The following list considers papers related to neural architecture search. It is by no means complete. We highlight papers accepted at conferences and journals; this should hopefully provide some guidance towards high-quality papers. If you miss a paper on the list, please let us know.
Update [Sept’19]: 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. Therefore, we will pin our research note on that topic at the top of the list for Sept’19. If you are interested in a teaser, please read our blog post or directly jump to our checklist.
- [STICKY] Best Practices for Scientific Research on Neural Architecture Search (Lindauer and Hutter. 2019)
https://arxiv.org/abs/1909.02453 - MANAS: Multi-Agent Neural Architecture Search (Carlucci et al. 2019)
https://arxiv.org/abs/1909.01051 - Neural Architecture Search for Joint Optimization of Predictive Power and Biological Knowledge (Zhang et al. 2019)
https://arxiv.org/abs/1909.00337 - Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research (Balaprakash et al. 2019)
https://arxiv.org/abs/1909.00311 - Automatic Neural Network Search Method for Open Set Recognition (Sun et al. 2019; accepted at ICIP’19)
https://ieeexplore.ieee.org/abstract/document/8803605 - HM-NAS: Efficient Neural Architecture Search via Hierarchical Masking (Yan et al. 2019; accepted at ICCV’19 Neural Architects Workshop)
https://arxiv.org/abs/1909.00122 - Once for All: Train One Network and Specialize it for Efficient Deployment (Cai et al. 2019)
https://arxiv.org/abs/1908.09791 - Refactoring Neural Networks for Verification (Shriver et al. 2019)
https://arxiv.org/abs/1908.08026 - CNASV: A Convolutional Neural Architecture Search-Train Prototype for Computer Vision Task (Zhou and Yang. 2019; accepted at CollaborateCom’19)
- Automatic Design of Deep Networks with Neural Blocks (Zhong et al. 2019; accepted at Cognitive Computation)
https://link.springer.com/article/10.1007/s12559-019-09677-5 - Differentiable Learning-to-Group Channels via Groupable Convolutional Neural Networks (Zhang et al. 2019)
https://arxiv.org/abs/1908.05867 - SCARLET-NAS: Bridging the gap Between Scalability and Fairness in Neural Architecture Search (Chu et al. 2019)
https://arxiv.org/abs/1908.06022 - A Novel Encoding Scheme for Complex Neural Architecture Search (Ahmad et al. 2019; accepted at ITC-CSCC)
https://ieeexplore.ieee.org/document/8793329 - A Graph-Based Encoding for Evolutionary Convolutional Neural Network Architecture Design (Irwin-Harris et al. 2019; accepted CEC’19)
https://ieeexplore.ieee.org/document/8790093 - A Novel Framework for Neural Architecture Search in the Hill Climbing Domain (Verma et al. 2019; accepted at AIKE’19)
https://ieeexplore.ieee.org/abstract/document/8791709 - Automated Neural Network Construction with Similarity Sensitive Evolutionary
Algorithms (Tian et al. 2019)
http://rvc.eng.miami.edu/Paper/2019/IRI19_EA.pdf - AutoGAN: Neural Architecture Search for Generative Adversarial Networks (Gong et al. 2019; accepted at ICCV’19)
https://arxiv.org/abs/1908.03835 - Refining the Structure of Neural Networks Using Matrix Conditioning (Yousefzadeh and O’Leary. 2019)
https://arxiv.org/abs/1908.02400 - SqueezeNAS: Fast neural architecture search for faster semantic segmentation (Shaw et al. 2019)
https://arxiv.org/abs/1908.01748 - MoGA: Searching Beyond MobileNetV3 (Chu et al. 2019)
https://arxiv.org/abs/1908.01314 - Particle Swarm Optimisation for Evolving Deep Neural Networks for Image Classification by Evolving and Stacking Transferable Blocks (Wang et al. 2019)
https://arxiv.org/abs/1907.12659 - MemNet: Memory-Efficiency Guided Neural Architecture Search with Augment-Trim learning (by Liu et al. 2019)
https://arxiv.org/abs/1907.09569 - Efficient Novelty-Driven Neural Architecture Search (Zhang et al. 2019)
https://arxiv.org/abs/1907.09109 - PC-DARTS: Partial Channel Connections for Memory-Efficient Differentiable Architecture Search (Xu et al. 2019)
https://arxiv.org/abs/1907.05737 - Hardware/Software Co-Exploration of Neural Architectures (Jiang et al. 2019)
https://arxiv.org/abs/1907.04650 - EPNAS: Efficient Progressive Neural Architecture Search (Zhou et al. 2019)
