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Awesome Neural Architecture Search Papers

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Awesome Neural Architecture Search Papers

(中文)

We would like to maintain a complete list of NAS-related papers and provide a guide for some of the papers that have received wide interest.

Table of Contents

Tasks

Medical

|Year | Title | Code | |:--------|:--------|:--------:| | 2020 | Deep Convolution Features in Non-linear Embedding Space for Fundus Image Classification(Dondeti et al. 2020)
accepted at Revue d’Intelligence Artificielle | | | 2020 | Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound(Huang et al. 2020)
accepted at MICCAI 2020 | | | 2020 | Multi-Modality Information Fusion for Radiomics-based Neural Architecture Search(Peng et al. 2020)
accepted at MICCAI 2020 | | | 2020 | Modeling Task-based fMRI Data via Deep Belief Network with Neural Architecture Search(Qiang et al. 2020)
accepted at Computerized Medical Imaging and Graphics | | | 2020 | AdaEn-Net: An Ensemble of Adaptive 2D-3D Fully Convolutional Networks for Medical Image Segmentation(Baldeon Calisto and Lai-Yuen. 2020)
accepted at Neural Networks | | | 2020 | AutoSegNet: An Automated Neural Network for Image Segmentation(Xu et al. 2020)
accepted at IEEE Access | | | 2020 | Optimize CNN Model for FMRI Signal Classification Via Adanet-Based Neural Architecture Search(Dai et al. 2020)
accepted at IEEE ISBI | | | 2020 | Neural Architecture Search for Skin Lesion Classification(Kwasigroch et al. 2020)
accepted at IEEE Access | | | 2019 | Scalable Neural Architecture Search for 3D Medical Image Segmentation(Kim et al. 2019)
accepted at MICCAI’19 | | | 2019 | Neural Architecture Search for Adversarial Medical Image Segmentation(Dong et al. 2019)
accepted at MICCAI’19 | | | 2019 | Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation(Yang et al. 2019)
accepted at MICCAI’19 | | | 2019 | Resource Optimized Neural Architecture Search for 3D Medical Image Segmentation(Bae et al. 2019)
accepted at MICCAI’19 | | | 2019 | Self-Adaptive 2D-3D Ensemble of Fully Convolutional Networks for Medical Image Segmentation(Calisto and Lai-Yuen. 2019)
accepted at SPIE Medical Imaging’20 | | | 2019 | AdaResU-Net: Multiobjective Adaptive Convolutional Neural Network for Medical Image Segmentation(Baldeon-Calisto and Lai-Yuen. 2019.)
accepted at Neurocomputing | | | 2019 | NAS-Unet: Neural Architecture Search for Medical Image Segmentation(Weng et al. 2019)
accepted at IEEE Access | | | 2020 | Efficient Oct Image Segmentation Using Neural Architecture Search(Gheshlaghi et al. 2020) | | | 2020 | MS-NAS: Multi-Scale Neural Architecture Search for Medical Image Segmentation(Yan et al. 2020) | | | 2020 | Heuristic Architecture Search Using Network Morphism for Chest X-Ray Classification(Radiuk and Kutucu 2020) | | | 2020 | Evolving Deep Neural Networks for X-ray Based Detection of Dangerous Objects(Tsukada et al. 2020) | | | 2020 | Neural Architecture Search for Gliomas Segmentation on Multimodal Magnetic Resonance Imaging(Wang et al. 2020) | | | 2020 | AutoHR: A Strong End-to-end Baseline for Remote Heart Rate Measurement with Neural Searching(Yu et al. 2020) | | | 2020 | Organ at Risk Segmentation for Head and Neck Cancer using Stratified Learning and Neural Architecture Search(Guo et al. 2020) | | | 2020 | ElixirNet: Relation-aware Network Architecture Adaptation for Medical Lesion Detection(Jiang et al. 2020) | | | 2020 | Neural Architecture Search for Compressed Sensing Magnetic Resonance Image Reconstruction(Yan et al. 2020) | Github | | 2020 | ENAS U-Net: Evolutionary Neural Architecture Search for Retinal Vessel(Fan et al. 2020) | | | 2019 | C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation(Yu et al. 2019) | | | 2019 | SegNAS3D: Network Architecture Search with Derivative-Free Global Optimization for 3D Image Segmentation(Wong and Moradi. 2019) | | | 2019 | V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation(Zhu et al. 2019) | | | 2019 | Efficient Neural Architecture Search on Low-Dimensional Data for OCT Image Segmentation(Gessert and Schlaefer. 2019) | | | 2018 | Automatically Designing CNN Architectures for Medical Image Segmentation(Mortazi and Bagci 2018) | |

Image_Segmentation

|Year | Title | Code | |:--------|:--------|:--------:| | 2020 | AdaEn-Net: An Ensemble of Adaptive 2D-3D Fully Convolutional Networks for Medical Image Segmentation(Baldeon Calisto and Lai-Yuen. 2020)
accepted at Neural Networks | | | 2020 | AutoSegNet: An Automated Neural Network for Image Segmentation(Xu et al. 2020)
accepted at IEEE Access | | | 2020 | Fast Neural Network Adaptation via Parameter Remapping and Architecture Search(Fang et al. 2020)
accepted at ICLR’20 | Github | | 2019 | Scalable Neural Architecture Search for 3D Medical Image Segmentation(Kim et al. 2019)
accepted at MICCAI’19 | | | 2019 | Neural Architecture Search for Adversarial Medical Image Segmentation(Dong et al. 2019)
accepted at MICCAI’19 | | | 2019 | Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation(Yang et al. 2019)
accepted at MICCAI’19 | | | 2019 | Resource Optimized Neural Architecture Search for 3D Medical Image Segmentation(Bae et al. 2019)
accepted at MICCAI’19 | | | 2019 | Self-Adaptive 2D-3D Ensemble of Fully Convolutional Networks for Medical Image Segmentation(Calisto and Lai-Yuen. 2019)
accepted at SPIE Medical Imaging’20 | | | 2019 | AdaResU-Net: Multiobjective Adaptive Convolutional Neural Network for Medical Image Segmentation(Baldeon-Calisto and Lai-Yuen. 2019.)
accepted at Neurocomputing | | | 2019 | NAS-Unet: Neural Architecture Search for Medical Image Segmentation(Weng et al. 2019)
accepted at IEEE Access | | | 2019 | Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation(Liu et al. 2019)
accepted at CVPR’19 | Github | | 2018 | Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells(Nekrasov et al. 2018)
accepted at CVPR’19 | | | 2020 | Efficient Oct Image Segmentation Using Neural Architecture Search(Gheshlaghi et al. 2020) | | | 2020 | MS-NAS: Multi-Scale Neural Architecture Search for Medical Image Segmentation(Yan et al. 2020) | | | 2020 | DCNAS: Densely Connected Neural Architecture Search for Semantic Image Segmentation(Zhang et al. 2020) | | | 2020 | ENAS U-Net: Evolutionary Neural Architecture Search for Retinal Vessel(Fan et al. 2020) | | | 2019 | C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation(Yu et al. 2019) | | | 2019 | SegNAS3D: Network Architecture Search with Derivative-Free Global Optimization for 3D Image Segmentation(Wong and Moradi. 2019) | | | 2019 | Graph-guided Architecture Search for Real-time Semantic Segmentation(Lin et al. 2019) | | | 2019 | SqueezeNAS: Fast neural architecture search for faster semantic segmentation(Shaw et al. 2019) | | | 2019 | V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation(Zhu et al. 2019) | | | 2019 | Efficient Neural Architecture Search on Low-Dimensional Data for OCT Image Segmentation(Gessert and Schlaefer. 2019) | | | 2019 | Template-Based Automatic Search of Compact Semantic Segmentation Architectures(Nekrasov et al. 2019) | | | 2018 | Automatically Designing CNN Architectures for Medical Image Segmentation(Mortazi and Bagci 2018) | |

Model_Compression

|Year | Title | Code | |:--------|:--------|:--------:| | 2020 | Mixed-Precision Quantization for CNN-Based Remote Sensing Scene Classification(Wei et al. 2020)
accepted at IEEE Geoscience and Remote Sensing Letters | | | 2020 | Search What You Want: Barrier Panelty NAS for Mixed Precision Quantization(Yu et al. 2020)
accepted at ECCV 2020 | | | 2020 | Standing on the Shoulders of Giants: Hardware and Neural Architecture Co-Search with Hot Start(Jiang et al. 2020)
accepted at IEEE Transactions On Computer-Aided Design of Integrated Circuits and System | | | 2020 | Finding Non-Uniform Quantization Schemes using Multi-Task Gaussian Processes(do Nascimento et al. 2020)
accepted at ECCV 2020 | Github | | 2020 | CP-NAS: Child-Parent Neural Architecture Search for 1-bit CNNs(Zhuo et al. 2020)
accepted at IJCAI 2020 | | | 2020 | Butterfly Transform: An Efficient FFT Based Neural Architecture Design(Alizadeh vahid et al. 2020)
accepted at CVPR 2020 | Github | | 2020 | NeuralScale: Efficient Scaling of Neurons for Resource-Constrained Deep Neural Networks(Lee and Lee)
accepted at CVPR 2020 | Github | | 2020 | Auto-Fas: Searching Lightweight Networks for Face Anti-Spoofing(Yu et al. 2020)
accepted at accetped at ICASSP 2020 | | | 2020 | Accelerator-Aware Neural Network Design Using AutoML(Gupta and Akin. 2020)
accepted at On-device Intelligence Workshop at MLSys’20 | | | 2020 | Efficient Evolutionary Architecture Search for CNN Optimization on GTSRB(Johner and Wassner. 2020)
accepted at ICMLA’19 | | | 2020 | Automating Deep Neural Network Model Selection for Edge Inference(Lu et al. 2020)
accepted at CogMI’20 | | | 2020 | Best of Both Worlds: AutoML Codesign of a CNN and its Model Compression(Abdelfattah et al. 2020)
accepted at DAC’20 | | | 2020 | Co-Exploration of Neural Architectures and Heterogeneous ASIC Accelerator Designs Targeting Multiple Tasks(Yang et al. 2020)
accepted at DAC’20 | | | 2020 | FPNet: Customized Convolutional Neural Network for FPGA Platforms(Yang et al. 2020)
accepted at FPT’20 | | | 2020 | Search for Better Students to Learn Distilled Knowledge(Gu et al. 2020)
accepted at ECAI'20 | | | 2020 | HNAS: Hierarchical Neural Architecture Search on Mobile Devices(Xia et al. 2020) | | | 2020 | Binarizing MobileNet via Evolution-based Searching(Phan et al. 2020) | | | 2020 | CP-NAS: Child-Parent Neural Architecture Search for 1-bit CNNs( Zhuo et al. 2020) | | | 2020 | MobileDets: Searching for Object Detection Architectures for Mobile Accelerators( Xiong et al. 2020) | | | 2020 | GAN Compression: Efficient Architectures for Interactive Conditional GAN(Li et al. 2020) | Github | | 2020 | Search for Winograd-Aware Quantized Networks(Fernandez-Marques et al. 2020) | | | 2020 | AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search(Chen et al. 2020) | |

Multi-objective_Search

|Year | Title | Code | |:--------|:--------|:--------:| | 2020 | CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending(Xu et al. 2020)
accepted at ECCV 2020 | Github | | 2020 | NSGANetV2: Evolutionary Multi-Objective Surrogate-Assisted Neural Architecture Search(Lu et al. 2020)
accepted at ECCV 2020 | Github | | 2020 | Neural-Architecture-Search-Based Multiobjective Cognitive Automation System(Wang et al. 2020)
accepted at IEEE System Journal | | | 2020 | Beyond Network Pruning: a Joint Search-and-Training Approach(Lu et al. 2020)
accepted at IJCAI 2020 | | | 2020 | Hardware-Aware Transformable Architecture Search with Efficient Search Space(Jiang et al. 2020)
accepted at accpeted at ICME 2020 | | | 2020 | Fast Hardware-Aware Neural Architecture Search(Zhang et al. 2020)
accepted at CVPR 2020 workshop | | | 2020 | MemNAS: Memory-Efficient Neural Architecture Search with Grow-Trim Learning(Liu et al.2020)
accepted at CVPR 2020 | Github | | 2020 | APQ: Joint Search for Network Architecture, Pruning and Quantization Policy(Wang et al.2020)
accepted at CVPR 2020 | Github | | 2020 | Designing Resource-Constrained Neural Networks Using Neural Architecture Search Targeting Embedded Devices(Cassimon et al. 2020)
accepted at IEEE Internet of Things | | | 2020 | FTT-NAS: Discovering Fault-Tolerant Neural Architecture(Li et al. 2020)
accepted at ASP-DAC 2020 | | | 2020 | DeepMaker: A multi-objective optimization framework for deep neural networks in embedded systems(Loni et al. 2020)
accepted at Microprocessors and Microsystems | | | 2020 | Multi-Objective Neural Architecture Search Based on Diverse Structures and Adaptive Recommendation(Wang et al. 2020) | Github | | 2020 | You Only Search Once: A Fast Automation Framework for Single-Stage DNN/Accelerator Co-design(Chen et al. 2020) | | | 2020 | FlexiBO: Cost-Aware Multi-Objective Optimization of Deep Neural Networks(Iqbal et al. 2020) | Github |

