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Awesome Neural Architecture Search Papers

本项目希望维护一个完整的NAS领域相关的论文列表，同时为其中一些广受关注的论文提供导读，以帮助大家更有效的开展NAS相关研究工作。

目录

• 2020 (283)
• 2019 (261)
• 2018 (72)
• 2017 (21)
• 2016 (10)
• 1988~2015 (12)

2020

标题	标签	代码
Deep Convolution Features in Non-linear Embedding Space for Fundus Image Classification(Dondeti et al. 2020) accepted at Revue d’Intelligence Artificielle	-	-
A Unified Approach to Anomaly Detection(Ball et al. 2020)	-	-
Evolving Multi-Resolution Pooling CNN for Monaural Singing Voice Separation(Yuan et al. 2020)	-	-
Weight-Sharing Neural Architecture Search: A Battle to Shrink the Optimization Gap(Xie et al. 2020)	-	-
Neural Architecture Search in Graph Neural Networks(Nunes and L.Pappa 2020)	-	-
Anti-Bandit Neural Architecture Search for Model Defense(Chen et al. 2020) accepted at ECCV 2020	-	-
HMCNAS: Neural Architecture Search Using Hidden Markov Chains And Bayesian Optimization(Lopes and Alexandre 2020)	-	-
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	-	-
Growing Efficient Deep Networks by Structured Continuous Sparsification(Yuan et al. 2020)	-	-
Lidar Data Classification Based on Automatic Designed CNN(Xie and Chen 2020) accepted at IEEE Geoscience and Remote Sensing Letters	-	-
Fusion Mechanisms for Human Activity Recognition using Automated Machine Learning(Popescu et al. 2020) accepted at IEEE Access	-	-
Mixed-Precision Quantization for CNN-Based Remote Sensing Scene Classification(Wei et al. 2020) accepted at IEEE Geoscience and Remote Sensing Letters	-	-
Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound(Huang et al. 2020) accepted at MICCAI 2020	-	-
TF-NAS: Rethinking Three Search Freedoms of Latency-Constrained Differentiable Neural Architecture Search(Hu et al. 2020) accepted at ECCV 2020	-	-
Efficient Oct Image Segmentation Using Neural Architecture Search(Gheshlaghi et al. 2020)	-	-
SOTERIA: In Search of Efficient Neural Networks for Private Inference(Aggarwal et al. 2020)	-	-
What and Where: Learn to Plug Adapters via NAS for Multi-Domain Learning(Zhao et al. 2020)	-	-
CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending(Xu et al. 2020) accepted at ECCV 2020	-	-
Representation Sharing for Fast Object Detector Search and Beyond(Zhou et al .2020) accepted at ECCV 2020	-	-
AttentionNAS: Spatiotemporal Attention Cell Search for Video Classification(Wang et al. 2020) accepted at ECCV 2020	-	-
Weight-Sharing Neural Architecture Search: A Battle to Shrink the Optimization Gap(Xie et al. 2020)	-	-
MCUNet: Tiny Deep Learning on IoT Devices(Lin et al. 2020)	-	-
Search What You Want: Barrier Panelty NAS for Mixed Precision Quantization(Yu et al. 2020) accepted at ECCV 2020	-	-
NSGANetV2: Evolutionary Multi-Objective Surrogate-Assisted Neural Architecture Search(Lu et al. 2020) accepted at ECCV 2020	-	-
CATCH: Context-based Meta Reinforcement Learning for Transferrable Architecture Search(Chen et al. 2020) accepted at ECCV 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	-	-
Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search(Tian et al. 2020) accepted at ECCV 2020	-	-
Neural Architecture Search for Speech Recognition(Hu et al. 2020)	-	-
BRP-NAS: Prediction-based NAS using GCNs(Chau et al .2020)	-	-
Finding Non-Uniform Quantization Schemes using Multi-Task Gaussian Processes(do Nascimento et al. 2020) accepted at ECCV 2020	-	-
One-Shot Neural Architecture Search via Novelty Driven Sampling(Zhang et al. 2020) accepted at IJCAI 2020	-	-
Neural Architecture Search in A Proxy Validation Loss Landscape(Li et al. 2020) accepted at ICML 2020	-	-
CP-NAS: Child-Parent Neural Architecture Search for 1-bit CNNs(Zhuo et al. 2020) accepted at IJCAI 2020	-	-
SI-VDNAS: Semi-Implicit Variational Dropout for Hierarchical One-shot Neural Architecture Search(Wang et al. 2020) accepted at IJCAI 2020	-	-
An Empirical Study on the Robustness of NAS based Architectures(Devaguptapu et al. 2020)	-	-
MergeNAS: Merge Operations into One for Differentiable Architecture Search(Wang et al. 2020) accepted at IJCAI 2020	-	-
DropNAS: Grouped Operation Dropout for Differentiable Architecture Search(Hong et al. 2020)	-	-
Evolving Robust Neural Architectures to Defend from Adversarial Attacks(Kotyan and Vargas 2020) accepted at Proceedings of the Workshop on Artificial Intelligence Safety 2020	-	-