https://arxiv.org/abs/1907.04648 - Video Action Recognition via Neural Architecture Searching (Peng et al. 2019)
https://arxiv.org/abs/1907.04632 - Reinforcement Learning for Neural Architecture Search: A Review (Jaafra et al. 2019 accepted at Image and Vision Computing) https://www.sciencedirect.com/science/article/pii/S026288561930088>
- Architecture Search for Image Inpainting (Li and King. 2019. accepted at International Symposium on Neural Networks)
https://link.springer.com/chapter/10.1007/978-3-030-22796-8_12 - Neural Network Architecture Search with Differentiable Cartesian Genetic Programming for Regression (Märtens and Izzo. 2019)
https://arxiv.org/abs/1907.01939 - FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural Architecture Search (Chu et al. 2019)
https://arxiv.org/abs/1907.01845 - HyperNOMAD: Hyperparameter optimization of deep neural networks using mesh adaptive direct search (Lakhmiri et al. 2019)
https://arxiv.org/pdf/1907.01698.pdf - Evolving Robust Neural Architectures to Defend from Adversarial Attacks (Vargas and Kotyan. 2019)
https://arxiv.org/abs/1906.11667 - Surrogate-Assisted Evolutionary Deep Learning Using an End-to-End Random Forest-based Performance Predictor (Sun et al. 2019; accepted by IEEE Transactions on Evolutionary Computation)
https://ieeexplore.ieee.org/document/8744404 - Adaptive Genomic Evolution of Neural Network Topologies (AGENT) for State-to-Action Mapping in Autonomous Agents (Behjat et al. 2019; accepted and presented in ICRA 2019)
https://arxiv.org/abs/1903.07107 - Densely Connected Search Space for More Flexible Neural Architecture Search (Fang et al. 2019)
https://arxiv.org/abs/1906.09607 - SwiftNet: Using Graph Propagation as Meta-knowledge to Search Highly Representative Neural Architectures (Cheng et al. 2019)
https://arxiv.org/abs/1906.08305 - Transfer NAS: Knowledge Transfer between Search Spaces with Transformer Agents (Borsos et al. 2019)
https://arxiv.org/abs/1906.08102 - XNAS: Neural Architecture Search with Expert Advice (Nayman et al. 2019; accepted at NeurIPS’19)
https://arxiv.org/abs/1906.08031 - A Study of the Learning Progress in Neural Architecture Search Techniques (Singh et al. 2019)
https://arxiv.org/abs/1906.07590 - Hardware aware Neural Network Architectures (using FBNet) (Srinivas et al. 2019)
https://arxiv.org/abs/1906.07214 - Sample-Efficient Neural Architecture Search by Learning Action Space (Wang et al. 2019)
https://arxiv.org/abs/1906.06832 - SwiftNet: Using Graph Propagation as Meta-knowledge to Search Highly Representative Neural Architectures (Cheng et al. 2019)
https://arxiv.org/abs/1906.08305 - Automatic Modulation Recognition Using Neural Architecture Search (Wei et al. 2019; accepted High Performance Big Data and Intelligent Systems)
https://ieeexplore.ieee.org/abstract/document/8735458 - Continual and Multi-Task Architecture Search (Pasunuru and Bansal. 2019)
https://arxiv.org/abs/1906.05226 - AutoGrow: Automatic Layer Growing in Deep Convolutional Networks (Wen et al. 2019)
https://arxiv.org/abs/1906.02909 - One-Short Neural Architecture Search via Compressing Sensing (Cho et al. 2019)
https://arxiv.org/abs/1906.02869 - V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation (Zhu et al. 2019)
https://arxiv.org/abs/1906.02817 - StyleNAS: An Empirical Study of Neural Architecture Search to Uncover Surprisingly Fast End-to-End Universal Style Transfer Networks (An et al. 2019)
https://arxiv.org/abs/1906.02470 - Efficient Forward Architecture Search (Hu et al. 2019; accepted at NeurIPS’19)
https://arxiv.org/abs/1905.13360 - Differentiable Neural Architecture Search via Proximal Iterations (Yao et al. 2019)
https://arxiv.org/abs/1905.13577 - Dynamic Distribution Pruning for Efficient Network Architecture Search (Zheng et al. 2019)
https://arxiv.org/abs/1905.13543 - Particle swarm optimization of deep neural networks architectures for image classification (Fernandes Junior and Yen. 2019. accepted at Swarm and Evolutionary Computation)
https://www.sciencedirect.com/science/article/abs/pii/S2210650218309246 - On Network Design Spaces for Visual Recognition (Radosavovic et al. 2019)
https://arxiv.org/abs/1905.13214 - AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures (Ryoo et al. 2019)