Object_Detection

|Year | Title | Code | |:--------|:--------|:--------:| | 2020 | Representation Sharing for Fast Object Detector Search and Beyond(Zhou et al .2020)
accepted at ECCV 2020 | | | 2020 | FNA++: Fast Network Adaptation via Parameter Remapping and Architecture Search(Fang et al. 2020)
accepted at ICLR 2020 | Github | | 2020 | SP-NAS: Serial-to-Parallel Backbone Search for Object Detection(Jiang et al. 2020)
accepted at CVPR 2020 | | | 2020 | Automated Design of Neural Network Architectures with Reinforcement Learning for Detection of Global Manipulations(Chen et al. 2020)
accepted at IEEE Journal of Selected Topics in Signal Processing | | | 2020 | Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection(Guo et al. 2020)
accepted at CVPR 2020 | Github | | 2020 | Fast Neural Network Adaptation via Parameter Remapping and Architecture Search(Fang et al. 2020)
accepted at ICLR’20 | Github | | 2019 | Auto-FPN: Automatic Network Architecture Adaptation for Object Detection Beyond Classification(Xu et al. 2019)
accepted at ICCV’19 | | | 2019 | DetNAS: Neural Architecture Search on Object Detection(Chen et al. 2019)
accepted at NeurIPS’19 | Github | | 2020 | MobileDets: Searching for Object Detection Architectures for Mobile Accelerators( Xiong et al. 2020) | |

GAN

|Year | Title | Code | |:--------|:--------|:--------:| | 2020 | Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search(Tian et al. 2020)
accepted at ECCV 2020 | Github | | 2020 | A Multi-objective architecture search for generative adversarial networks(Kobayashi et al. 2020)
accepted at GECCO 2020 | | | 2020 | AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks(Fu et al. 2020)
accepted at ICML 2020 | Github | | 2019 | AutoGAN: Neural Architecture Search for Generative Adversarial Networks(Gong et al. 2019)
accepted at ICCV’19 | Github | | 2020 | Searching towards Class-Aware Generators for Conditional Generative Adversarial Networks(Zhou et al. 2020) | Github | | 2020 | AlphaGAN: Fully Differentiable Architecture Search for Generative Adversarial Networks(Tian et al. 2020) | Github | | 2020 | Conditional Neural Architecture Search(Kao et al. 2020) | | | 2020 | GAN Compression: Efficient Architectures for Interactive Conditional GAN(Li et al. 2020) | Github |

Image_Translator

|Year | Title | Code | |:--------|:--------|:--------:| | 2020 | AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks(Fu et al. 2020)
accepted at ICML 2020 | Github | | 2020 | Memory-Efficient Hierarchical Neural Architecture Search for Image Denoising(Zhang et al. 2020)
accepted at CVPR 2020 | | | 2020 | All in One Bad Weather Removal using Architectural Search(Li et al. 2020)
accepted at CVPR 2020 | | | 2020 | Journey Towards Tiny Perceptual Super-Resolution(Lee et al. 2020) | | | 2020 | Hierarchical Neural Architecture Search for Single Image Super-Resolution(Guo et al. 2020) | | | 2020 | Automatically Searching for U-Net Image Translator Architecture(Shu and Wang. 2020) | |

Video_Models

|Year | Title | Code | |:--------|:--------|:--------:| | 2020 | AttentionNAS: Spatiotemporal Attention Cell Search for Video Classification(Wang et al. 2020)
accepted at ECCV 2020 | | | 2020 | Architecture Search of Dynamic Cells for Semantic Video Segmentation(Nekrasov et al. 2020)
accepted at WACV 2020 | | | 2020 | Tiny Video Networks: Architecture Search for Efficient Video Models(Piergiovanni et al. 2020)
accepted at 7th ICML Workshop on Automated Machine Learning, 2020 | | | 2018 | Evolving Space-Time Neural Architectures for Videos(Piergiovanni et al. 2018)
accepted at ICCV’19 | | | 2019 | Video Action Recognition via Neural Architecture Searching(Peng et al. 2019) | | | 2019 | AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures(Ryoo et al. 2019) | |

GNN

|Year | Title | Code | |:--------|:--------|:--------:| | 2020 | Graph Neural Architecture Search(Gao et al. 2020)
accepted at IJCAI 2020 | Github | | 2020 | A Semi-Supervised Assessor of Neural Architectures(Tang et al. 2020)
accepted at CVPR 2020 | | | 2020 | Neural Architecture Optimization with Graph VAE(Li et al. 2020) | | | 2020 | A Generic Graph-based Neural Architecture Encoding Scheme for Predictor-based NAS(Ning et al. 2020)
accepted at ECCV 2020 | Github | | 2020 | Probabilistic Dual Network Architecture Search on Graphs(Zhao et al. 2020) | |

Unsupervised

|Year | Title | Code | |:--------|:--------|:--------:| | 2020 | Superkernel Neural Architecture Search for Image Denoising(Mozejko et al. 2020)
accepted at NTIRE2020 Workshop at CVPR 2020 | | | 2020 | An Evolutionary Approach to Variational Autoencoders(Hajewski and Oliveira. 2020)
accepted at CCWC’20 | | | 2020 | Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?(Yan et al. 2020) | | | 2020 | Are Labels Necessary for Neural Architecture Search?(Liu et al. 2020) | Github |

Binary_Networks

|Year | Title | Code | |:--------|:--------|:--------:| | 2020 | CP-NAS: Child-Parent Neural Architecture Search for 1-bit CNNs(Zhuo et al. 2020)
accepted at IJCAI 2020 | | | 2020 | DMS: Differentiable Dimension Search for Binary Neural Networks(Li et al. 2020)
accepted at 1st Workshop on Neural Architecture Search at ICLR 2020 | | | 2020 | BATS: Binary ArchitecTure Search(Bulat et al. 2020)
accepted at ECCV’20 | Github | | 2020 | Learning Architectures for Binary Networks(Kim et al. 2020)
accepted at ECCV’20 | |

CTR

|Year | Title | Code | |:--------|:--------|:--------:| | 2020 | Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction(Song et al. 2020)
accepted at KDD2020 | | | 2020 | AMER: Automatic Behavior Modeling and Interaction Exploration in Recommender System(Zhao et al. 2020) | | | 2020 | Differentiable Neural Input Search for Recommender Systems(Cheng et al. 2020) | | | 2020 | AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations(Zhao et al. 2020) | |

Multimodal_Learning

|Year | Title | Code | |:--------|:--------|:--------:| | 2020 | Multi-Modality Information Fusion for Radiomics-based Neural Architecture Search(Peng et al. 2020)
accepted at MICCAI 2020 | | | 2020 | RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning(Alletto et al. 2020)
accepted at Meta-Eval 2020 workshop | | | 2019 | MFAS: Multimodal Fusion Architecture Search(Pérez-Rúa et al. 2019)
accepted at CVPR’19 | | | 2020 | Deep Multimodal Neural Architecture Search(Yu et al. 2020) | |

Federated_Learning

|Year | Title | Code | |:--------|:--------|:--------:| | 2020 | FedNAS: Federated Deep Learning via Neural Architecture Search(He et al. 2020)
accepted at CVPR 2020 Workshop on Neural Architecture Search and Beyond for Representation Learning | Github | | 2020 | Differentially-private Federated Neural Architecture Search(Singh et al. 2020) | Github | | 2020 | Real-time Federated Evolutionary Neural Architecture Search(Zhu and Jin. 2020) | | | 2020 | Neural Architecture Search over Decentralized Data(Xu et al. 2020) | |

Speech_Recognition

|Year | Title | Code | |:--------|:--------|:--------:| | 2020 | DARTS-ASR: Differentiable Architecture Search for Multilingual Speech Recognition and Adaptation(Chen et al. 2020)
accepted at INTERSPEECH 2020 | | | 2020 | Neural Architecture Search for Speech Recognition(Hu et al. 2020) | | | 2020 | AutoSpeech: Neural Architecture Search for Speaker Recognition(Ding et al. 2020) | Github |

Benchmark

|Year | Title | Code | |:--------|:--------|:--------:| | 2020 | NAS-Bench-1Shot1: Benchmarking and Dissecting One-Shot Neural Architecture Search(Zela et al. 2020)
accepted at ICLR’20 | | | 2020 | NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search(Dong and Yang et al. 2020)
accepted at ICLR’20 | Github | | 2020 | NAS-Bench-NLP: Neural Architecture Search Benchmark for Natural Language Processing(Klyuchnikov et al. 2020) | |

Remote_Sensing

|Year | Title | Code | |:--------|:--------|:--------:| | 2020 | Mixed-Precision Quantization for CNN-Based Remote Sensing Scene Classification(Wei et al. 2020)
accepted at IEEE Geoscience and Remote Sensing Letters | | | 2020 | RSNet: The Search for Remote Sensing Deep Neural Networks in Recognition Tasks(Wang et al. 2020)
accepted at IEEE Transactions on Geoscience and Remote Sensing | | | 2020 | Convolution Neural Network Architecture Learning for Remote Sensing Scene Classification(Chen et al. 2010) | |

3DDeepLearning

|Year | Title | Code | |:--------|:--------|:--------:| | 2020 | Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution(Tang et al. 2020)
accepted at ECCV 2020 | | | 2020 | Lidar Data Classification Based on Automatic Designed CNN(Xie and Chen 2020)
accepted at IEEE Geoscience and Remote Sensing Letters | | | 2020 | Fusion Mechanisms for Human Activity Recognition using Automated Machine Learning(Popescu et al. 2020)
accepted at IEEE Access | |

SceneTextRecognition

|Year | Title | Code | |:--------|:--------|:--------:| | 2020 | Memory-Efficient Models for Scene Text Recognition via Neural Architecture Search(Hong et al. 2020)
accepted at WACV’20 workshop | | | 2020 | Efficient Backbone Search for Scene Text Recognition(Zhang et al. 2020) | |

NLP

|Year | Title | Code | |:--------|:--------|:--------:| | 2020 | NAS-Bench-NLP: Neural Architecture Search Benchmark for Natural Language Processing(Klyuchnikov et al. 2020) | | | 2020 | AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search(Chen et al. 2020) | |

Private_Inference

|Year | Title | Code | |:--------|:--------|:--------:| | 2020 | SOTERIA: In Search of Efficient Neural Networks for Private Inference(Aggarwal et al. 2020) | | | 2020 | CryptoNAS: Private Inference on a ReLU Budget(Ghodsi et al. 2020) | |

Imitation_Learning

|Year | Title | Code | |:--------|:--------|:--------:| | 2020 | NASIL: Neural Architecture Search With Imitation Learning(Fard et al. 2020)
accepted at ICASSP 2020 | | | 2020 | AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning(Li et al. 2020) | |

Time_Series

|Year | Title | Code | |:--------|:--------|:--------:| | 2020 | Neural Architecture Search for Time Series Classification(Rakhshani et al. 2020)
accepted at ijcnn 2020 | | | 2020 | Improving Neuroevolution Using Island Extinction And Repopulation(Lyu et al. 2020) | |

Semantic_Segmentation

|Year | Title | Code | |:--------|:--------|:--------:| | 2020 | Architecture Search of Dynamic Cells for Semantic Video Segmentation(Nekrasov et al. 2020)
accepted at WACV 2020 | | | 2020 | FNA++: Fast Network Adaptation via Parameter Remapping and Architecture Search(Fang et al. 2020)
accepted at ICLR 2020 | Github |

Distributed_System

|Year | Title | Code | |:--------|:--------|:--------:| | 2020 | A Scalable System for Neural Architecture Search(Hajewski and Oliveira. 2020)
accepted at CCWC’20 | | | 2020 | Distributed Evolution of Deep Autoencoders(Hajewski et al. 2020) | |

Autonomous_Driving

|Year | Title | Code | |:--------|:--------|:--------:| | 2020 | Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution(Tang et al. 2020)
accepted at ECCV 2020 | | | 2020 | CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending(Xu et al. 2020)
accepted at ECCV 2020 | Github |

Meta-learning

|Year | Title | Code | |:--------|:--------|:--------:| | 2020 | CATCH: Context-based Meta Reinforcement Learning for Transferrable Architecture Search(Chen et al. 2020)
accepted at ECCV 2020 | | | 2020 | M-NAS: Meta Neural Architecture Search(Wang et al. 2020)
accepted at AAAI 2020 | |

Language_Modeling

|Year | Title | Code | |:--------|:--------|:--------:| | 2020 | Searching Better Architectures for Neural Machine Translation(Fan et al. 2020)
accepted at IEEE/ACM Transactions on Audio, Speech, and Language Processing | | | 2020 | Learning Architectures from an Extended Search Space for Language Modeling(Li et al. 2020)
accepted at ACL 2020 | |

Image_Denoising

|Year | Title | Code | |:--------|:--------|:--------:| | 2020 | Superkernel Neural Architecture Search for Image Denoising(Mozejko et al. 2020)
accepted at NTIRE2020 Workshop at CVPR 2020 | | | 2020 | Neural Architecture Search for Deep Image Prior(Ho et al. 2020) | |

Image_Recognition

|Year | Title | Code | |:--------|:--------|:--------:| | 2020 | On Network Design Spaces for Visual Recognition(Radosavovic et al. 2020)
accepted at ICCV 2019 | Github | | 2020 | Memory-Efficient Models for Scene Text Recognition via Neural Architecture Search(Hong et al. 2020)
accepted at WACV’20 workshop | |