Architecture Search of Dynamic Cells for Semantic Video Segmentation(Nekrasov et al. 2020) accepted at WACV 2020	-	-
Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search(Guo et al. 2020)	-	-
Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction(Song et al. 2020) accepted at KDD2020	-	-
MS-NAS: Multi-Scale Neural Architecture Search for Medical Image Segmentation(Yan et al. 2020)	-	-
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)	-	-
Graph Neural Architecture Search(Gao et al. 2020) accepted at IJCAI 2020	-	-
Ensembles of Networks Produced from Neural Architecture Search(Herron et al. 2020)	-	-
Neural Architecture Search with GBDT(Luo et al. 2020)	-	-
A Study on Encodings for Neural Architecture Search(White et al. 2020)	-	-
NASGEM: Neural Architecture Search via Graph Embedding Method(Cheng et al. 2020)	-	-
Neuro-evolution using Game-Driven Cultural Algorithms(Waris and Reynolds) accepted at GECCO 2020	-	-
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	-	-
Towards Evolving Robust Neural Architectures to Defend From Adversarial Attacks(Kotyan and Vargas) accepted at GECCO 2020	-	-
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)	-	-
Hyperparameter Optimization in Neural Networks via Structured Sparse Recovery(Cho et al. 2020)	-	-
Journey Towards Tiny Perceptual Super-Resolution(Lee et al. 2020)	-	-
Self-supervised Neural Architecture Search(Kaplan and Giryes 2020)	-	-
Blocks for Image Classification(Wang et al. 2020)	-	-
Multi-Objective Neural Architecture Search Based on Diverse Structures and Adaptive Recommendation(Wang et al. 2020)	-	-
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)	-	-
M-NAS: Meta Neural Architecture Search(Wang et al. 2020) accepted at AAAI 2020	-	-
FiFTy: Large-scale File Fragment Type Identification using Convolutional Neural Networks(Mittal et al. 2020) accepted at IEEE Transactions on Information Forensics and Security	-	-
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	-	-
Theory-Inspired Path-Regularized Differential Network Architecture Search(Zhou et al. 2020)	-	-
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)	-	-
AutoSNAP: Automatically Learning Neural Architectures for Instrument Pose Estimation(Kügler et al. 2020)	-	-
Evolutionary Recurrent Neural Architecture Search(Tian et al. 2020) accepted at IEEE Embedded System Letters	-	-
Neural-Architecture-Search-Based Multiobjective Cognitive Automation System(Wang et al. 2020) accepted at IEEE System Journal	-	-
Enhancing Model Parallelism in Neural Architecture Search for Multi-device System(Fu et al. 2020) accepted at IEEE Micro	-	-
AutoST: Efficient Neural Architecture Search for Spatio-Temporal Prediction(Li et al. 2020) accepted at KDD 2020	-	-
Neural Architecture Search for Sparse DenseNets with Dynamic Compression(O’Neill et al. 2020) accepted at GECCO 2020	-	-
Searching towards Class-Aware Generators for Conditional Generative Adversarial Networks(Zhou et al. 2020)	-	-
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	-	-
FNA++: Fast Network Adaptation via Parameter Remapping and Architecture Search(Fang et al. 2020)	-	-
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)	-	-
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	-	-
Neural Architecture Search for Time Series Classification(Rakhshani et al. 2020) accepted at ijcnn 2020	-	-
Cyclic Differentiable Architecture Search(Yu et al. 2020)	-	-
Differentially-private Federated Neural Architecture Search(Singh et al. 2020)	-	-
DrNAS: Dirichlet Neural Architecture Search(Chen et al. 2020)	-	-
Neural Architecture Optimization with Graph VAE(Li et al. 2020)	-	-
Fine-Grained Stochastic Architecture Search(Chaudhuri et al. 2020)	-	-
Bonsai-Net: One-Shot Neural Architecture Search via Differentiable Pruners(Geada et al. 2020)	-	-
AlphaGAN: Fully Differentiable Architecture Search for Generative Adversarial Networks(Tian et al. 2020)	-	-
Fine-Tuning DARTS for Image Classification(Tanveer et al. 2020)	-	-
Neural Anisotropy Directions(Ortiz-Jiménez et al. 2020)	-	-
CryptoNAS: Private Inference on a ReLU Budget(Ghodsi et al. 2020)	-	-
Heuristic Architecture Search Using Network Morphism for Chest X-Ray Classification(Radiuk and Kutucu 2020)	-	-
