The following list considers papers related to neural architecture search. It is by no means a complete list. If you miss a paper on the list, please let us know.
Update (Dec 2018): Since the list is already quite long by now, we will highlight papers accepted at conferences and journals in the future. This should hopefully provide some guidance towards high-quality papers.
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Architecture Search (and Hyperparameter Optimization):
- AutoGAN: Neural Architecture Search for Generative Adversarial Networks (Gong et al. 2019; accepted at ICCV’19)
https://arxiv.org/abs/1908.03835 - MoGA: Searching Beyond MobileNetV3 (Chu et al. 2019)
https://arxiv.org/abs/1908.01314 - 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)
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 - 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)
https://arxiv.org/abs/1905.12107 - Network Pruning via Transformable Architecture Search (Dong and Yang. 2019)
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)
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 - 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)
https://arxiv.org/abs/1811.07579 - Stochastic Adaptive Neural Architecture Search for Keyword Spotting (Véniat et al. 2018)
https://arxiv.org/abs/1811.06753 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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
- AutoGAN: Neural Architecture Search for Generative Adversarial Networks (Gong et al. 2019; accepted at ICCV’19)
- 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; accepted at AIStat’17)
http://proceedings.mlr.press/v54/klein17a.html - 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
- Speeding up the Hyperparameter Optimization of Deep Convolutional Neural Networks (Hinz et al. 2018)
- Multi-Objective
- TEA-DNN: the Quest for Time-Energy-Accuracy Co-optimized Deep Neural Networks (Cai et al. 2018)
https://arxiv.org/abs/1811.12065 - NSGA-NET: A Multi-Objective Genetic Algorithm for Neural Architecture Search (Lu et al. 2018)
https://arxiv.org/abs/1810.03522 - Searching Toward Pareto-Optimal Device-Aware Neural Architectures (Cheng et al. 2018)
https://arxiv.org/abs/1808.09830 - Multi-objective Architecture Search for CNNs (Elsken et al. 2018)
https://arxiv.org/abs/1804.09081 - MnasNet: Platform-Aware Neural Architecture Search for Mobile (Tan et al. 2018; accepted at CVPR’19)
https://arxiv.org/abs/1807.11626 - 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
- TEA-DNN: the Quest for Time-Energy-Accuracy Co-optimized Deep Neural Networks (Cai et al. 2018)
- Maintained by Marius Lindauer; Last update: July 22nd 2019