2020

| Title | Tags | Code | |:--------|:--------:|:--------:| | Deep Convolution Features in Non-linear Embedding Space for Fundus Image Classification(Dondeti et al. 2020)
accepted at Revue d’Intelligence Artificielle | Medical
Image Classification
NASNet | - | | A Unified Approach to Anomaly Detection(Ball et al. 2020)
accepted at The Sixth International Conference on Machine Learning | Anomaly Detection
AutoEncoder
Evoluationary | - | | Evolving Multi-Resolution Pooling CNN for Monaural Singing Voice Separation(Yuan et al. 2020) | Monaural Singing Voice Separation
Evolutionary | - | | Weight-Sharing Neural Architecture Search: A Battle to Shrink the Optimization Gap(Xie et al. 2020) | Survey
CV | - | | Neural Architecture Search in Graph Neural Networks(Nunes and L.Pappa 2020) | Graph Neural Networks
Evolutionary
RL| - | | Anti-Bandit Neural Architecture Search for Model Defense(Chen et al. 2020)
accepted at ECCV 2020 | Adversarial Defense
ABanditNAS | Github | | HMCNAS: Neural Architecture Search Using Hidden Markov Chains And Bayesian Optimization(Lopes and Alexandre 2020) | HMCNAS
Evolutionary | - | | Neural Architecture Search as Sparse Supernet(Wu et al. 2020) | - | - | | Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution(Tang et al. 2020)
accepted at ECCV 2020 | 3D Deep Learning
Autonomous Driving
Resource Constraints
Evolutionary | - | | Growing Efficient Deep Networks by Structured Continuous Sparsification(Yuan et al. 2020) | Network Pruning | - | | Lidar Data Classification Based on Automatic Designed CNN(Xie and Chen 2020)
accepted at IEEE Geoscience and Remote Sensing Letters | 3D Deep Learning
Gradient-based | - | | Fusion Mechanisms for Human Activity Recognition using Automated Machine Learning(Popescu et al. 2020)
accepted at IEEE Access | Human Activity Recognition
3D Deep Learning
CV
RL | - | | Mixed-Precision Quantization for CNN-Based Remote Sensing Scene Classification(Wei et al. 2020)
accepted at IEEE Geoscience and Remote Sensing Letters | Remote Sensing
Model Compression
Mixed-Precision Quantization | - | | Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound(Huang et al. 2020)
accepted at MICCAI 2020 | Medical
GDAS
RL | - | | TF-NAS: Rethinking Three Search Freedoms of Latency-Constrained Differentiable Neural Architecture Search(Hu et al. 2020)
accepted at ECCV 2020 | TF-NAS | Github | | Efficient Oct Image Segmentation Using Neural Architecture Search(Gheshlaghi et al. 2020) | Medical
Image Segmentation
ProxylessNAS | - | | SOTERIA: In Search of Efficient Neural Networks for Private Inference(Aggarwal et al. 2020) | Private Inference
DARTS | - | | What and Where: Learn to Plug Adapters via NAS for Multi-Domain Learning(Zhao et al. 2020) | Multi-Domain Learning | - | | CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending(Xu et al. 2020)
accepted at ECCV 2020 | Autonomous Driving
Lane Detection
Multi-objective Search
Evolutionary | Dataset | | Representation Sharing for Fast Object Detector Search and Beyond(Zhou et al .2020)
accepted at ECCV 2020 | Object Detection
| - | | AttentionNAS: Spatiotemporal Attention Cell Search for Video Classification(Wang et al. 2020)
accepted at ECCV 2020 | Video Models
DARTS | - | | MCUNet: Tiny Deep Learning on IoT Devices(Lin et al. 2020) | IoT
| - | | Search What You Want: Barrier Panelty NAS for Mixed Precision Quantization(Yu et al. 2020)
accepted at ECCV 2020 | Model Compression
Mixed Precision Quantization
DARTS | - | | NSGANetV2: Evolutionary Multi-Objective Surrogate-Assisted Neural Architecture Search(Lu et al. 2020)
accepted at ECCV 2020 | Multi-objective Search
| Github | | CATCH: Context-based Meta Reinforcement Learning for Transferrable Architecture Search(Chen et al. 2020)
accepted at ECCV 2020 | Meta-learning
RL | - | | Standing on the Shoulders of Giants: Hardware and Neural Architecture Co-Search with Hot Start(Jiang et al. 2020)
accepted at IEEE Transactions On Computer-Aided Design of Integrated Circuits and System | Model Compression
HotNAS
RL| - | | Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search(Tian et al. 2020)
accepted at ECCV 2020 | GAN
RL | Github | | Neural Architecture Search for Speech Recognition(Hu et al. 2020) | Speech Recognition
DARTS | - | | BRP-NAS: Prediction-based NAS using GCNs(Chau et al .2020) | Predictor-based
GCN | - | | Finding Non-Uniform Quantization Schemes using Multi-Task Gaussian Processes(do Nascimento et al. 2020)
accepted at ECCV 2020 | Model Compression
Bayesian Optimization | Github | | One-Shot Neural Architecture Search via Novelty Driven Sampling(Zhang et al. 2020)
accepted at IJCAI 2020 | Evolutionary
Single-path One-shot | - | | Neural Architecture Search in A Proxy Validation Loss Landscape(Li et al. 2020)
accepted at ICML 2020 | Estimation Strategy | - | | CP-NAS: Child-Parent Neural Architecture Search for 1-bit CNNs(Zhuo et al. 2020)
accepted at IJCAI 2020 | Model Compression
Binary Networks | - | | SI-VDNAS: Semi-Implicit Variational Dropout for Hierarchical One-shot Neural Architecture Search(Wang et al. 2020)
accepted at IJCAI 2020 | Search Strategy | - | | An Empirical Study on the Robustness of NAS based Architectures(Devaguptapu et al. 2020) | Study | - | | MergeNAS: Merge Operations into One for Differentiable Architecture Search(Wang et al. 2020)
accepted at IJCAI 2020 | Search Strategy | - | | DropNAS: Grouped Operation Dropout for Differentiable Architecture Search(Hong et al. 2020)
accepted at IJCAI 2020 | Search Strategy | - | | Evolving Robust Neural Architectures to Defend from Adversarial Attacks(Kotyan and Vargas 2020)
accepted at Proceedings of the Workshop on Artificial Intelligence Safety 2020 | Adversarial Attacks and Defenses | Github | | Architecture Search of Dynamic Cells for Semantic Video Segmentation(Nekrasov et al. 2020)
accepted at WACV 2020 | Video Models
Semantic Segmentation | - | | Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search(Guo et al. 2020)
accepted at ICML 2020 | Search Strategy | - | | Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction(Song et al. 2020)
accepted at KDD2020 | CTR | - | | MS-NAS: Multi-Scale Neural Architecture Search for Medical Image Segmentation(Yan et al. 2020) | Medical
Image Segmentation | - | | VINNAS: Variational Inference-based Neural Network Architecture Search(Ferianc et al. 2020) | - | - | | Multi-Modality Information Fusion for Radiomics-based Neural Architecture Search(Peng et al. 2020)
accepted at MICCAI 2020 | Medical
Multimodal Learning | - | | Graph Neural Architecture Search(Gao et al. 2020)
accepted at IJCAI 2020 | GNN
RL | Github | | Ensembles of Networks Produced from Neural Architecture Search(Herron et al. 2020) | Neural Network Ensembles | - | | Neural Architecture Search with GBDT(Luo et al. 2020) | Predictor-based | Github | | A Study on Encodings for Neural Architecture Search(White et al. 2020) | Study
Survey | Github | | NASGEM: Neural Architecture Search via Graph Embedding Method(Cheng et al. 2020) | Estimation Strategy | - | | An Evolution-based Approach for Efficient Differentiable Architecture Search(Kobayashi and Nagao)
accepted at GECCO 2020 | - | - | | HyperFDA: a bi-level Optimization Approach to Neural Architecture Search and Hyperparameters’ optimization via fractal decomposition-based algorithm(Souquet et al. 2020)
accepted at GECCO 2020 | - | - | | A Multi-objective architecture search for generative adversarial networks(Kobayashi et al. 2020)
accepted at GECCO 2020 | GAN | - | | A first Step toward Incremental Evolution of Convolutional Neural Networks(Barnes et al. 2020)
accepted at GECCO 2020 | - | - | | Computational model for neural architecture search(Gottapu 2020) | - | - | | Neural Architecture Search for extreme multi-label classification: an evolutionary approach(Pauletto et al. 2020) | Multi-label Classification | - | | Hyperparameter Optimization in Neural Networks via Structured Sparse Recovery(Cho et al. 2020) | - | - | | Journey Towards Tiny Perceptual Super-Resolution(Lee et al. 2020) | Image Translator
Super-Resolution | - | | Self-supervised Neural Architecture Search(Kaplan and Giryes 2020) | - | - | | Multi-Objective Neural Architecture Search Based on Diverse Structures and Adaptive Recommendation(Wang et al. 2020) | Multi-objective Search | Github | | Parametric machines: a fresh approach to architecture search(Vertechi et al. 2020) | - | - | | Discretization-Aware Architecture Search(Tian et al. 2020) | - | - | | GOLD-NAS: Gradual, One-Level, Differentiable(Bi et al. 2020) | - | - | | Surrogate-assisted Particle Swarm Optimisation for Evolving Variable-length Transferable(Wang et al. 2020) | Search Strategy | - | | M-NAS: Meta Neural Architecture Search(Wang et al. 2020)
accepted at AAAI 2020 | Meta-learning | - | | FiFTy: Large-scale File Fragment Type Identification using Convolutional Neural Networks(Mittal et al. 2020)
accepted at IEEE Transactions on Information Forensics and Security | File-type Identification
Forensics | - | | RSNet: The Search for Remote Sensing Deep Neural Networks in Recognition Tasks(Wang et al. 2020)
accepted at IEEE Transactions on Geoscience and Remote Sensing | Remote Sensing | - | | Theory-Inspired Path-Regularized Differential Network Architecture Search(Zhou et al. 2020) | Search Strategy | - | | The Heterogeneity Hypothesis: Finding Layer-Wise Dissimilated Network Architecture(Li et al. 2020) | - | - | | Semi-Discrete Optimization Through Semi-Discrete Optimal Transport: A Framework for Neural Architecture Search(Trillos and Morales 2020) | - | - | | Traditional And Accelerated Gradient Descent for Neural Architecture Search(Trillos et al. 2020) | Search Strategy | - | | AutoSNAP: Automatically Learning Neural Architectures for Instrument Pose Estimation(Kügler et al. 2020)
accepted at MICCAI 2020 | Pose Estimation | Github | | Evolutionary Recurrent Neural Architecture Search(Tian et al. 2020)
accepted at IEEE Embedded System Letters | Search Strategy | - | | Neural-Architecture-Search-Based Multiobjective Cognitive Automation System(Wang et al. 2020)
accepted at IEEE System Journal | Cognitive Computing
Multi-objective Search | - | | Enhancing Model Parallelism in Neural Architecture Search for Multi-device System(Fu et al. 2020)
accepted at IEEE Micro | Multi-device System | - | | AutoST: Efficient Neural Architecture Search for Spatio-Temporal Prediction(Li et al. 2020)
accepted at KDD 2020 | Spatio-Temporal Prediction | - | | Neural Architecture Search for Sparse DenseNets with Dynamic Compression(O’Neill et al. 2020)
accepted at GECCO 2020 | Search Strategy | - | | Searching towards Class-Aware Generators for Conditional Generative Adversarial Networks(Zhou et al. 2020) | GAN | Github | | Neural Architecture Design for GPU-Efficient Networks(Lin et al. 2020) | - | - | | Equivalence in Deep Neural Networks via Conjugate Matrix Ensembles(Süzen 2020) | - | - | | Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL(Zimmer et al. 2020) | - | - | | NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search(Panda et al. 2020) | - | - | | Tiny Video Networks: Architecture Search for Efficient Video Models(Piergiovanni et al. 2020)
accepted at 7th ICML Workshop on Automated Machine Learning, 2020 | Video Models | - | | FNA++: Fast Network Adaptation via Parameter Remapping and Architecture Search(Fang et al. 2020)
accepted at ICLR 2020 | Semantic Segmentation
Object Detection | Github | | Neural networks adapting to datasets: learning network size and topology(Janik and Nowak 2020) | - | - | | AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning(Li et al. 2020) | Outlier Detection
Imitation Learning | - | | Reinforcement Learning Aided Network Architecture Generation for JPEG Image Steganalysis(Yang et al. 2020)
accepted at Proceedings of the 2020 ACM Workshop on Information Hiding and Multimedia Security | Image Steganalysis | - | | Neural Architecture Search for Time Series Classification(Rakhshani et al. 2020)