Task-aware Performance Prediction for Efficient Architecture Search(Kokiopoulou et al. 2020) accepted at ECAI 2020	-	-
Beyond Network Pruning: a Joint Search-and-Training Approach(Lu et al. 2020) accepted at IJCAI 2020	-	-
Neural Ensemble Search for Performant and Calibrated Predictions(Zaidi et al. 2020)	-	-
Multi-fidelity Neural Architecture Search with Knowledge Distillation(Trofimov et al. 2020)	-	-
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)	-	-
Neural Architecture Search using Bayesian Optimisation with Weisfeiler-Lehman Kernel(Ru et al. 2020)	-	-
NAS-Bench-NLP: Neural Architecture Search Benchmark for Natural Language Processing(Klyuchnikov et al. 2020)	-	-
Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?(Yan et el. 2020)	-	-
Few-shot Neural Architecture Search(Zhao et al. 2020)	-	-
NADS: Neural Architecture Distribution Search for Uncertainty Awareness(Ardywibowo et al. 2020)	-	-
Towards Efficient Automated Machine Learning(Li 2020)	-	-
AMER: Automatic Behavior Modeling and Interaction Exploration in Recommender System(Zhao et al. 2020)	-	-
Neuroevolution in Deep Neural Networks: Current Trends and Future Challenges(Galvan and Mooney 2020)	-	-
AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks(Fu et al. 2020) accepted at ICML 2020	-	-
Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?(Yan et al. 2020)	-	-
Hardware-Aware Transformable Architecture Search with Efficient Search Space(Jiang et al. 2020) accepted at accpeted at ICME 2020	-	-
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	-	-
Neural Architecture Search without Training(Mellor et al. 2020)	-	-
Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search(Ru et al. 2020)	-	-
Differentiable Neural Input Search for Recommender Systems(Cheng et al. 2020)	-	-
Efficient Architecture Search for Continual Learning(Gao et al. 2020)	-	-
Conditional Neural Architecture Search(Kao et al. 2020)	-	-
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	-	-
Fast Hardware-Aware Neural Architecture Search(Zhang et al. 2020) accepted at CVPR 2020 workshop	-	-
Memory-Efficient Hierarchical Neural Architecture Search for Image Denoising(Zhang et al. 2020) accepted at CVPR 2020	-	-
GP-NAS: Gaussian Process based Neural Architecture Search(Li et al. 2020) accepted at CVPR 2020	-	-
MemNAS: Memory-Efficient Neural Architecture Search with Grow-Trim Learning(Liu et al.2020) accepted at CVPR 2020	-	-
Can weight sharing outperform random architecture search? An investigation with TuNAS(Bender et al. 2020) accepted at CVPR 2020	-	-
Butterfly Transform: An Efficient FFT Based Neural Architecture Design(Alizadeh vahid et al. 2020) accepted at CVPR 2020	-	-
APQ: Joint Search for Network Architecture, Pruning and Quantization Policy(Wang et al.2020) accepted at CVPR 2020	-	-
SP-NAS: Serial-to-Parallel Backbone Search for Object Detection(Jiang et al. 2020) accepted at CVPR 2020	-	-
All in One Bad Weather Removal using Architectural Search(Li et al. 2020) accepted at CVPR 2020	-	-
NeuralScale: Efficient Scaling of Neurons for Resource-Constrained Deep Neural Networks(Lee and Lee) accepted at CVPR 2020	-	-
On Network Design Spaces for Visual Recognition(Radosavovic et al. 2020)	-	-
A Comprehensive Survey of Neural Architecture Search: Challanges and Solutions(Ren et al. 2020)	-	-
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)	-	-
Automation of Deep Learning – Theory and Practice(Wistuba et al. 2020) accepted at ICMR 202	-	-
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	-	-
DC-NAS: Divide-and-Conquer Neural Architecture Search(Wang et al. 2020)	-	-
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	-	-
Searching Better Architectures for Neural Machine Translation(Fan et al. 2020) accepted at IEEE/ACM Transactions on Audio, Speech, and Language Processing	-	-
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	-	-
A New Deep Neural Architecture Search Pipeline for Face Recognition(Zhu et al. 2020) accepted at IEEE Access	-	-
Regularized Evolution for Marco Neural Architecture Search(Kyriakides and Margaritis) accepted at AIAI2020	-	-
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)	-	-