https://arxiv.org/abs/1905.13209 - EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (Tan and Le, accepted at ICML’19. 2019)
http://proceedings.mlr.press/v97/tan19a/tan19a.pdf - Structure Learning for Neural Module Networks (Pahuja et al. 2019)
https://arxiv.org/abs/1905.11532 - SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers (Fedorov et al. 2019; accepted at NeurIPS’19)
https://arxiv.org/abs/1905.12107 - Network Pruning via Transformable Architecture Search (Dong and Yang. 2019; accepted at NeurIPS’19)
https://arxiv.org/abs/1905.09717 - DEEP-BO for Hyperparameter Optimization of Deep Networks (Cho et al. 2019)
https://arxiv.org/abs/1905.09680 - Constrained Design of Deep Iris Networks (Nguyen et al. 2019)
https://arxiv.org/abs/1905.09481 - Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search (Akimoto et al. 2019; accepted at ICML’19)
https://arxiv.org/abs/1905.08537 - Multinomial Distribution Learning for Effective Neural Architecture Search (Zheng et al. 2019)
https://arxiv.org/abs/1905.07529 - EENA: Efficient Evolution of Neural Architecture (Zhu et al. 2019)
https://arxiv.org/abs/1905.07320 - DeepSwarm: Optimising Convolutional Neural Networks using Swarm Intelligence (Byla and Pang. 2019)
https://arxiv.org/abs/1905.07350 - AutoDispNet: Improving Disparity Estimation with AutoML (Saikia et al. 2019)
https://arxiv.org/abs/1905.07443 - Online Hyper-parameter Learning for Auto-Augmentation Strategy (Lin et al. 2019)
https://arxiv.org/abs/1905.07373 - Regularized Evolutionary Algorithm for Dynamic Neural Topology Search (Saltori et al. 2019)
https://arxiv.org/abs/1905.06252 - Deep Neural Architecture Search with Deep Graph Bayesian Optimization (Ma et al. 2019)
https://arxiv.org/abs/1905.06159 - Automatic Model Selection for Neural Networks (Laredo et al. 2019)
https://arxiv.org/abs/1905.06010 - Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization (Klein and Hutter. 2019)
https://arxiv.org/abs/1905.04970 - BayesNAS: A Bayesian Approach for Neural Architecture Search (Zhou et al. 2019, accepted at ICML’19)
https://arxiv.org/abs/1905.04919 - Single-Path NAS: Device-Aware Efficient ConvNet Design (Stamoulis et al. 2019)
https://arxiv.org/abs/1905.04159 - Automatic Design of Artificial Neural Networks for Gamma-Ray Detection (Assuncao et al. 2019)
https://arxiv.org/abs/1905.03532 - Neural Architecture Refinement: A Practical Way for Avoiding Overfitting in NAS (Jiang et al. 2019)
https://arxiv.org/abs/1905.02341 - Fast and Reliable Architecture Selection for Convolutional Neural Networks (Hahn et al. 2019)
https://arxiv.org/abs/1905.01924 - Differentiable Architecture Search with Ensemble Gumbel-Softmax (Chang et al. 2019)
https://arxiv.org/abs/1905.01786 - Searching for A Robust Neural Architecture in Four GPU Hours (Dong and Yang 2019, accepted at CVPR’19)
https://xuanyidong.com/publication/cvpr-2019-gradient-based-diff-sampler/ - Evolving unsupervised deep neural networks for learning meaningful representations (Sun et al. 2019, accepted by IEEE Transactions on Evolutionary Computation)
https://arxiv.org/abs/1712.05043 - Evolving Deep Convolutional Neural Networks for Image Classification (Sun et al. 2019, accepted by IEEE Transactions on Evolutionary Computation)
https://arxiv.org/abs/1710.10741 - AdaResU-Net: Multiobjective Adaptive Convolutional Neural Network for Medical Image Segmentation (Baldeon-Calisto and Lai-Yuen. 2019.; accepted at Neurocomputing)
https://www.sciencedirect.com/science/article/pii/S0925231219304679 - Automatic Design of Convolutional Neural Network for Hyperspectral Image Classification (Chen et al. 2019; accepted at IEEE Transactions on Geoscience and Remote Sensing)
https://ieeexplore.ieee.org/abstract/document/8703410 - Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation (Chen et al. 2019)
https://arxiv.org/abs/1904.12760 - Design Automation for Efficient Deep Learning Computing (Han et al. 2019)
https://arxiv.org/abs/1904.10616 - CascadeML: An Automatic Neural Network Architecture Evolution and Training Algorithm for Multi-label Classification (Pakrashi and Namee 2019)