accepted at ijcnn 2020 | Time Series | - | | Cyclic Differentiable Architecture Search(Yu et al. 2020) | Search Strategy | Github | | Differentially-private Federated Neural Architecture Search(Singh et al. 2020) | Federated Learning | Github | | DrNAS: Dirichlet Neural Architecture Search(Chen et al. 2020) | Search Strategy | Github | | Neural Architecture Optimization with Graph VAE(Li et al. 2020) | Estimation Strategy
VAE
GNN | - | | Fine-Grained Stochastic Architecture Search(Chaudhuri et al. 2020) | Search Strategy | - | | Bonsai-Net: One-Shot Neural Architecture Search via Differentiable Pruners(Geada et al. 2020) | Search Strategy | Github | | AlphaGAN: Fully Differentiable Architecture Search for Generative Adversarial Networks(Tian et al. 2020) | GAN
DARTS | Github | | Fine-Tuning DARTS for Image Classification(Tanveer et al. 2020) | DARTS | - | | Neural Anisotropy Directions(Ortiz-Jiménez et al. 2020) | - | - | | CryptoNAS: Private Inference on a ReLU Budget(Ghodsi et al. 2020) | Private Inference | - | | Heuristic Architecture Search Using Network Morphism for Chest X-Ray Classification(Radiuk and Kutucu 2020) | Medical
Network Morphism | - | | Task-aware Performance Prediction for Efficient Architecture Search(Kokiopoulou et al. 2020)
accepted at ECAI 2020 | Estimation Strategy | - | | Beyond Network Pruning: a Joint Search-and-Training Approach(Lu et al. 2020)
accepted at IJCAI 2020 | Multi-objective Search | - | | Neural Ensemble Search for Performant and Calibrated Predictions(Zaidi et al. 2020) | Ensemble | - | | Multi-fidelity Neural Architecture Search with Knowledge Distillation(Trofimov et al. 2020) | Estimation Strategy | - | | Differentiable Neural Architecture Transformation for Reproducible Architecture Improvement(Kim et al. 2020) | - | - | | Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search(Nguyen et al. 2020) | Search Strategy | - | | Neural Architecture Search using Bayesian Optimisation with Weisfeiler-Lehman Kernel(Ru et al. 2020) | Search Strategy | - | | NAS-Bench-NLP: Neural Architecture Search Benchmark for Natural Language Processing(Klyuchnikov et al. 2020) | NLP
Benchmark | - | | Few-shot Neural Architecture Search(Zhao et al. 2020) | Estimation Strategy | - | | NADS: Neural Architecture Distribution Search for Uncertainty Awareness(Ardywibowo et al. 2020) | - | - | | Towards Efficient Automated Machine Learning(Li 2020) | Survey | - | | AMER: Automatic Behavior Modeling and Interaction Exploration in Recommender System(Zhao et al. 2020) | CTR | - | | Neuroevolution in Deep Neural Networks: Current Trends and Future Challenges(Galvan and Mooney 2020) | Survey | - | | AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks(Fu et al. 2020)
accepted at ICML 2020 | GAN
Image Translator | Github | | Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?(Yan et al. 2020) | Unsupervised
Search Strategy | - | | Hardware-Aware Transformable Architecture Search with Efficient Search Space(Jiang et al. 2020)
accepted at accpeted at ICME 2020 | Search Space
Multi-objective Search | - | | Sparse CNN Archtitecture Search(Yeshwanth et al. 2020)
accepted at ICME 2020 | - | - | | Auto-Generating Neural Networks with Reinforcement Learning for Multi-Purpose Image Forensics(Wei et al. 2020)
accepted at ICME 2020 | Image Forensics | - | | Neural Architecture Search without Training(Mellor et al. 2020) | Estimation Strategy | Github | | Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search(Ru et al. 2020) | Estimation Strategy | - | | Differentiable Neural Input Search for Recommender Systems(Cheng et al. 2020) | CTR | - | | Efficient Architecture Search for Continual Learning(Gao et al. 2020) | Continual Learning | - | | Conditional Neural Architecture Search(Kao et al. 2020) | Search Strategy
GAN | - | | AutoHAS: Differentiable Hyper-parameter and Architecture Search(Dong et al. 2020) | - | - | | Modeling Task-based fMRI Data via Deep Belief Network with Neural Architecture Search(Qiang et al. 2020)
accepted at Computerized Medical Imaging and Graphics | Medical
Deep Belief Network | - | | Fast Hardware-Aware Neural Architecture Search(Zhang et al. 2020)
accepted at CVPR 2020 workshop | Multi-objective Search | - | | Memory-Efficient Hierarchical Neural Architecture Search for Image Denoising(Zhang et al. 2020)
accepted at CVPR 2020 | Image Translator | | | GP-NAS: Gaussian Process based Neural Architecture Search(Li et al. 2020)
accepted at CVPR 2020 | Search Strategy | - | | MemNAS: Memory-Efficient Neural Architecture Search with Grow-Trim Learning(Liu et al.2020)
accepted at CVPR 2020 | Multi-objective Search | Github | | Can weight sharing outperform random architecture search? An investigation with TuNAS(Bender et al. 2020)
accepted at CVPR 2020 | Estimation Strategy | - | | Butterfly Transform: An Efficient FFT Based Neural Architecture Design(Alizadeh vahid et al. 2020)
accepted at CVPR 2020 | Model Compression | Github | | APQ: Joint Search for Network Architecture, Pruning and Quantization Policy(Wang et al.2020)
accepted at CVPR 2020 | Multi-objective Search | Github | | SP-NAS: Serial-to-Parallel Backbone Search for Object Detection(Jiang et al. 2020)
accepted at CVPR 2020 | Object Detection | - | | All in One Bad Weather Removal using Architectural Search(Li et al. 2020)
accepted at CVPR 2020 | Image Translator | - | | NeuralScale: Efficient Scaling of Neurons for Resource-Constrained Deep Neural Networks(Lee and Lee)
accepted at CVPR 2020 | Model Compression | Github | | On Network Design Spaces for Visual Recognition(Radosavovic et al. 2020)
accepted at ICCV 2019 | Image Recognition | Github | | A Comprehensive Survey of Neural Architecture Search: Challanges and Solutions(Ren et al. 2020) | Survey | - | | FBNetV3: Joint Architecture-Recipe Search using Neural Acquisition Function(Dai et al. 2020) | - | - | | Neural Architecture Search With Reinforce And Masked Attention Autoregressive Density Estimators(Krishna et al. 2020) | Search Strategy | - | | Automation of Deep Learning – Theory and Practice(Wistuba et al. 2020)
accepted at ICMR 2020 | Survey | - | | AdaEn-Net: An Ensemble of Adaptive 2D-3D Fully Convolutional Networks for Medical Image Segmentation(Baldeon Calisto and Lai-Yuen. 2020)
accepted at Neural Networks | Medical
Image Segmentation | - | | DC-NAS: Divide-and-Conquer Neural Architecture Search(Wang et al. 2020) | Search Strategy | - | | HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens(Yang et al. 2020) | - | - | | Designing Resource-Constrained Neural Networks Using Neural Architecture Search Targeting Embedded Devices(Cassimon et al. 2020)
accepted at IEEE Internet of Things | Multi-objective Search | - | | Searching Better Architectures for Neural Machine Translation(Fan et al. 2020)
accepted at IEEE/ACM Transactions on Audio, Speech, and Language Processing | Language Modeling
Machine Translation | - | | Automated Design of Neural Network Architectures with Reinforcement Learning for Detection of Global Manipulations(Chen et al. 2020)
accepted at IEEE Journal of Selected Topics in Signal Processing | Object Detection | - | | A New Deep Neural Architecture Search Pipeline for Face Recognition(Zhu et al. 2020)
accepted at IEEE Access | Face Recognition
| - | | Regularized Evolution for Marco Neural Architecture Search(Kyriakides and Margaritis)
accepted at AIAI2020 | Search Strategy | - | | Evolutionary NAS with Gene Expression Programming of Cellular Encoding(Broni-Bediako et al. 2020) | - | - | | Synthetic Petri Dish: A Novel Surrogate Model for Rapid Architecture Search(Rawal et al. 2020) | Search Strategy
Estimation Strategy | Github | | Designing Convolutional Neural Network Architectures Using Cartesian Genetic Programming(Suganuma et al. 2020)
accepted at accepted in book on “Deep Neural Evolution” | Search Strategy | - | | An Introduction to Neural Architecture Search for Convolutional Networks(Kyriakides and Margaritis, 2020) | Survey | - | | AutoSegNet: An Automated Neural Network for Image Segmentation(Xu et al. 2020)
accepted at IEEE Access | Medical
Image Segmentation | - | | DMS: Differentiable Dimension Search for Binary Neural Networks(Li et al. 2020)
accepted at 1st Workshop on Neural Architecture Search at ICLR 2020 | Search Strategy
Binary Networks | - | | Evolving Deep Neural Networks for X-ray Based Detection of Dangerous Objects(Tsukada et al. 2020)
accepted at accepted in book on “Deep Neural Evolution” | Medical | - | | Powering One-shot Topological NAS with Stabilized Share-parameter Proxy(Guo et al. 2020) | - | - | | Optimize CNN Model for FMRI Signal Classification Via Adanet-Based Neural Architecture Search(Dai et al. 2020)
accepted at IEEE ISBI | Medical | - | | Rethinking Performance Estimation in Neural Architecture Search(Zheng et al. 2020)
accepted at CVPR 2020 | Estimation Strategy | Github | | Application of a genetic algorithm to search for the optimal convolutional neural network architecture with weight distribution(Radiuk 2020) | - | - | | HNAS: Hierarchical Neural Architecture Search on Mobile Devices(Xia et al. 2020) | Search Strategy
Model Compression | - | | Improving Neuroevolution Using Island Extinction And Repopulation(Lyu et al. 2020) | Time Series
Evolutionary | - | | You Only Search Once: A Fast Automation Framework for Single-Stage DNN/Accelerator Co-design(Chen et al. 2020) | Multi-objective Search | - | | DARTS-ASR: Differentiable Architecture Search for Multilingual Speech Recognition and Adaptation(Chen et al. 2020)
accepted at INTERSPEECH 2020 | Speech Recognition
DARTS | - | | A Semi-Supervised Assessor of Neural Architectures(Tang et al. 2020)
accepted at CVPR 2020 | Estimation Strategy
GNN | - | | Neural Architecture Search for Gliomas Segmentation on Multimodal Magnetic Resonance Imaging(Wang et al. 2020) | Medical | - | | Binarizing MobileNet via Evolution-based Searching(Phan et al. 2020) | Model Compression | - | | Neural Architecture Transfer(Lu et al. 2020) | Transfer Learning
Evolutionary | Github | | Optimization of deep neural networks: a survey and unified taxonomy(Talbi 2020) | Survey | - | | Auto-Fas: Searching Lightweight Networks for Face Anti-Spoofing(Yu et al. 2020)
accepted at accetped at ICASSP 2020 | Face Anti-spoofing
Model Compression | - | | Neuro Evolutional with Game-Driven Cultural Algorithms(Waris and Reynolds 2020)
accepted at ACM GECCO 2020 | Game Playing
Search Strategy | - | | NASIL: Neural Architecture Search With Imitation Learning(Fard et al. 2020)
accepted at ICASSP 2020 | Imitation Learning
Search Strategy | - | | Noisy Differentiable Architecture Search(Chu et al. 2020) | Search Strategy | Github | | AutoSpeech: Neural Architecture Search for Speaker Recognition(Ding et al. 2020) | Speech Recognition | Github | | Learning Architectures from an Extended Search Space for Language Modeling(Li et al. 2020)
accepted at ACL 2020 | Language Modeling
Search Space | - | | CP-NAS: Child-Parent Neural Architecture Search for 1-bit CNNs( Zhuo et al. 2020) | Model Compression
DARTS | - | | Particle Swarm Optimization for Evolving Deep Convolutional Neural Networks for Image Classification: Single- and Multi-Objective Approaches(Wang et al. 2020)
accepted at accepted in book on “Deep Neural Evolution” | Search Strategy | - | | Optimizing Neural Architecture Search using Limited GPU Time in a Dynamic Search Space: A Gene Expression Programming Approach(Alves and de Oliveira. 2020)
accepted at IEEE CEC | Search Strategy | Github | | Local Search is State of the Art for Neural Architecture Search Benchmarks(White et al. 2020)
accepted at AutoML workshop at ICML’20 | Search Strategy | Github | | SIPA: A Simple Framework for Efficient Networks(Lee et al. 2020) | - | - | | The effect of reduced training in neural architecture search(Kyriakides and Margaritis. 2020)
accepted at Neural Comput & Applic | - | - | | Efficient Evolutionary Neural Architecture Search (NAS) by Modular Inheritable Crossover(Tan et al. 2020)
