecture Search Spaces Inproceedings
In: 2018.
N2N learning - Network to Network Compression via Policy Gradient Reinforcement Learning Inproceedings
In: 2018.
MorphNet - Fast & Simple Resource-Constrained Structure Learning of Deep Networks Inproceedings
In: pp. 1586-1595, 2018.
MaskConnect - Connectivity Learning by Gradient Descent Inproceedings
In: pp. 362-378, 2018.
A Flexible Approach to Automated RNN Architecture Generation Inproceedings
In: 2018.
A Genetic Programming Approach to Designing Convolutional Neural Network Architectures Inproceedings
In: pp. 5369-5373, 2018.
Practical Block-Wise Neural Network Architecture Generation Inproceedings
In: pp. 2423-2432, 2018.
Accelerating Neural Architecture Search using Performance Prediction Inproceedings
In: 2018.
Hierarchical Representations for Efficient Architecture Search Inproceedings
In: 2018.
Learning Transferable Architectures for Scalable Image Recognition Inproceedings
In: pp. 8697-8710, 2018.
Simple and efficient architecture search for Convolutional Neural Networks Inproceedings
In: 2018.
Hyperparameter optimization - a spectral approach Inproceedings
In: 2018.
SMASH - One-Shot Model Architecture Search through HyperNetworks Inproceedings
In: 2018.
Efficient Architecture Search by Network Transformation Inproceedings
In: pp. 2787-2794, 2018.
Searching for Efficient Multi-Scale Architectures for Dense Image Prediction Inproceedings
In: Bengio, Samy; Wallach, Hanna M.; Larochelle, Hugo; Grauman, Kristen; Cesa-Bianchi, Nicolò; Garnett, Roman (Ed.): Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montréal, Canada, pp. 8713–8724, 2018.
2017
Skin cancer reorganization and classification with deep neural network Technical Report
2017.
A Genetic Programming Approach to Designing Convolutional Neural Network Architectures Technical Report
2017.
Skin cancer reorganization and classification with deep neural network Technical Report
2017.
DR-RNN: A deep residual recurrent neural network for model reduction Technical Report
2017.
DeepArchitect - Automatically Designing and Training Deep Architectures Technical Report
2017.
Large-Scale Evolution of Image Classifiers Inproceedings
In: pp. 2902-2911, 2017.
Neural Optimizer Search with Reinforcement Learning Inproceedings
In: pp. 459-468, 2017.
Finding Competitive Network Architectures Within a Day Using UCT Technical Report
2017.
AdaNet - Adaptive Structural Learning of Artificial Neural Networks Inproceedings
In: pp. 874-883, 2017.
Designing Neural Network Architectures using Reinforcement Learning Inproceedings
In: 2017.
Learning Curve Prediction with Bayesian Neural Networks Inproceedings
In: 2017.
Hyperband - A Novel Bandit-Based Approach to Hyperparameter Optimization Journal Article
In: vol. 18, pp. 185:1-185:52, 2017.
2016
Combining Recurrent and Convolutional Neural Networks for Relation Classification Technical Report
2016.
SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioning Technical Report
2016.
Neural networks designing neural networks - multi-objective hyper-parameter optimization Inproceedings
In: pp. 104, 2016.
Convolutional Neural Fabrics Inproceedings
In: pp. 4053-4061, 2016.
CMA-ES for Hyperparameter Optimization of Deep Neural Networks Technical Report
2016.
Net2Net - Accelerating Learning via Knowledge Transfer Inproceedings
In: 2016.
Combinatorially Generated Piecewise Activation Functions Technical Report
2016.
2015
Evaluation of Phosphorene as Anode Material for Na-ion Batteries from First Principles Technical Report
2015.
Optimizing deep learning hyper-parameters through an evolutionary algorithm Inproceedings
In: pp. 4:1-4:5, 2015.
2013
Scalable Oriented-Service Architecture for Heterogeneous and Ubiquitous IoT Domains Technical Report
2013.
A comprehensive review of firefly algorithms Technical Report
2013.
2012
Minimizing Electricity Theft using Smart Meters in AMI Technical Report
2012.
Practical Bayesian Optimization of Machine Learning Algorithms Inproceedings
In: pp. 2960-2968, 2012.
2009
A multiobjective Tabu framework for the optimization and evaluation of wireless systems Technical Report
2009.
A Hypercube-Based Encoding for Evolving Large-Scale Neural Networks Journal Article
In: vol. 15, no. 2, pp. 185-212, 2009.