https://arxiv.org/abs/1904.10551 - GraphNAS: Graph Neural Architecture Search with Reinforcement Learning (Gao et al. 2019)
https://arxiv.org/abs/1904.09981 - Neural Architecture Search for Deep Face Recognition (Zhu. 2019)
https://arxiv.org/abs/1904.09523 - Efficient Neural Architecture Search on Low-Dimensional
Data for OCT Image Segmentation (Gessert and Schlaefer. 2019)
https://openreview.net/forum?id=Syg3FDjntN - NAS-Unet: Neural Architecture Search for Medical Image Segmentation (Weng et al. 2019; accepted at IEEE Access)
https://ieeexplore.ieee.org/document/8681706 - Fast DENSER: Efficient Deep NeuroEvolution (Assunção et al. 2019; accepted at ECGP’19)
https://link.springer.com/chapter/10.1007/978-3-030-16670-0_13 - NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection (Ghaisi et al. 2019; accepted at CVPR’19)
https://arxiv.org/abs/1904.07392 - Automated Search for Configurations of Deep Neural Network Architectures (Ghamizi et al. 2019; accepted at SPLC’19)
https://arxiv.org/abs/1904.04612 - WeNet: Weighted Networks for Recurrent Network Architecture Search (Huang and Xiang. 2019)
https://arxiv.org/abs/1904.03819 - Resource Constrained Neural Network Architecture Search (Xiong et al. 2019)
https://arxiv.org/abs/1904.03786 - Size/Accuracy Trade-Off in Convolutional Neural Networks: An Evolutionary Approach (Cetto et al. 2019; accepted at INNSBDDL)
https://link.springer.com/chapter/10.1007/978-3-030-16841-4_3 - ASAP: Architecture Search, Anneal and Prune (Noy et al. 2019)
https://arxiv.org/abs/1904.04123 - Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours (Stamoulis et al. 2019)
https://arxiv.org/abs/1904.02877 - Architecture Search of Dynamic Cells for Semantic Video Segmentation (Nekrasov et al. 2019)
https://arxiv.org/abs/1904.02371 - Template-Based Automatic Search of Compact Semantic Segmentation Architectures (Nekrasov et al. 2019)
https://arxiv.org/abs/1904.02365 - Exploring Randomly Wired Neural Networks for Image Recognition (Xie et al. 2019)
https://arxiv.org/abs/1904.01569 - Understanding Neural Architecture Search Techniques (Adam and Lorraine 2019)
https://arxiv.org/abs/1904.00438 - Automatic Convolutional Neural Architecture Search for Image Classification Under Different Scenes (Weng et al. 2019; accepted for IEEE Access)
https://ieeexplore.ieee.org/document/8676019 - Single Path One-Shot Neural Architecture Search with Uniform Sampling (Guo et al. 2019)
https://arxiv.org/abs/1904.00420 - Network Slimming by Slimmable Networks: Towards One-Shot Architecture Search for Channel Numbers (Yu and Huang 2019)
https://arxiv.org/abs/1903.11728 - sharpDARTS: Faster and More Accurate Differentiable Architecture Search (Hundt et al. 2019)
https://arxiv.org/abs/1903.09900 - DetNAS: Neural Architecture Search on Object Detection (Chen et al. 2019)
https://arxiv.org/abs/1903.10979 - Evolution of Deep Convolutional Neural Networks Using Cartesian Genetic Programming (Suganuma et al. 2019; accepted at Evolutionary Computation)
https://www.mitpressjournals.org/doi/abs/10.1162/evco_a_00253 - Deep Evolutionary Networks with Expedited Genetic Algorithm for Medical Image Denoising (Liu et al. 2019; accepted at Medical Image Analysis)
https://www.sciencedirect.com/science/article/abs/pii/S1361841518307734 - Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly (Kandasamy et al. 2019)
https://arxiv.org/abs/1903.06694 - AttoNets: Compact and Efficient Deep Neural Networks for the Edge via Human-Machine Collaborative Design (Wong et al. 2019)
https://arxiv.org/abs/1903.07209 - Improving Neural Architecture Search Image Classifiers via Ensemble Learning (Macko et al. 2019)
https://arxiv.org/abs/1903.06236 - Software-Defined Design Space Exploration for an Efficient AI Accelerator Architecture (Yu et al. 2019)
https://arxiv.org/abs/1903.07676 - MFAS: Multimodal Fusion Architecture Search (Pérez-Rúa et al. 2019; accepted at CVPR’19)
https://hal.archives-ouvertes.fr/hal-02068293/document - A Hybrid GA-PSO Method for Evolving Architecture and Short Connections of Deep Convolutional Neural Networks (Wang et al. 2019)
https://arxiv.org/abs/1903.03893 - Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search (Li et al. 2019)