accepted at BIC-TA’20 | Evolutionary | - | | MobileDets: Searching for Object Detection Architectures for Mobile Accelerators( Xiong et al. 2020) | Object Detection
Model Compression | - | | Angle-based Search Space Shrinking for Neural Architecture Search(Hu et al. 2020) | - | - | | AutoHR: A Strong End-to-end Baseline for Remote Heart Rate Measurement with Neural Searching(Yu et al. 2020) | Medical | - | | Deep Multimodal Neural Architecture Search(Yu et al. 2020) | Multimodal Learning
| - | | Depth-Wise Neural Architecture Search(Jordao et al. 2020) | - | - | | Recurrent Neural Network Architecture Search for Geophyiscal Emulation(Maulik et al. 2020) | Emulators
Simulation
Evolutionary | - | | Local Search is a Remarkably Strong Baseline for Neural Architecture Search(Ottelander et al. 2020) | - | - | | Superkernel Neural Architecture Search for Image Denoising(Mozejko et al. 2020)
accepted at NTIRE2020 Workshop at CVPR 2020 | Image Denoising
Unsupervised | - | | Organ at Risk Segmentation for Head and Neck Cancer using Stratified Learning and Neural Architecture Search(Guo et al. 2020) | Medical
DARTS | - | | Fitting the Search Space of Weight-sharing NAS with Graph Convolutional Networks(Chen et al. 2020) | - | - | | A Neural Architecture Search based Framework for Liquid State Machine Design(Tian et al. 2020) | - | - | | Geometry-Aware Gradient Algorithms for Neural Architecture Search(Li et al. 2020) | - | - | | Distributed Evolution of Deep Autoencoders(Hajewski et al. 2020) | Distributed System
Evolutionary | - | | FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions(Wan et al. 2020) | - | - | | ModuleNet: Knowledge-inherited Neural Architecture Search(Chen et al. 2020) | - | - | | Evolutionary recurrent neural network for image captioning(Wang et al. 2020)
accepted at Neurocomputing | Image Captioning
Cross-modal
Evolutionary | - | | Neural Architecture Search for Lightweight Non-Local Networks(Li et al. 2020) | - | - | | A Generic Graph-based Neural Architecture Encoding Scheme for Predictor-based NAS(Ning et al. 2020) | GNN
Predictor-based | - | | FedNAS: Federated Deep Learning via Neural Architecture Search(He et al. 2020)
accepted at CVPR 2020 Workshop on Neural Architecture Search and Beyond for Representation Learning | Federated Learning | Github | | An Evolutionary Approach to Variational Autoencoders(Hajewski and Oliveira. 2020)
accepted at CCWC’20 | Variational Autoencoder
Unsupervised
Evolutionary | - | | A Scalable System for Neural Architecture Search(Hajewski and Oliveira. 2020)
accepted at CCWC’20 | Distributed System | - | | Neural Architecture Generator Optimization(Ru et al. 2020) | - | - | | Deep-n-Cheap: An Automated Search Framework for Low Complexity Deep Learning(Dey et al. 2020) | Bayesian Optimization | Github | | MTL-NAS: Task-Agnostic Neural Architecture Search towards General-Purpose Multi-Task Learning(Gao et al. 2020)
accepted at CVPR’20 | Multi-Task Learning
One-shot
Gradient-based | Github | | Designing Network Design Spaces(Radosavovic et al. 2020)
accepted at CVPR’20 | - | - | | Disturbance-immune Weight Sharing for Neural Architecture Search(Niu et al. 2020) | - | - | | NPENAS:Neural Predictor Guided Evolution for Neural Architecture Search(Wei et al. 2020) | - | - | | DA-NAS: Data Adapted Pruning for Efficient Neural Architecture Search(Dai et al. 2020) | - | - | | MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation(He et al. 2020)
accepted at CVPR’20 | MiLeNAS | Github | | Are Labels Necessary for Neural Architecture Search?(Liu et al. 2020) | Unsupervised
DARTS | Github | | DCNAS: Densely Connected Neural Architecture Search for Semantic Image Segmentation(Zhang et al. 2020) | Image Segmentation | - | | Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection(Guo et al. 2020)
accepted at CVPR 2020 | Object Detection
DARTS | Github | | Sampled Training and Node Inheritance for Fast Evolutionary Neural Architecture Search(Zhang et al. 2020) | Evolutionary | - | | GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernet(You et al. 2020)
accepted at CVPR’2020 | GreedyNAS | - | | BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage Models(Yu et al. 2020) | - | - | | Steepest Descent Neural Architecture Optimization: Escaping Local Optimum with Signed Neural Splitting(Wu et al. 2020) | - | - | | BS-NAS: Broadening-and-Shrinking One-Shot NAS with Searchable Numbers of Channels(Shen et al. 2020) | - | - | | Probabilistic Dual Network Architecture Search on Graphs(Zhao et al. 2020) | GNN
Gradient-based | - | | GAN Compression: Efficient Architectures for Interactive Conditional GAN(Li et al. 2020) | GAN
Model Compression
One-shot | Github | | ElixirNet: Relation-aware Network Architecture Adaptation for Medical Lesion Detection(Jiang et al. 2020) | Medical
Lesion Detection | - | | Lifelong Learning with Searchable Extension Units(Wang et al. 2020) | Lifelong Learning | - | | Efficient Backbone Search for Scene Text Recognition(Zhang et al. 2020) | Scene Text Recognition | - | | AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data(Erickson et al. 2020) | Structured Data | - | | PONAS: Progressive One-shot Neural Architecture Search for Very Efficient Deployment(Huang and Chu. 2020) | - | - | | Hierarchical Neural Architecture Search for Single Image Super-Resolution(Guo et al. 2020) | Image Translator | - | | How to Train Your Super-Net: An Analysis of Training Heuristics in Weight-Sharing NAS(Yu et al. 2020) | - | - | | AutoML-Zero: Evolving Machine Learning Algorithms From Scratch(Real et al. 2020)
accepted at ICML 2020 | AutoML | Github | | Accelerator-Aware Neural Network Design Using AutoML(Gupta and Akin. 2020)
accepted at On-device Intelligence Workshop at MLSys’20 | Model Compression | - | | Real-time Federated Evolutionary Neural Architecture Search(Zhu and Jin. 2020) | Federated Learning
Evolutionary | - | | BATS: Binary ArchitecTure Search(Bulat et al. 2020)
accepted at ECCV’20 | Binary Networks
DARTS | Github | | ADWPNAS: Architecture-Driven Weight Prediction for Neural Architecture Search(Zhang et al. 2020) | - | - | | NAS-Count: Counting-by-Density with Neural Architecture Search(Hu et al. 2020) | - | - | | ImmuNetNAS: An Immune-network approach for searching Convolutional Neural Network Architectures(Kefan and Pang. 2020) | - | - | | Neural Inheritance Relation Guided One-Shot Layer Assignment Search(Meng et al. 2020) | - | - | | Automatically Searching for U-Net Image Translator Architecture(Shu and Wang. 2020) | Image Translator
Evolutionary | - | | AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations(Zhao et al. 2020) | CTR
DARTS | - | | Memory-Efficient Models for Scene Text Recognition via Neural Architecture Search(Hong et al. 2020)
accepted at WACV’20 workshop | Scene Text Recognition
Image Recognition
ProxylessNAS | - | | Search for Winograd-Aware Quantized Networks(Fernandez-Marques et al. 2020) | Model Compression
Winograd
ProxylessNAS | - | | Semi-Supervised Neural Architecture Search(Luo et al. 2020) | SemiNAS
NAO | Github | | Neural Architecture Search for Compressed Sensing Magnetic Resonance Image Reconstruction(Yan et al. 2020) | Medical
Magnetic Resonance Imaging
DARTS | Github | | DSNAS: Direct Neural Architecture Search without Parameter Retraining(Hu et al. 2020) | DSNAS | Github | | Neural Architecture Search For Fault Diagnosis(Li et al. 2020)
accepted at ESREL’20 | Fault Diagnosis
RL
Controller-based | - | | Learning Architectures for Binary Networks(Kim et al. 2020)
accepted at ECCV’20 | Binary Networks
DARTS | - | | Efficient Evolutionary Architecture Search for CNN Optimization on GTSRB(Johner and Wassner. 2020)
accepted at ICMLA’19 | Model Compression
Evolutionary | - | | Automating Deep Neural Network Model Selection for Edge Inference(Lu et al. 2020)
accepted at CogMI’20 | Model Compression | - | | Neural Architecture Search over Decentralized Data(Xu et al. 2020) | Federated Learning
| - | | Automatic Structural Search for Multi-task Learning VALPs(Garciarena et al. 2020)
accepted at OLA’20 | Multi-task Learning | - | | RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning(Alletto et al. 2020)
accepted at Meta-Eval 2020 workshop | Multimodal Learning | - | | Stabilizing Differentiable Architecture Search via Perturbation-based Regularization(Chen and Hsieh. 2020) | DARTS | - | | Best of Both Worlds: AutoML Codesign of a CNN and its Model Compression(Abdelfattah et al. 2020)
accepted at DAC’20 | Model Compression
RL
| - | | Co-Exploration of Neural Architectures and Heterogeneous ASIC Accelerator Designs Targeting Multiple Tasks(Yang et al. 2020)
accepted at DAC’20 | Model Compression
ASIC
RL
Controller-based | - | | FPNet: Customized Convolutional Neural Network for FPGA Platforms(Yang et al. 2020)
accepted at FPT’20 | Model Compression
FPGA
RL
Controller-based | - | | AutoFCL: Automatically Tuning Fully Connected Layers for Transfer Learning(Basha et al. 2020) | Transfer Learning
CV
Bayesian Optimization | - | | NASS: Optimizing Secure Inference via Neural Architecture Search(Bian et al. 2020)
accepted at ECAI’20 | Secure Inference
Privacy
Controller-based| - | | Search for Better Students to Learn Distilled Knowledge(Gu et al. 2020)
accepted at ECAI'20 | Model Compression
Knowledge Distillation
DARTS | - | | Bayesian Neural Architecture Search using A Training-Free Performance Metric(Camero et al. 2020) | RNN
Bayesian Optimization | - | | NAS-Bench-1Shot1: Benchmarking and Dissecting One-Shot Neural Architecture Search(Zela et al. 2020)
accepted at ICLR’20 | Benchmark
One-shot | - | | Convolution Neural Network Architecture Learning for Remote Sensing Scene Classification(Chen et al. 2010) | Remote Sensing
RL | - | | Multi-objective Neural Architecture Search via Non-stationary Policy Gradient(Chen et al. 2020) | RL
Controller-based | - | | Efficient Neural Architecture Search: A Broad Version(Ding et al. 2020) | ENAS | - | | ENAS U-Net: Evolutionary Neural Architecture Search for Retinal Vessel(Fan et al. 2020) | Medical
Image Segmentation
Evolutionary | - | | FlexiBO: Cost-Aware Multi-Objective Optimization of Deep Neural Networks(Iqbal et al. 2020) | Multi-objective Search
Bayesian Optimization | Github | | Up to two billion times acceleration of scientific simulations with deep neural architecture search(Kasim et al. 2020) | Scientific Simulations
ProxylessNAS | - | | Latency-Aware Differentiable Neural Architecture Search(Xu et al. 2020) | DARTS | - | | MixPath: A Unified Approach for One-shot Neural Architecture Search(Chu et al. 2020) | One-shot | - | | Neural Architecture Search for Skin Lesion Classification(Kwasigroch et al. 2020)
accepted at IEEE Access | Medical
Image Classification
Network Morphism | - | | AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search(Chen et al. 2020) | BERT
Model Compression
NLP
DARTS | - | | Neural Architecture Search for Deep Image Prior(Ho et al. 2020) | Image Denoising
Image Inpainting
Image Super-resolution
Evolutionary | - | | Fast Neural Network Adaptation via Parameter Remapping and Architecture Search(Fang et al. 2020)
accepted at ICLR’20 | Object Detection
Image Segmentation
DARTS | Github | | FTT-NAS: Discovering Fault-Tolerant Neural Architecture(Li et al. 2020)
accepted at ASP-DAC 2020 | Multi-objective Search
RL | - | | Deeper Insights into Weight Sharing in Neural Architecture Search(Zhang et al. 2020) | Survey
Weight Sharing
One-shot | - | | EcoNAS: Finding Proxies for Economical Neural Architecture Search(Zhou et al. 2020)
accepted at CVPR’20 | Evolutionary | - | | DeepMaker: A multi-objective optimization framework for deep neural networks in embedded systems(Loni et al. 2020)
accepted at Microprocessors and Microsystems | Multi-objective Search
Evolutionary | - | | Auto-ORVNet: Orientation-boosted Volumetric Neural Architecture Search for 3D Shape Classification(Ma et al. 2020)
accepted at IEEE Access | 3D Deep learning
DARTS | - | | NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search(Dong and Yang et al. 2020)
accepted at ICLR’20 | Benchmark | Github |