Designing Convolutional Neural Network Architectures Using Cartesian Genetic Programming(Suganuma et al. 2020) accepted at accepted in book on “Deep Neural Evolution”	-	-
An Introduction to Neural Architecture Search for Convolutional Networks(Kyriakides and Margaritis, 2020)	-	-
AutoSegNet: An Automated Neural Network for Image Segmentation(Xu et al. 2020) accepted at IEEE Access	-	-
DMS: Differentiable Dimension Search for Binary Neural Networks(Li et al. 2020) accepted at 1st Workshop on Neural Architecture Search at ICLR 2020	-	-
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”	-	-
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	-	-
Rethinking Performance Estimation in Neural Architecture Search(Zheng et al. 2020) accepted at CVPR 2020	-	-
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)	-	-
Improving Neuroevolution Using Island Extinction And Repopulation(Lyu et al. 2020)	-	-
A Framework for Exploring and Modelling Neural Architecture Search Methods(Radiuk et al. 2020)	-	-
You Only Search Once: A Fast Automation Framework for Single-Stage DNN/Accelerator Co-design(Chen et al. 2020)	-	-
DARTS-ASR: Differentiable Architecture Search for Multilingual Speech Recognition and Adaptation(Chen et al. 2020)	-	-
A Semi-Supervised Assessor of Neural Architectures(Tang et al. 2020) accepted at CVPR 2020	-	-
Neural Architecture Search for Gliomas Segmentation on Multimodal Magnetic Resonance Imaging(Wang et al. 2020)	-	-
Binarizing MobileNet via Evolution-based Searching(Phan et al. 2020)	-	-
Neural Architecture Transfer(Lu et al. 2020)	-	-
Optimization of deep neural networks: a survey and unified taxonomy(Talbi 2020)	-	-
Auto-Fas: Searching Lightweight Networks for Face Anti-Spoofing(Yu et al. 2020) accepted at accetped at ICASSP 2020	-	-
Neuro Evolutional with Game-Driven Cultural Algorithms(Waris and Reynolds 2020) accepted at ACM GECCO 2020	-	-
NASIL: Neural Architecture Search With Imitation Learning(Fard et al. 2020) accepted at ICASSP 2020	-	-
Noisy Differentiable Architecture Search(Chu et al. 2020)	-	-
AutoSpeech: Neural Architecture Search for Speaker Recognition(Ding et al. 2020)	-	-
Learning Architectures from an Extended Search Space for Language Modeling(Li et al. 2020)	-	-
CP-NAS: Child-Parent Neural Architecture Search for 1-bit CNNs( Zhuo et al. 2020)	-	-
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”	-	-
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	-	-
Local Search is State of the Art for Neural Architecture Search Benchmarks(White et al. 2020) accepted at AutoML workshop at ICML’20	-	-
SIPA: A Simple Framework for Efficient Networks(Lee et al. 2020)	-	-
Neural Architecture Search Based on Model Statistics for Wildlife Identification(Jia et al. 2020) accepted at Journal of the Franklin Institute	-	-
The effect of reduced training in neural architecture search(Kyriakides and Margaritis. 2020) accepted at Neural Comput & Applic	-	-
Efficient Evolutionary Neural Architecture Search(Tan et al. 2020) accepted at BIC-TA’20	-	-
MobileDets: Searching for Object Detection Architectures for Mobile Accelerators( Xiong et al. 2020)	-	-
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)	-	-
Deep Multimodal Neural Architecture Search(Yu et al. 2020)	-	-
Depth-Wise Neural Architecture Search(Jordao et al. 2020)	-	-
Recurrent Neural Network Architecture Search for Geophyiscal Emulation(Maulik et al. 2020)	-	-
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	-	-
Organ at Risk Segmentation for Head and Neck Cancer using Stratified Learning and Neural Architecture Search(Guo et al. 2020)	-	-
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)	-	-
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	-	-
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) accepted at ECCV 2020	-	Github
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	-	-
Neural architecture search based on model pool for wildlife identification(Jia et al. 2020) accepted at Neurocomputing	-	-
An Evolutionary Approach to Variational Autoencoders(Hajewski and Oliveira. 2020) accepted at CCWC’20	-	-
A Scalable System for Neural Architecture Search(Hajewski and Oliveira. 2020) accepted at CCWC’20	-	-
Neural Architecture Generator Optimization(Ru et al. 2020)	-	-
Deep-n-Cheap: An Automated Search Framework for Low Complexity Deep Learning(Dey et al. 2020)	-	-