https://arxiv.org/abs/1903.03777 - Inductive Transfer for Neural Architecture Optimization (Wistuba and Pedapati 2019)
https://arxiv.org/abs/1903.03536 - Evolutionary Cell Aided Design for Neural Network (Colangelo et al. 2019)
https://arxiv.org/abs/1903.02130 - Automated Architecture-Modeling for Convolutional Neural Networks (Duong 2019)
https://btw.informatik.uni-rostock.de/download/workshopband/D1-1.pdf - Learning Implicitly Recurrent CNNs Through Parameter Sharing (Savarese and Maire, accepted at ICLR’19)
https://arxiv.org/abs/1902.09701 - Evaluating the Search Phase of Neural Architecture Searc (Sciuto et al. 2019)
https://arxiv.org/abs/1902.08142 - Random Search and Reproducibility for Neural Architecture Search (Li and Talwalkar 2019)
https://arxiv.org/abs/1902.07638 - Evolutionary Neural AutoML for Deep Learning (Liang et al. 2019)
https://arxiv.org/abs/1902.06827 - Fast Task-Aware Architecture Inference (Kokiopoulou et al. 2019)
https://arxiv.org/abs/1902.05781 - Probabilistic Neural Architecture Search (Casale et al. 2019)
https://arxiv.org/abs/1902.05116 - Investigating Recurrent Neural Network Memory Structures using Neuro-Evolution (Ororbia et al. 2019)
https://arxiv.org/abs/1902.02390 - Accuracy vs. Efficiency: Achieving Both through FPGA-Implementation Aware Neural Architecture Search (Jiang et al. 2019)
https://arxiv.org/abs/1901.11211 - The Evolved Transformer (So et al. 2019)
https://arxiv.org/abs/1901.11117 - Designing neural networks through neuroevolution (Stanley et al. 2019; accepted at Nature Machine Intelligence)
https://www.nature.com/articles/s42256-018-0006-z - NeuNetS: An Automated Synthesis Engine for Neural Network Design (Sood et al. 2019)
https://arxiv.org/abs/1901.06261 - Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search (Chu et al. 2019)
https://arxiv.org/abs/1901.07261 - EAT-NAS: Elastic Architecture Transfer for Accelerating Large-scale Neural Architecture Search (Fang et al. 2019)
https://arxiv.org/abs/1901.05884 - Bayesian Learning of Neural Network Architectures (Dikov et al. 2019)
https://arxiv.org/abs/1901.04436 - Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation (Liu et al. 2019; accepted at CVPR’19)
https://arxiv.org/abs/1901.02985 - The Art of Getting Deep Neural Networks in Shape (Mammadli et al. 2019; accepted at TACO Journal)
https://dl.acm.org/citation.cfm?id=3291053 - Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search (Chu et al. 2019)
https://arxiv.org/abs/1901.01074 - A particle swarm optimization-based flexible convolutional auto-encoder for image classification (Sun et al. 2018, published by IEEE Transactions on Neural Networks and Learning Systems)
https://arxiv.org/abs/1712.05042 - SNAS: Stochastic Neural Architecture Search (Xie et al. 2018; accepted at ICLR’19)
https://arxiv.org/abs/1812.09926 - Graph Hypernetworks for Neural Architecture Search (Zhang et al. 2018; Accepted at ICLR’19)
https://arxiv.org/abs/1810.05749 - Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution (Elsken et al. 2018; accepted at ICLR’19)
https://arxiv.org/abs/1804.09081 - Macro Neural Architecture Search Revisited (Hu et al. 2018; accepted at Meta-Learn NeurIPS workshop’18)
http://metalearning.ml/2018/papers/metalearn2018_paper16.pdf - AMLA: an AutoML frAmework for Neural Network Design (Kamath et al. 2018; at ICML AutoML workshop)
http://pkamath.com/publications/papers/amla_automl18.pdf - ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation (Dai et al. 2018)
https://arxiv.org/abs/1812.08934 - Neural Architecture Search Over a Graph Search Space (de Laroussilhe et al. 2018)
https://arxiv.org/abs/1812.10666 - A Review of Meta-Reinforcement Learning for Deep Neural Networks Architecture Search (Jaafra et al. 2018)
https://arxiv.org/abs/1812.07995 - Evolutionary Neural Architecture Search for Image Restoration (van Wyk and Bosman 2018)
https://arxiv.org/abs/1812.05866 - IRLAS: Inverse Reinforcement Learning for Architecture Search (Guo et al. 2018; accepted at CVPR’19)
https://arxiv.org/abs/1812.05285 - FBNet: Hardware-Aware Efficient ConvNet Designvia Differentiable Neural Architecture Search (Wu et al. 2018; accepted at CVPR’19)