2019

| Title | Tags | Code | |:--------|:--------:|:--------:| | Scalable NAS with Factorizable Architectural Parameters(Wang et al. 2019) | - | - | | Modeling Neural Architecture Search Methods for Deep Networks(Malekhosseini et al. 2019) | - | - | | Searching for Stage-wise Neural Graphs in the Limit(Zhou et al. 2019) | - | - | | Neural Architecture Search on Acoustic Scene Classification(Li et al. 2019) | - | - | | RC-DARTS: Resource Constrained Differentiable Architecture Search(Jin et al. 2019) | - | - | | NAS Evaluation is frustatingly hard(Yang et al. 2019)
accepted at ICLR’20 | - | - | | A Genetic Algorithm based Kernel-size Selection Approach for a Multi-column Convolutional Neural Network(Singh et al. 2019) | - | - | | BetaNAS: Balanced Training and Selective Drop for Neural Architecture Search(Fang et al. 2019) | - | - | | Progressive DARTS: Bridging the Optimization Gap for NAS in the Wild(Chen et al. 2019) | - | - | | TextNAS: A Neural Architecture Search Space tailored for Text Representation(Wang et al. 2019) | - | - | | AtomNAS: Fine-Grined End-To-End Neural Architecture Search(Mei et al. 2019)
accepted at ICLR’20 | - | - | | C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation(Yu et al. 2019) | Medical
Image Segmentation | - | | A Reinforcement Neural Architecture Search Method for Rolling Bearing Fault Diagnosis(Wang et al. 2019)
accepted at Measurement | - | - | | Neural Architecture Search for Optimizing Deep Belief Network Models of fMRI Data(Quiang et al. 2019)
accepted at MMMI’19 | - | - | | QoS-aware Neural Architecture Search(Cheng et al. 2019)
accepted at NeurIPS’19 | - | - | | Neural-Hardware Architecture Search(Lin et al. 2019)
accepted at NeurIPS’19 | - | - | | Preventing Information Leakage with Neural Architecture Search(Zhang et al. 2019) | - | - | | Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data(Such et al. 2019) | - | - | | UNAS: Differentiable Architecture Search Meets Reinforcement Learning(Vahdat et al. 2019) | - | - | | Efficient network architecture search via multiobjective particle swarm optimization based on decomposition(Jiang et al. 2019) | - | - | | Deep Uncertainty Estimation for Model-based Neural Architecture Search(White et al. 2019)
accepted at workshop on Bayesian Deep Learning at NeurIPS’19 | - | - | | A Variational-Sequential Graph Autoencoder for Neural Architecture Performance Prediction(Friede et al. 2019) | - | - | | STEERAGE: Synthesis of Neural Networks Using Architecture Search and Grow-and-Prune Methods(Hassantabar et al. 2019) | - | - | | Leveraging End-to-End Speech Recognition with Neural Architecture Search(Baruwa et al. 2019) | - | - | | Efficient Differentiable Neural Architecture Search with Meta Kernels(Chen et al. 2019) | - | - | | Neural architecture search for image saliency fusion(Bianco et al. 2019)
accepted at Information Fusion | - | - | | Ultrafast Photorealistic Style Transfer via Neural Architecture Search(An et al. 2019) | - | - | | AdversarialNAS: Adversarial Neural Architecture Search for GANs(Gao et al. 2019) | - | - | | MetAdapt: Meta-Learned Task-Adaptive Architecture for Few-Shot Classification(Doveh et al. 2019) | - | - | | SGAS: Sequential Greedy Architecture Search(Li et al. 2019)
accepted at CVPR’20 | - | - | | Blockwisely Supervised Neural Architecture Search with Knowledge Distillation(Li et al. 2019) | - | - | | Towards Oracle Knowledge Distillation with Neural Architecture Search(Kang et al. 2019) | - | - | | AutoML for Architecting Efficient and Specialized Neural Networks(Cai et al. 2019)
accepted at IEEE Micro | - | - | | Artificial Neural Network and Accelerator Co-design using Evolutionary Algorithms(Colangelo et al. 2019)
accepted at HPEC’19 | - | - | | Auto-creation of Effective Neural Network Architecture by Evolutionary Algorithm and ResNet for Image Classification(Chen et al. 2019)
accepted at SMC’19 | - | - | | Performance Prediction Based on Neural Architecture Features(Long et al. 2019)
accepted at CCHI’19 | - | - | | Fair DARTS: Eliminating Unfair Advantages in Differentiable Architecture Search(Chu et al. 2019)
accepted at ECCV’20 | - | - | | EDAS: Efficient and Differentiable Architecture Search(Hong et al. 2019) | - | - | | SGAS: Sequential Greedy Architecture Search(Li et al. 2019) | - | - | | Ranking architectures using meta-learning(Dubatovka et al. 2019) | - | - | | Meta-Learning of Neural Architectures for Few-Shot Learning(Elsken et al. 2019) | - | - | | When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks(Guo et al. 2019) | - | - | | Exploiting Operation Importance for Differentiable Neural Architecture Search(Xie et al. 2019) | - | - | | SM-NAS: Structural-to-Modular Neural Architecture Search for Object Detection(Yao et al. 2019) | - | - | | Multi-Objective Neural Architecture Search via Predictive Network Performance Optimization(Shi et al. 2019) | - | - | | Data Proxy Generation for Fast and Efficient Neural Architecture Search(Park. 2019) | - | - | | AutoShrink: A Topology-aware NAS for Discovering Efficient Neural Architecture(Zhang et al. 2019) | - | - | | Search to Distill: Pearls are Everywhere but not the Eyes(Liu et al. 2019) | - | - | | EfficientDet: Scalable and Efficient Object Detection(EfficientDet: Scalable and Efficient Object Detection) | - | - | | Periodic Spectral Ergodicity: A Complexity Measure for Deep Neural Networks and Neural Architecture Search(Süzen et al. 2019) | - | - | | IMMUNECS: Neural Committee Search by an Artificial Immune System(IMMUNECS: Neural Committee Search by an Artificial Immune System) | - | - | | NAIS: Neural Architecture and Implementation Search and its Applications in Autonomous Driving(Hao et al. 2019) | - | - | | Neural Recurrent Structure Search for Knowledge Graph Embedding(Zhang et al. 2019) | - | - | | S2DNAS: Transforming Static CNN Model for Dynamic Inference via Neural Architecture Search(Yuan et al. 2019) | - | - | | Automatic Design of CNNs via Differentiable Neural Architecture Search for PolSAR Image Classification(Dong et al. 2019) | - | - | | Enhancing Neural Architecture Search with Speciation and Inter-Epoch Crossover(Baughmann and Wozniak. 2019)
accepted at Supercomputing’19 | - | - | | RAPDARTS: Resource-Aware Progressive Differentiable Architecture Search(Green et al. 2019) | - | - | | AutoPrune: Automatic Network Pruning by Regularizing Auxiliary Parameters(Xiao et al. 2019)
accepted at NeurIPS’19 | - | - | | DATA: Differentiable ArchiTecture Approximation(Chang et al. 2019)
accepted at NeurIPS’19 | - | - | | Learning to reinforcement learn for Neural Architecture Search(Robles and Vanschoren. 2019) | - | - | | An Automated Approach for Developing a Convolutional Neural Network Using a Modified Firefly Algorithm for Image Classification(Sharaf ad Radwan. 2019)
accepted at accepted book chapter | - | - | | ENAS Oriented Layer Adaptive Data Scheduling Strategy for Resource Limited Hardware(Li et al. 2019)
accepted at Neurocomputing Journal | - | - | | Improved Differentiable Architecture Search for Language Modeling and Named Entity Recognition(Jiang et al. 2019)
accepted at EMNLP-IJCNLP’19 | - | - | | Device-Circuit-Architecture Co-Exploration for Computing-in-Memory Neural Accelerators(Jiang et al. 2019) | - | - | | On Neural Architecture Search for Resource-Constrained Hardware Platforms(Lu et al. 2020)
accepted at ICCAD’19 | - | - | | NAT: Neural Architecture Transformer for Accurate and Compact Architectures(Guo et al. 2019) | - | - | | Deep neural network architecture search using network morphism(Kwasigroch et al. 2019)
accepted at accepted MMAR’19 | - | - | | Person Re-identification with Neural Architecture Search(Zhang et al. 2019)
accepted at accepted PRCV’19 | - | - | | Resource Constrained Neural Network Architecture Search: Will a Submodularity Assumption Help?(Xiong et al. 2019)
accepted at ICCV’19 | - | - | | Auto-FPN: Automatic Network Architecture Adaptation for Object Detection Beyond Classification(Xu et al. 2019)
accepted at ICCV’19 | Object Detection | - | | BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search(White et al. 2019) | - | - | | Stabilizing DARTS with Amended Gradient Estimation on Architectural Parameters(Bi et al. 2019) | - | - | | An End-to-End HW/SW Co-Design Methodology to Design Efficient Deep Neural Network Systems using Virtual Models(Klaiber et al. 2019) | - | - | | Hardware-aware one-short Neural Architecture Search in Coordinate Ascent Framework(Hardware-aware one-short Neural Architecture Search in Coordinate Ascent Framework) | - | - | | Efficient Structured Pruning and Architecture Searching for Group Convolution(Zhao and Luk. 2019)
accepted at ICCV’19 workshop | - | - | | On-Device Image Classification with Proxyless Neural Architecture Search and Quantization-Aware Fine-tuning(Cai et al. 2019)
accepted at ICCV’19 workshop | - | - | | MSNet: Structural Wired Neural Architecture Search for Internet of Things(Cheng et al. 2019)
accepted at ICCV’19 workshop | - | - | | Efficient Decoupled Neural Architecture Search by Structure and Operation Sampling(Lee et al. 2019) | - | - | | Using Neural Architecture Search to Optimize Neural Networks for Embedded Devices(Cassimon et al. 2019)
accepted at 3PGCIC’19 | - | - | | NASIB: Neural Architecture Search withIn Budget(Singh et al. 2019) | - | - | | State of Compact Architecture Search For Deep Neural Networks(Shafiee et al. 2019) | - | - | | One-Shot Neural Architecture Search via Self-Evaluated Template Network(Dong and Yang. 2019) | - | - | | Scalable Neural Architecture Search for 3D Medical Image Segmentation(Kim et al. 2019)
accepted at MICCAI’19 | Medical
Image Segmentation | - | | Neural Architecture Search for Adversarial Medical Image Segmentation(Dong et al. 2019)
accepted at MICCAI’19 | Medical
Image Segmentation | - | | Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation(Yang et al. 2019)
accepted at MICCAI’19 | Medical
Image Segmentation | - | | Identify Hierarchical Structures from Task-Based fMRI Data via Hybrid Spatiotemporal Neural Architecture Search Net(Zhang et al. 2019)
accepted at MICCAI’19 | - | - | | Energy-aware Neural Architecture Optimization with Fast Splitting Steepest Descent(Wang et al. 2019)
accepted at accepted EMC2 workshop’19 | - | - | | Improving one-shot NAS by Surppressing the Posterior Fading(Li et al. 2019) | - | - | | Splitting Steepest Descent for Growing Neural Architectures(Liu et al. 2019) | - | - | | A Novel Automatic CNN Architecture Design Approach Based on Genetic Algorithm(Ahmed et al. 2019)
accepted at AISI’19 | - | - | | RNAS: Architecture Ranking for Powerful Networks(Xu et al. 2019) | - | - | | Towards Unifying Neural Architecture Space Exploration and Generalization(Bhardwaj and Marculescu) | - | - | | Sub-Architecture Ensemble Pruning in Neural Architecture Search(Bia et al. 2019) | - | - | | Towards modular and programmable architecture search(Negrinho et al. 2019)
accepted at NeurIPS’19 | - | - | | Automated design of error-resilient and hardware-efficient deep neural networks(Schorn et al. 2019) | - | - | | STACNAS: Towards Stable and Consistent Optimization for Differentiable Neural Architecture Search(Guilin et al. 2019) | - | - | | Efficient Residual Dense Block Search for Image Super-Resolution(Song et al. 2019) | - | - | | Understanding and Improving One-shot Neural Architecture Optimization(Luo et al. 2019) | - | - | | Scheduled Differentiable Architecture Search for Visual Recognition(Qui et al. 2019) | - | - | | Understanding and Robustifying Differentiable Architecture Search(Zela et al. 2019)
accepted at ICLR’20 | - | - | | Genetic Neural Architecture Search for automatic assessment of human sperm images(Miahi et al. 2019) | - | - | | IR-NAS: Neural Architecture Search for Image Restoration(Zhang et al. 2019) | - | - | | Pose Neural Fabrics Search(Yang et al. 2019) | - | - | | SegNAS3D: Network Architecture Search with Derivative-Free Global Optimization for 3D Image Segmentation(Wong and Moradi. 2019) | 3D
Medical
Image Segmentation | - | | DARTS+: Improved Differentiable Architecture Search with Early Stopping(Liang et al. 2019) | - | - | | Searching for Accurate Binary Neural Architectures(Shen et al. 2019)
accepted at ICCV’19 Neural Architects workshop | - | - | | Improving Keyword Spotting and Language Identification via Neural Architecture Search at Scale(Mazzawi et al. 2019)
accepted at INTERSPEECH 2019 | - | - | | Neural Architecture Search for Class-incremental Learning(Huang et al. 2019) | - | - | | Graph-guided Architecture Search for Real-time Semantic Segmentation(Lin et al. 2019) | Image Segmentation | - | | CARS: Continuous Evolution for Efficient Neural Architecture Search(Yang et al. 2019)
accepted at CVPR’20 | - | - | | Bayesian Optimization of Neural Architectures for Human Activity Recognition(Osmani and Hamidi. 2019)
accepted at Human Activity Sensing | - | - | | Compute-Efficient Neural Network Architecture Optimization by a Genetic Algorithm(Litzinger et al. 2019)
accepted at ICANN’19 | - | - | | Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study(Faes et al. 2019)
accepted at The Lancet Digital Health | - | - | | A greedy constructive algorithm for the optimization of neural network architectures(Pasini et al. 2019) | - | - | | Differentiable Mask Pruning for Neural Networks(Ramakrishnan et al. 2019) | - | - | | Neural Architecture Search in Embedding Space(Liu. 2019) | - | - | | Auto-GNN: Neural Architecture Search of Graph Neural Networks(Zhou et al. 2019) | - | - | | Best Practices for Scientific Research on Neural Architecture Search(Lindauer and Hutter. 2019) | - | - | | Efficient Neural Architecture Transformation Search in Channel-Level for Object Detection(Peng et al. 2019) | - | - | | Training compact neural networks via auxiliary overparameterization(Liu et al. 2019) | - | - | | Rethinking the Number of Channels for Convolutional Neural Networks(Zhu et al. 2019) | - | - | | MANAS: Multi-Agent Neural Architecture Search(Carlucci et al. 2019) | - | - | | Resource Optimized Neural Architecture Search for 3D Medical Image Segmentation(Bae et al. 2019)
accepted at MICCAI’19 | Medical
Image Segmentation | - | | Neural Architecture Search for Joint Optimization of Predictive Power and Biological Knowledge(Zhang et al. 2019) | - | - | | Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research(Balaprakash et al. 2019)
accepted at SC’19 | - | - | | Automatic Neural Network Search Method for Open Set Recognition(Sun et al. 2019)
accepted at ICIP’19 | - | - | | HM-NAS: Efficient Neural Architecture Search via Hierarchical Masking(Yan et al. 2019)
accepted at ICCV’19 Neural Architects Workshop | - | - | | Once for All: Train One Network and Specialize it for Efficient Deployment(Cai et al. 2019) | - | - | | Refactoring Neural Networks for Verification(Shriver et al. 2019) | - | - | | 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 | - | - | | Differentiable Learning-to-Group Channels via Groupable Convolutional Neural Networks(Zhang et al. 2019) | - | - | | SCARLET-NAS: Bridging the gap Between Scalability and Fairness in Neural Architecture Search(Chu et al. 2019) | - | - | | A Novel Encoding Scheme for Complex Neural Architecture Search(Ahmad et al. 2019)
accepted at ITC-CSCC | - | - | | A Graph-Based Encoding for Evolutionary Convolutional Neural Network Architecture Design(Irwin-Harris et al. 2019)
accepted at accepted CEC’19 | - | - | | A Novel Framework for Neural Architecture Search in the Hill Climbing Domain(Verma et al. 2019)
accepted at AIKE’19 | - | - | | Automated Neural Network Construction with Similarity Sensitive Evolutionary Algorithms(Tian et al. 2019) | - | - | | AutoGAN: Neural Architecture Search for Generative Adversarial Networks(Gong et al. 2019)
accepted at ICCV’19 | GAN | Github | | Refining the Structure of Neural Networks Using Matrix Conditioning(Yousefzadeh and O’Leary. 2019) | - | - | | SqueezeNAS: Fast neural architecture search for faster semantic segmentation(Shaw et al. 2019) | Image Segmentation | - | | MoGA: Searching Beyond MobileNetV3(Chu et al. 2019)
accepted at ICASSP’20 | - | - | | Evolving deep neural networks by multi-objective particle swarm optimization for image classification(Wang et al. 2019)
accepted at GECCO’19 | - | - | | Particle Swarm Optimisation for Evolving Deep Neural Networks for Image Classification by Evolving and Stacking Transferable Blocks(Wang et al. 2019)
accepted at IEEE CEC’20 | - | - | | Self-Adaptive 2D-3D Ensemble of Fully Convolutional Networks for Medical Image Segmentation(Calisto and Lai-Yuen. 2019)
accepted at SPIE Medical Imaging’20 | Medical
Image Segmentation | - | | MemNet: Memory-Efficiency Guided Neural Architecture Search with Augment-Trim learning(by Liu et al. 2019) | - | - | | Efficient Novelty-Driven Neural Architecture Search(Zhang et al. 2019) | - | - | | PC-DARTS: Partial Channel Connections for Memory-Efficient Differentiable Architecture Search(Xu et al. 2019) | - | - | | Hardware/Software Co-Exploration of Neural Architectures(Jiang et al. 2019) | - | - | | EPNAS: Efficient Progressive Neural Architecture Search(Zhou et al. 2019) | - | - | | Video Action Recognition via Neural Architecture Searching(Peng et al. 2019) | Video Models | - | | Hardware/Software Co-Exploration of Neural Architectures(Jiang et al. 2019)
accepted at ASP-DAC’20 | - | - | | When Neural Architecture Search Meets Hardware Implementation: from Hardware Awareness to Co-Design(Zhang et al. 2019)
accepted at ISVLSI’19 | - | - | | Reinforcement Learning for Neural Architecture Search: A Review(Jaafra et al. 2019 accepted at Image and Vision Computing) | - | - | | Architecture Search for Image Inpainting(Li and King. 2019. accepted at International Symposium on Neural Networks) | - | - | | Neural Network Architecture Search with Differentiable Cartesian Genetic Programming for Regression(Märtens and Izzo. 2019) | - | - | | FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural Architecture Search(Chu et al. 2019) | - | - | | HyperNOMAD: Hyperparameter optimization of deep neural networks using mesh adaptive direct search(Lakhmiri et al. 2019) | - | - | | Surrogate-Assisted Evolutionary Deep Learning Using an End-to-End Random Forest-based Performance Predictor(Sun et al. 2019)
accepted at accepted by IEEE Transactions on Evolutionary Computation | - | - | | Adaptive Genomic Evolution of Neural Network Topologies(Behjat et al. 2019)
accepted at accepted and presented in ICRA 2019 | - | - | | Densely Connected Search Space for More Flexible Neural Architecture Search(Fang et al. 2019) | - | - | | Posterior-Guided Neural Architecture Search(Zhou et al. 2020)
accepted at AAAI | - | - | | SwiftNet: Using Graph Propagation as Meta-knowledge to Search Highly Representative Neural Architectures(Cheng et al. 2019) | - | - | | Transfer NAS: Knowledge Transfer between Search Spaces with Transformer Agents(Borsos et al. 2019) | - | - | | XNAS: Neural Architecture Search with Expert Advice(Nayman et al. 2019)
accepted at NeurIPS’19 | - | - | | A Study of the Learning Progress in Neural Architecture Search Techniques(Singh et al. 2019) | - | - | | Hardware aware Neural Network Architectures(Srinivas et al. 2019) | - | - | | Sample-Efficient Neural Architecture Search by Learning Action Space(Wang et al. 2019) | - | - | | SwiftNet: Using Graph Propagation as Meta-knowledge to Search Highly Representative Neural Architectures(Cheng et al. 2019) | - | - | | Automatic Modulation Recognition Using Neural Architecture Search(Wei et al. 2019)
accepted at accepted High Performance Big Data and Intelligent Systems | - | - | | Continual and Multi-Task Architecture Search(Pasunuru and Bansal. 2019) | - | - | | AutoGrow: Automatic Layer Growing in Deep Convolutional Networks(Wen et al. 2019) | - | - | | One-Short Neural Architecture Search via Compressing Sensing(Cho et al. 2019) | - | - | | V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation(Zhu et al. 2019) | Medical
Image Segmentation | - | | StyleNAS: An Empirical Study of Neural Architecture Search to Uncover Surprisingly Fast End-to-End Universal Style Transfer Networks(An et al. 2019) | - | - | | Efficient Forward Architecture Search(Hu et al. 2019)
accepted at NeurIPS’19 | - | - | | Differentiable Neural Architecture Search via Proximal Iterations(Yao et al. 2019) | - | - | | Dynamic Distribution Pruning for Efficient Network Architecture Search(Zheng et al. 2019) | - | - | | Particle swarm optimization of deep neural networks architectures for image classification(Fernandes Junior and Yen. 2019. accepted at Swarm and Evolutionary Computation) | - | - | | On Network Design Spaces for Visual Recognition(Radosavovic et al. 2019)
accepted at ICCV’20 | - | - | | AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures(Ryoo et al. 2019) | Video Models | - | | EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks(Tan and Le)
accepted at ICML’19. 2019 | - | - | | Structure Learning for Neural Module Networks(Pahuja et al. 2019) | - | - | | SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers(Fedorov et al. 2019)
accepted at NeurIPS’19 | - | - | | Network Pruning via Transformable Architecture Search(Dong and Yang. 2019)
accepted at NeurIPS’19 | - | - | | DEEP-BO for Hyperparameter Optimization of Deep Networks(Cho et al. 2019) | - | - | | Constrained Design of Deep Iris Networks(Nguyen et al. 2019) | - | - | | Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search(Akimoto et al. 2019)
accepted at ICML’19 | - | - | | Multinomial Distribution Learning for Effective Neural Architecture Search(Zheng et al. 2019) | - | - | | EENA: Efficient Evolution of Neural Architecture(Zhu et al. 2019)
accepted at ICCV’19 Neural Architects Workshop | - | - | | DeepSwarm: Optimising Convolutional Neural Networks using Swarm Intelligence(Byla and Pang. 2019) | - | - | | AutoDispNet: Improving Disparity Estimation with AutoML(Saikia et al. 2019) | - | - | | Online Hyper-parameter Learning for Auto-Augmentation Strategy(Lin et al. 2019) | - | - | | Regularized Evolutionary Algorithm for Dynamic Neural Topology Search(Saltori et al. 2019) | - | - | | Deep Neural Architecture Search with Deep Graph Bayesian Optimization(Ma et al. 2019) | - | - | | Automatic Model Selection for Neural Networks(Laredo et al. 2019) | - | - | | Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization(Klein and Hutter. 2019) | - | - | | BayesNAS: A Bayesian Approach for Neural Architecture Search(Zhou et al. 2019)
accepted at ICML’19 | - | - | | Single-Path NAS: Device-Aware Efficient ConvNet Design(Stamoulis et al. 2019) | - | - | | Automatic Design of Artificial Neural Networks for Gamma-Ray Detection(Assuncao et al. 2019) | - | - | | Neural Architecture Refinement: A Practical Way for Avoiding Overfitting in NAS(Jiang et al. 2019) | - | - | | Fast and Reliable Architecture Selection for Convolutional Neural Networks(Hahn et al. 2019) | - | - | | Differentiable Architecture Search with Ensemble Gumbel-Softmax(Chang et al. 2019) | - | - | | Searching for A Robust Neural Architecture in Four GPU Hours(Dong and Yang 2019)
accepted at CVPR’19 | - | - | | Evolving unsupervised deep neural networks for learning meaningful representations(Sun et al. 2019, accepted by IEEE Transactions on Evolutionary Computation) | - | - | | Evolving Deep Convolutional Neural Networks for Image Classification(Sun et al. 2019, accepted by IEEE Transactions on Evolutionary Computation) | - | - | | AdaResU-Net: Multiobjective Adaptive Convolutional Neural Network for Medical Image Segmentation(Baldeon-Calisto and Lai-Yuen. 2019.)
accepted at Neurocomputing | Medical
Image Segmentation | - | | Automatic Design of Convolutional Neural Network for Hyperspectral Image Classification(Chen et al. 2019)
accepted at IEEE Transactions on Geoscience and Remote Sensing | - | - | | Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation(Chen et al. 2019) | - | - | | Design Automation for Efficient Deep Learning Computing(Han et al. 2019) | - | - | | CascadeML: An Automatic Neural Network Architecture Evolution and Training Algorithm for Multi-label Classification(Pakrashi and Namee 2019) | - | - | | GraphNAS: Graph Neural Architecture Search with Reinforcement Learning(Gao et al. 2019) | - | - | | Neural Architecture Search for Deep Face Recognition(Zhu. 2019) | - | - | | Efficient Neural Architecture Search on Low-Dimensional Data for OCT Image Segmentation(Gessert and Schlaefer. 2019) | Medical
Image Segmentation | - | | NAS-Unet: Neural Architecture Search for Medical Image Segmentation(Weng et al. 2019)
accepted at IEEE Access | Medical
Image Segmentation | - | | Fast DENSER: Efficient Deep NeuroEvolution(Assunção et al. 2019)
accepted at ECGP’19 | - | - | | NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection(Ghaisi et al. 2019)
accepted at CVPR’19 | - | - | | Automated Search for Configurations of Deep Neural Network Architectures(Ghamizi et al. 2019)
accepted at SPLC’19 | - | - | | WeNet: Weighted Networks for Recurrent Network Architecture Search(Huang and Xiang. 2019) | - | - | | Resource Constrained Neural Network Architecture Search(Xiong et al. 2019) | - | - | | Size/Accuracy Trade-Off in Convolutional Neural Networks: An Evolutionary Approach(Cetto et al. 2019)
accepted at INNSBDDL | - | - | | ASAP: Architecture Search, Anneal and Prune(Noy et al. 2019) | - | - | | Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours(Stamoulis et al. 2019) | - | - | | Template-Based Automatic Search of Compact Semantic Segmentation Architectures(Nekrasov et al. 2019) | Image Segmentation | - | | Exploring Randomly Wired Neural Networks for Image Recognition(Xie et al. 2019) | - | - | | Understanding Neural Architecture Search Techniques(Adam and Lorraine 2019) | - | - | | Automatic Convolutional Neural Architecture Search for Image Classification Under Different Scenes(Weng et al. 2019)
accepted at accepted for IEEE Access | - | - | | Single Path One-Shot Neural Architecture Search with Uniform Sampling(Guo et al. 2019) | - | - | | Network Slimming by Slimmable Networks: Towards One-Shot Architecture Search for Channel Numbers(Yu and Huang 2019) | - | - | | sharpDARTS: Faster and More Accurate Differentiable Architecture Search(Hundt et al. 2019) | - | - | | DetNAS: Neural Architecture Search on Object Detection(Chen et al. 2019)
accepted at NeurIPS’19 | Object Detection | megvii-code | | Evolution of Deep Convolutional Neural Networks Using Cartesian Genetic Programming(Suganuma et al. 2019)
accepted at Evolutionary Computation | - | - | | Deep Evolutionary Networks with Expedited Genetic Algorithm for Medical Image Denoising(Liu et al. 2019)
accepted at Medical Image Analysis | - | - | | Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly(Kandasamy et al. 2019) | - | - | | AttoNets: Compact and Efficient Deep Neural Networks for the Edge via Human-Machine Collaborative Design(Wong et al. 2019) | - | - | | Improving Neural Architecture Search Image Classifiers via Ensemble Learning(Macko et al. 2019) | - | - | | Software-Defined Design Space Exploration for an Efficient AI Accelerator Architecture(Yu et al. 2019) | - | - | | MFAS: Multimodal Fusion Architecture Search(Pérez-Rúa et al. 2019)
accepted at CVPR’19 | Multimodal Learning | - | | A Hybrid GA-PSO Method for Evolving Architecture and Short Connections of Deep Convolutional Neural Networks(Wang et al. 2019)
accepted at PRICAI’19 | - | - | | Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search(Li et al. 2019) | - | - | | Inductive Transfer for Neural Architecture Optimization(Wistuba and Pedapati 2019) | - | - | | Evolutionary Cell Aided Design for Neural Network(Colangelo et al. 2019) | - | - | | Automated Architecture-Modeling for Convolutional Neural Networks(Duong 2019) | - | - | | Learning Implicitly Recurrent CNNs Through Parameter Sharing(Savarese and Maire)
accepted at ICLR’19 | - | - | | Evaluating the Search Phase of Neural Architecture Searc(Sciuto et al. 2019) | - | - | | Random Search and Reproducibility for Neural Architecture Search(Li and Talwalkar 2019) | - | - | | Evolutionary Neural AutoML for Deep Learning(Liang et al. 2019) | - | - | | Fast Task-Aware Architecture Inference(Kokiopoulou et al. 2019) | - | - | | Probabilistic Neural Architecture Search(Casale et al. 2019) | - | - | | Investigating Recurrent Neural Network Memory Structures using Neuro-Evolution(Ororbia et al. 2019) | - | - | | Accuracy vs. Efficiency: Achieving Both through FPGA-Implementation Aware Neural Architecture Search(Jiang et al. 2019)
accepted at DAC’19 | - | - | | The Evolved Transformer(So et al. 2019) | - | - | | Designing neural networks through neuroevolution(Stanley et al. 2019)
accepted at Nature Machine Intelligence | - | - | | NeuNetS: An Automated Synthesis Engine for Neural Network Design(Sood et al. 2019) | - | - | | Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search(Chu et al. 2019)
accepted at ICPR’20 | - | - | | EAT-NAS: Elastic Architecture Transfer for Accelerating Large-scale Neural Architecture Search(Fang et al. 2019) | - | - | | Bayesian Learning of Neural Network Architectures(Dikov et al. 2019) | - | - | | Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation(Liu et al. 2019)
accepted at CVPR’19 | Image Segmentation | Github | | The Art of Getting Deep Neural Networks in Shape(Mammadli et al. 2019)
accepted at TACO Journal | - | - | | Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search(Chu et al. 2019) | - | - |