https://arxiv.org/abs/1812.03443 - ShuffleNASNets: Efficient CNN models throughmodified Efficient Neural Architecture Search (Laube et al. 2018)
https://arxiv.org/abs/1812.02975 - ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware (Cai et al. 2018; accepted at ICLR’19)
https://arxiv.org/abs/1812.00332 - Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search (Wu et al. 2018)
https://arxiv.org/abs/1812.00090 - TEA-DNN: the Quest for Time-Energy-Accuracy Co-optimized Deep Neural Networks (Cai et al. 2018)
https://arxiv.org/abs/1811.12065 - Evolving Space-Time Neural Architectures for Videos (Piergiovanni et al. 2018)
https://arxiv.org/abs/1811.10636 - InstaNAS: Instance-aware Neural Architecture Search (Cheng et al. 2018)
https://arxiv.org/abs/1811.10201 - Evolutionary-Neural Hybrid Agents for Architecture Search (Maziarz et al. 2018; accepted at ICML’19 workshop on AutoML)
https://arxiv.org/abs/1811.09828 - Joint Neural Architecture Search and Quantization (Chen et al. 2018)
https://arxiv.org/abs/1811.09426 - Transfer Learning with Neural AutoML (Wong et al. 2018; accepted at NeurIPS’18)
http://papers.nips.cc/paper/8056-transfer-learning-with-neural-automl.pdf - Evolving Image Classification Architectures with Enhanced Particle Swarm Optimisation (Fielding and Zhang 2018)
https://ieeexplore.ieee.org/document/8533601 - Deep Active Learning with a Neural Architecture Search (Geifman and El-Yaniv 2018; accepted at NeurIPS’19)
https://arxiv.org/abs/1811.07579 - Stochastic Adaptive Neural Architecture Search for Keyword Spotting (Véniat et al. 2018)
https://arxiv.org/abs/1811.06753 - NSGA-NET: A Multi-Objective Genetic Algorithm for Neural Architecture Search (Lu et al. 2018)
https://arxiv.org/abs/1810.03522 - You only search once: Single Shot Neural Architecture Search via Direct Sparse Optimization (Zhang et al. 2018)
https://arxiv.org/abs/1811.01567 - Automatically Evolving CNN Architectures Based on Blocks (Sun et al. 2018; accepted by IEEE Transactions on Neural Networks and Learning Systems)
https://arxiv.org/abs/1810.11875 - The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints (Hundt et al. 2018; accepted at IROS’19)
https://arxiv.org/abs/1810.11714 - Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells (Nekrasov et al. 2018; accepted at CVPR’19)
https://arxiv.org/abs/1810.10804 - Automatic Configuration of Deep Neural Networks with Parallel Efficient Global Optimization (van Stein et al. 2018)
https://arxiv.org/abs/1810.05526 - Gradient Based Evolution to Optimize the Structure of Convolutional Neural Networks (Mitschke et al. 2018)
https://ieeexplore.ieee.org/document/8451394 - Searching Toward Pareto-Optimal Device-Aware Neural Architectures (Cheng et al. 2018)
https://arxiv.org/abs/1808.09830 - Neural Architecture Optimization (Luo et al. 2018; accepted at NeurIPS’18)
https://arxiv.org/abs/1808.07233 - Exploring Shared Structures and Hierarchies for Multiple NLP Tasks (Chen et al. 2018)
https://arxiv.org/abs/1808.07658 - Neural Architecture Search: A Survey (Elsken et al. 2018)
https://arxiv.org/abs/1808.05377 - BlockQNN: Efficient Block-wise Neural Network Architecture Generation (Zhong et al. 2018)
https://arxiv.org/abs/1808.05584 - Automatically Designing CNN Architectures Using Genetic Algorithm for Image Classification (Sunet al. 2018)
https://arxiv.org/abs/1808.03818 - Reinforced Evolutionary Neural Architecture Search (Chen et al. 2018; accepted at CVPR’19)
https://arxiv.org/abs/1808.00193 - Teacher Guided Architecture Search (Bashivan et al. 2018)
https://arxiv.org/abs/1808.01405 - Efficient Progressive Neural Architecture Search (Perez-Rua et al. 2018)
https://arxiv.org/abs/1808.00391 - MnasNet: Platform-Aware Neural Architecture Search for Mobile (Tan et al. 2018; accepted at CVPR’19)
https://arxiv.org/abs/1807.11626 - Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search (Zela et al. 2018)
https://arxiv.org/abs/1807.06906 - Automatically Designing CNN Architectures for Medical Image Segmentation (Mortazi and Bagci 2018)
https://arxiv.org/abs/1807.07663 - MONAS: Multi-Objective Neural Architecture Search using Reinforcement Learning (Hsu et al. 2018)
https://arxiv.org/abs/1806.10332 - Path-Level Network Transformation for Efficient Architecture Search (Cai et al. 2018; accepted at ICML’18)
https://arxiv.org/abs/1806.02639 - Lamarckian Evolution of Convolutional Neural Networks (Prellberg and Kramer, 2018)
https://arxiv.org/abs/1806.08099 - Deep Learning Architecture Search by Neuro-Cell-based Evolution with Function-Preserving Mutations (Wistuba, 2018)
http://www.ecmlpkdd2018.org/wp-content/uploads/2018/09/108.pdf - DARTS: Differentiable Architecture Search (Liu et al. 2018; accepted at ICLR’19)
https://arxiv.org/abs/1806.09055 - Constructing Deep Neural Networks by Bayesian Network Structure Learning (Rohekar et al. 2018)
https://arxiv.org/abs/1806.09141 - Resource-Efficient Neural Architect (Zhou et al. 2018)
https://arxiv.org/abs/1806.07912 - Efficient Neural Architecture Search with Network Morphism (Jin et al. 2018)
https://arxiv.org/abs/1806.10282 - TAPAS: Train-less Accuracy Predictor for Architecture Search (Istrate et al. 2018)
https://arxiv.org/abs/1806.00250 - AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search (Wang et al 2018)
https://arxiv.org/abs/1805.07440
https://arxiv.org/abs/1903.11059 - Multi-objective Architecture Search for CNNs (Elsken et al. 2018)
https://arxiv.org/abs/1804.09081 - GNAS: A Greedy Neural Architecture Search Method for Multi-Attribute Learning (Huang et al 2018)
https://arxiv.org/abs/1804.06964 - Evolutionary Architecture Search For Deep Multitask Networks (Liang et al. 2018)
https://arxiv.org/abs/1803.03745 - From Nodes to Networks: Evolving Recurrent Neural Networks (Rawal et al. 2018)
https://arxiv.org/abs/1803.04439 - Neural Architecture Construction using EnvelopeNets (Kamath et al. 2018)
https://arxiv.org/abs/1803.06744 - Transfer Automatic Machine Learning (Wong et al. 2018)
https://arxiv.org/abs/1803.02780 - 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; accepted at ICML’18)
https://arxiv.org/abs/1802.03268 - Regularized Evolution for Image Classifier Architecture Search (Real et al. 2018)
https://arxiv.org/abs/1802.01548 - Effective Building Block Design for Deep Convolutional Neural Networks using Search (Dutta et al. 2018)
https://arxiv.org/abs/1801.08577 - Combination of Hyperband and Bayesian Optimization for Hyperparameter Optimization in Deep Learning (Wang et al. 2018)
https://arxiv.org/abs/1801.01596 - Memetic Evolution of Deep Neural Networks (Lorenzo and Nalepa 2018)
https://dl.acm.org/citation.cfm?id=3205631 - Understanding and Simplifying One-Shot Architecture Search (Bender et al. 2018; accepted at ICML’18)
http://proceedings.mlr.press/v80/bender18a/bender18a.pdf - Differentiable Neural Network Architecture Search (Shin et al. 2018; accepted at ICLR’18 workshop)
https://openreview.net/pdf?id=BJ-MRKkwG - PPP-Net: Platform-aware progressive search for pareto-optimal neural architectures (Dong et al. 2018; accepted at ICLR’18 workshop)
https://openreview.net/pdf?id=B1NT3TAIM - 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 - N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning (Ashok et al. 2017; accepted at ICLR’18)
https://arxiv.org/abs/1709.06030 - Genetic CNN (Xie and Yuille, 2017; accepted at ICCV’17)
https://arxiv.org/abs/1703.01513 - MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks (Gordon et al. 2017)
https://arxiv.org/abs/1711.06798 - MaskConnect: Connectivity Learning by Gradient Descent (Ahmed and Torresani. 2017; accepted at ECCV’18)
https://arxiv.org/abs/1709.09582 - A Flexible Approach to Automated RNN Architecture Generation (Schrimpf et al. 2017)
https://arxiv.org/abs/1712.07316 - DeepArchitect: Automatically Designing and Training Deep Architectures (Negrinho and Gordon 2017)
https://arxiv.org/abs/1704.08792 - A Genetic Programming Approach to Designing Convolutional Neural Network Architectures (Suganuma et al. 2017; accepted at GECCO’17)
https://arxiv.org/abs/1704.00764 - Practical Block-wise Neural Network Architecture Generation (Zhong et al. 2017; accepted at CVPR’18)
https://arxiv.org/abs/1708.05552 - Accelerating Neural Architecture Search using Performance Prediction (Baker et al. 2017; accepted at NeurIPS worshop on Meta-Learning 2017)
https://arxiv.org/abs/1705.10823 - Large-Scale Evolution of Image Classifiers (Real et al. 2017; accepted at ICML’17)