2018

| Title | Tags | Code | |:--------|:--------:|:--------:| | 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) | - | - | | SNAS: Stochastic Neural Architecture Search(Xie et al. 2018)
accepted at ICLR’19 | SNAS | Github | | Graph Hypernetworks for Neural Architecture Search(Zhang et al. 2018)
accepted at Accepted at ICLR’19 | - | - | | Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution(Elsken et al. 2018)
accepted at ICLR’19 | - | - | | Macro Neural Architecture Search Revisited(Hu et al. 2018)
accepted at Meta-Learn NeurIPS workshop’18 | - | - | | AMLA: an AutoML frAmework for Neural Network Design(Kamath et al. 2018)
accepted at at ICML AutoML workshop | - | - | | ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation(Dai et al. 2018) | - | - | | Neural Architecture Search Over a Graph Search Space(de Laroussilhe et al. 2018) | - | - | | A Review of Meta-Reinforcement Learning for Deep Neural Networks Architecture Search(Jaafra et al. 2018) | - | - | | Evolutionary Neural Architecture Search for Image Restoration(van Wyk and Bosman 2018) | - | - | | IRLAS: Inverse Reinforcement Learning for Architecture Search(Guo et al. 2018)
accepted at CVPR’19 | - | - | | FBNet: Hardware-Aware Efficient ConvNet Designvia Differentiable Neural Architecture Search(Wu et al. 2018)
accepted at CVPR’19 | - | - | | ShuffleNASNets: Efficient CNN models throughmodified Efficient Neural Architecture Search(Laube et al. 2018) | - | - | | ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware(Cai et al. 2018)
accepted at ICLR’19 | - | - | | Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search(Wu et al. 2018) | - | - | | Evolving Deep Convolutional Neural Networks by Variable-length Particle Swarm Optimization for Image Classification(Wang et al. 2018)
accepted at CEC’18 | - | - | | A Hybrid Differential Evolution Approach to Designing Deep Convolutional Neural Networks for Image Classification(Wang et al. 2018)
accepted at accepted AI’18 | - | - | | TEA-DNN: the Quest for Time-Energy-Accuracy Co-optimized Deep Neural Networks(Cai et al. 2018) | - | - | | Evolving Space-Time Neural Architectures for Videos(Piergiovanni et al. 2018)
accepted at ICCV’19 | Video Models | - | | InstaNAS: Instance-aware Neural Architecture Search(Cheng et al. 2018) | - | - | | Evolutionary-Neural Hybrid Agents for Architecture Search(Maziarz et al. 2018)
accepted at ICML’19 workshop on AutoML | - | - | | Joint Neural Architecture Search and Quantization(Chen et al. 2018) | - | - | | Transfer Learning with Neural AutoML(Wong et al. 2018)
accepted at NeurIPS’18 | - | - | | Evolving Image Classification Architectures with Enhanced Particle Swarm Optimisation(Fielding and Zhang 2018) | - | - | | Deep Active Learning with a Neural Architecture Search(Geifman and El-Yaniv 2018)
accepted at NeurIPS’19 | - | - | | Stochastic Adaptive Neural Architecture Search for Keyword Spotting(Véniat et al. 2018) | - | - | | NSGA-NET: A Multi-Objective Genetic Algorithm for Neural Architecture Search(Lu et al. 2018) | - | - | | You only search once: Single Shot Neural Architecture Search via Direct Sparse Optimization(Zhang et al. 2018) | - | - | | Automatically Evolving CNN Architectures Based on Blocks(Sun et al. 2018)
accepted at accepted by IEEE Transactions on Neural Networks and Learning Systems | - | - | | The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints(Hundt et al. 2018)
accepted at IROS’19 | - | - | | Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells(Nekrasov et al. 2018)
accepted at CVPR’19 | Image Segmentation | - | | Automatic Configuration of Deep Neural Networks with Parallel Efficient Global Optimization(van Stein et al. 2018) | - | - | | Gradient Based Evolution to Optimize the Structure of Convolutional Neural Networks(Mitschke et al. 2018) | - | - | | Searching Toward Pareto-Optimal Device-Aware Neural Architectures(Cheng et al. 2018) | - | - | | Neural Architecture Optimization(Luo et al. 2018)
accepted at NeurIPS’18 | - | - | | Exploring Shared Structures and Hierarchies for Multiple NLP Tasks(Chen et al. 2018) | - | - | | Neural Architecture Search: A Survey(Elsken et al. 2018) | - | - | | BlockQNN: Efficient Block-wise Neural Network Architecture Generation(Zhong et al. 2018) | - | - | | Automatically Designing CNN Architectures Using Genetic Algorithm for Image Classification(Sunet al. 2018) | - | - | | Reinforced Evolutionary Neural Architecture Search(Chen et al. 2018)
accepted at CVPR’19 | - | - | | Teacher Guided Architecture Search(Bashivan et al. 2018) | - | - | | Efficient Progressive Neural Architecture Search(Perez-Rua et al. 2018) | - | - | | MnasNet: Platform-Aware Neural Architecture Search for Mobile(Tan et al. 2018)
accepted at CVPR’19 | - | - | | Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search(Zela et al. 2018) | - | - | | Automatically Designing CNN Architectures for Medical Image Segmentation(Mortazi and Bagci 2018) | Medical
Image Segmentation | - | | MONAS: Multi-Objective Neural Architecture Search using Reinforcement Learning(Hsu et al. 2018) | - | - | | Path-Level Network Transformation for Efficient Architecture Search(Cai et al. 2018)
accepted at ICML’18 | - | - | | Lamarckian Evolution of Convolutional Neural Networks(Prellberg and Kramer, 2018) | - | - | | Deep Learning Architecture Search by Neuro-Cell-based Evolution with Function-Preserving Mutations(Wistuba, 2018) | - | - | | DARTS: Differentiable Architecture Search(Liu et al. 2018)
accepted at ICLR’19 | - | - | | Constructing Deep Neural Networks by Bayesian Network Structure Learning(Rohekar et al. 2018) | - | - | | Resource-Efficient Neural Architect(Zhou et al. 2018) | - | - | | Efficient Neural Architecture Search with Network Morphism(Jin et al. 2018) | - | - | | TAPAS: Train-less Accuracy Predictor for Architecture Search(Istrate et al. 2018) | - | - | | Neural Architecture Search using Deep Neural Networks and Monte Carlo Tree Search(Wang et al 2018)
accepted at AAAI’20 | - | - | | Multi-objective Architecture Search for CNNs(Elsken et al. 2018) | - | - | | GNAS: A Greedy Neural Architecture Search Method for Multi-Attribute Learning(Huang et al 2018) | - | - | | Evolutionary Architecture Search For Deep Multitask Networks(Liang et al. 2018) | - | - | | From Nodes to Networks: Evolving Recurrent Neural Networks(Rawal et al. 2018) | - | - | | Neural Architecture Construction using EnvelopeNets(Kamath et al. 2018) | - | - | | Transfer Automatic Machine Learning(Wong et al. 2018) | - | - | | Neural Architecture Search with Bayesian Optimisation and Optimal Transport(Kandasamy et al. 2018) | - | - | | Efficient Neural Architecture Search via Parameter Sharing(Pham et al. 2018)
accepted at ICML’18 | - | - | | Regularized Evolution for Image Classifier Architecture Search(Real et al. 2018) | - | - | | Effective Building Block Design for Deep Convolutional Neural Networks using Search(Dutta et al. 2018) | - | - | | Combination of Hyperband and Bayesian Optimization for Hyperparameter Optimization in Deep Learning(Wang et al. 2018) | - | - | | Memetic Evolution of Deep Neural Networks(Lorenzo and Nalepa 2018) | - | - | | Understanding and Simplifying One-Shot Architecture Search(Bender et al. 2018)
accepted at ICML’18 | - | - | | Differentiable Neural Network Architecture Search(Shin et al. 2018)
accepted at ICLR’18 workshop | - | - | | PPP-Net: Platform-aware progressive search for pareto-optimal neural architectures(Dong et al. 2018)
accepted at ICLR’18 workshop | - | - | | Speeding up the Hyperparameter Optimization of Deep Convolutional Neural Networks(Hinz et al. 2018) | - | - | | Gitgraph – From Computational Subgraphs to Smaller Architecture search spaces(Bennani-Smires et al. 2018) | - | - |

2017

| Title | Tags | Code | |:--------|:--------:|:--------:| | N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning(Ashok et al. 2017)
accepted at ICLR’18 | - | - | | Genetic CNN(Xie and Yuille, 2017)
accepted at ICCV’17 | - | - | | MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks(Gordon et al. 2017) | - | - | | MaskConnect: Connectivity Learning by Gradient Descent(Ahmed and Torresani. 2017)
accepted at ECCV’18 | - | - | | A Flexible Approach to Automated RNN Architecture Generation(Schrimpf et al. 2017) | - | - | | DeepArchitect: Automatically Designing and Training Deep Architectures(Negrinho and Gordon 2017) | - | - | | A Genetic Programming Approach to Designing Convolutional Neural Network Architectures(Suganuma et al. 2017)
accepted at GECCO’17 | - | - | | Practical Block-wise Neural Network Architecture Generation(Zhong et al. 2017)
accepted at CVPR’18 | - | - | | Accelerating Neural Architecture Search using Performance Prediction(Baker et al. 2017)
accepted at NeurIPS worshop on Meta-Learning 2017 | - | - | | Large-Scale Evolution of Image Classifiers(Real et al. 2017)
accepted at ICML’17 | - | - | | Hierarchical Representations for Efficient Architecture Search(Liu et al. 2017)
accepted at ICLR’18 | - | - | | Neural Optimizer Search with Reinforcement Learning(Bello et al. 2017) | - | - | | Progressive Neural Architecture Search(Liu et al. 2017)
accepted at ECCV’18 | - | - | | Learning Transferable Architectures for Scalable Image Recognition(Zoph et al. 2017)
accepted at CVPR’18 | - | - | | Simple And Efficient Architecture Search for Convolutional Neural Networks(Elsken et al. 2017)
accepted at NeurIPS workshop on Meta-Learning’17 | - | - | | Bayesian Optimization Combined with Incremental Evaluation for Neural Network Architecture Optimization(Wistuba, 2017) | - | - | | Finding Competitive Network Architectures Within a Day Using UCT(Wistuba 2017) | - | - | | Hyperparameter Optimization: A Spectral Approach(Hazan et al. 2017) | - | - | | SMASH: One-Shot Model Architecture Search through HyperNetworks(Brock et al. 2017)
accepted at NeurIPS workshop on Meta-Learning’17 | - | - | | Efficient Architecture Search by Network Transformation(Cai et al. 2017)
accepted at AAAI’18 | - | - | | Modularized Morphing of Neural Networks(Wei et al. 2017) | - | - |

2016

| Title | Tags | Code | |:--------|:--------:|:--------:| | Towards Automatically-Tuned Neural Networks(Mendoza et al. 2016)
accepted at ICML AutoML workshop | - | - | | Neural Networks Designing Neural Networks: Multi-Objective Hyper-Parameter Optimization(Smithson et al. 2016) | - | - | | AdaNet: Adaptive Structural Learning of Artificial Neural Networks(Cortes et al. 2016) | - | - | | Network Morphism(Wei et al. 2016) | - | - | | Convolutional Neural Fabrics(Saxena and Verbeek 2016)
accepted at NeurIPS’16 | - | - | | CMA-ES for Hyperparameter Optimization of Deep Neural Networks(Loshchilov and Hutter 2016) | - | - | | Designing Neural Network Architectures using Reinforcement Learning(Baker et al. 2016)
accepted at ICLR’17 | - | - | | Neural Architecture Search with Reinforcement Learning(Zoph and Le. 2016)
accepted at ICLR’17 | - | - | | Learning curve prediction with Bayesian Neural Networks(Klein et al. 2017: accepted at ICLR’17) | - | - | | Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization(Li et al. 2016) | - | - |

1988-2015

| Title | Tags | Code | |:--------|:--------:|:--------:| | Net2Net: Accelerating Learning via Knowledge Transfer(Chen et al. 2015)
accepted at ICLR’16 | - | - | | Optimizing deep learning hyper-parameters through an evolutionary algorithm(Young et al. 2015) | - | - | | Practical Bayesian Optimization of Machine Learning Algorithms(Snoek et al. 2012)
accepted at NeurIPS’12 | - | - | | A Hypercube-based Encoding for Evolving large-scale Neural Networks(Stanley et al. 2009) | - | - | | Neuroevolution: From Architectures to Learning(Floreano et al. 2008)
accepted at Evolutionary Intelligence’08 | - | - | | Evolving Neural Networks through Augmenting Topologies(Stanley and Miikkulainen, 2002)
accepted at Evolutionary Computation’02 | - | - | | Evolving Artificial Neural Networks(Yao, 1999)
accepted at IEEE | - | - | | An Evolutionary Algorithm that Constructs Recurrent Neural Networks(Angeline et al. 1994) | - | - | | Designing Neural Networks Using Genetic Algorithms with Graph Generation System(Kitano, 1990) | - | - | | Designing Neural Networks using Genetic Algorithms(Miller et al. 1989)
accepted at ICGA’89 | - | - | | The Cascade-Correlation Learning Architecture(Fahlman and Leblere, 1989)
accepted at NeurIPS’89 | - | - | | Self Organizing Neural Networks for the Identification Problem(Tenorio and Lee, 1988)
accepted at NeurIPS’88 | - | - |

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