https://arxiv.org/abs/1703.01041 - Hierarchical Representations for Efficient Architecture Search (Liu et al. 2017; accepted at ICLR’18)
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; accepted at ECCV’18)
https://arxiv.org/abs/1712.00559 - Learning Transferable Architectures for Scalable Image Recognition (Zoph et al. 2017; CVPR’18)
https://arxiv.org/abs/1707.07012 - Simple And Efficient Architecture Search for Convolutional Neural Networks (Elsken et al. 2017; accepted at NeurIPS workshop on Meta-Learning’17)
https://arxiv.org/abs/1711.04528 - Bayesian Optimization Combined with Incremental Evaluation for Neural Network Architecture Optimization (Wistuba, 2017)
https://www.semanticscholar.org/paper/Bayesian-Optimization-Combined-with-Successive-for-Wistuba/ddb182533c91f0941f088e1e298c52a111253554 - 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 - SMASH: One-Shot Model Architecture Search through HyperNetworks (Brock et al. 2017; accepted at NeurIPS workshop on Meta-Learning’17)
https://arxiv.org/abs/1708.05344 - Efficient Architecture Search by Network Transformation (Cai et al. 2017; accepted at AAAI’18)
https://arxiv.org/abs/1707.04873 - Modularized Morphing of Neural Networks (Wei et al. 2017)
https://arxiv.org/abs/1701.03281 - Towards Automatically-Tuned Neural Networks (Mendoza et al. 2016; accepted at ICML AutoML workshop)
http://proceedings.mlr.press/v64/mendoza_towards_2016.html - Neural Networks Designing Neural Networks: Multi-Objective Hyper-Parameter Optimization (Smithson et al. 2016)
https://arxiv.org/abs/1611.02120 - AdaNet: Adaptive Structural Learning of Artificial Neural Networks (Cortes et al. 2016)
https://arxiv.org/abs/1607.01097 - Network Morphism (Wei et al. 2016)
https://arxiv.org/abs/1603.01670 - Convolutional Neural Fabrics (Saxena and Verbeek 2016; accepted at NeurIPS’16)
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; accepted at ICLR’17)
https://arxiv.org/abs/1611.02167 - Neural Architecture Search with Reinforcement Learning (Zoph and Le. 2016; accepted at ICLR’17)
https://arxiv.org/abs/1611.01578 - Learning curve prediction with Bayesian Neural Networks (Klein et al. 2017: accepted at ICLR’17)
http://ml.informatik.uni-freiburg.de/papers/17-ICLR-LCNet.pdf - Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization (Li et al. 2016)
https://arxiv.org/abs/1603.06560 - Net2Net: Accelerating Learning via Knowledge Transfer (Chen et al. 2015; accepted at ICLR’16)
https://arxiv.org/abs/1511.05641 - 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; accepted at NeurIPS’12)
https://papers.nips.cc/paper/4522-practical-bayesian-optimization-of-machine-learning-algorithms.pdf - A Hypercube-based Encoding for Evolving large-scale Neural Networks (Stanley et al. 2009)
https://ieeexplore.ieee.org/document/6792316/ - Neuroevolution: From Architectures to Learning (Floreano et al. 2008; accepted at Evolutionary Intelligence’08)
https://link.springer.com/article/10.1007/s12065-007-0002-4 - Evolving Neural Networks through Augmenting Topologies (Stanley and Miikkulainen, 2002; accepted at Evolutionary Computation’02)
http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf - Evolving Artificial Neural Networks (Yao, 1999; accepted at IEEE)
https://ieeexplore.ieee.org/document/784219/ - An Evolutionary Algorithm that Constructs Recurrent Neural Networks (Angeline et al. 1994)
https://ieeexplore.ieee.org/document/265960/ - Designing Neural Networks Using Genetic Algorithms with Graph Generation System (Kitano, 1990)
http://www.complex-systems.com/abstracts/v04_i04_a06/ - Designing Neural Networks using Genetic Algorithms (Miller et al. 1989; accepted at ICGA’89)
https://dl.acm.org/citation.cfm?id=94034 - The Cascade-Correlation Learning Architecture (Fahlman and Leblere, 1989; accepted at NeurIPS’89)
https://papers.nips.cc/paper/207-the-cascade-correlation-learning-architecture - Self Organizing Neural Networks for the Identification Problem (Tenorio and Lee, 1988; accepted at NeurIPS’88)
https://papers.nips.cc/paper/149-self-organizing-neural-networks-for-the-identification-problem
Maintained by Marius Lindauer; Last update: September 9th 2019