MTL-NAS: Task-Agnostic Neural Architecture Search towards General-Purpose Multi-Task Learning(Gao et al. 2020) accepted at CVPR’20	-	-
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	-	-
Are Labels Necessary for Neural Architecture Search?(Liu et al. 2020)	-	-
DCNAS: Densely Connected Neural Architecture Search for Semantic Image Segmentation(Zhang et al. 2020)	-	-
Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection(Guo et al. 2020) accepted at CVPR 2020	-	-
Sampled Training and Node Inheritance for Fast Evolutionary Neural Architecture Search(Zhang et al. 2020)	-	-
GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernet(You et al. 2020) accepted at CVPR’2020	-	-
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)	-	-
GAN Compression: Efficient Architectures for Interactive Conditional GAN(Li et al. 2020)	-	-
ElixirNet: Relation-aware Network Architecture Adaptation for Medical Lesion Detection(Jiang et al. 2020)	-	-
Lifelong Learning with Searchable Extension Units(Wang et al. 2020)	-	-
Efficient Backbone Search for Scene Text Recognition(Zhang et al. 2020)	-	-
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data(Erickson et al. 2020)	-	-
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)	-	-
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)	-	-
Accelerator-Aware Neural Network Design Using AutoML(Gupta and Akin. 2020) accepted at On-device Intelligence Workshop at MLSys’20	-	-
Real-time Federated Evolutionary Neural Architecture Search(Zhu and Jin. 2020)	-	-
BATS: Binary ArchitecTure Search(Bulat et al. 2020) accepted at ECCV’20	-	-
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)	-	-
AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations(Zhao et al. 2020)	-	-
Memory-Efficient Models for Scene Text Recognition via Neural Architecture Search(Hong et al. 2020) accepted at WACV’20 workshop	-	-
Search for Winograd-Aware Quantized Networks(Fernandez-Marques et al. 2020)	-	-
Semi-Supervised Neural Architecture Search(Luo et al. 2020)	-	-
Neural Architecture Search for Compressed Sensing Magnetic Resonance Image Reconstruction(Yan et al. 2020)	-	-
DSNAS: Direct Neural Architecture Search without Parameter Retraining(Hu et al. 2020)	-	-
Neural Architecture Search For Fault Diagnosis(Li et al. 2020) accepted at ESREL’20	-	-
Learning Architectures for Binary Networks(Kim et al. 2020) accepted at ECCV’20	-	-
Efficient Evolutionary Architecture Search for CNN Optimization on GTSRB(Johner and Wassner. 2020) accepted at ICMLA’19	-	-
Automating Deep Neural Network Model Selection for Edge Inference(Lu et al. 2020) accepted at CogMI’20	-	-
Neural Architecture Search over Decentralized Data(Xu et al. 2020)	-	-
Automatic Structural Search for Multi-task Learning VALPs(Garciarena et al. 2020) accepted at OLA’20	-	-
RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning(Alletto et al. 2020) accepted at Meta-Eval 2020 workshop	-	-
Classifying the classifier: dissecting the weight space of neural networks(Eilertsen et al. 2020)	-	-
Stabilizing Differentiable Architecture Search via Perturbation-based Regularization(Chen and Hsieh. 2020)	-	-
Best of Both Worlds: AutoML Codesign of a CNN and its Hardware Accelerator(Abdelfattah et al. 2020) accepted at DAC’20	-	-
Variational Depth Search in ResNets(Antoran et al. 2020)	-	-
Co-Exploration of Neural Architectures and Heterogeneous ASIC Accelerator Designs Targeting Multiple Tasks(Yang et al. 2020) accepted at DAC’20	-	-
FPNet: Customized Convolutional Neural Network for FPGA Platforms(Yang et al. 2020) accepted at FPT’20	-	-
AutoFCL: Automatically Tuning Fully Connected Layers for Transfer Learning(Basha et al. 2020)	-	-
NASS: Optimizing Secure Inference via Neural Architecture Search(Bian et al. 2020) accepted at ECAI’20	-	-
Search for Better Students to Learn Distilled Knowledge(Gu et al. 2020)	-	-
Bayesian Neural Architecture Search using A Training-Free Performance Metric(Camero et al. 2020)	-	-
NAS-Bench-1Shot1: Benchmarking and Dissecting One-Short Neural Architecture Search(Zela et al. 2020) accepted at ICLR’20	-	-
Convolution Neural Network Architecture Learning for Remote Sensing Scene Classification(Chen et al. 2010)	-	-
Multi-objective Neural Architecture Search via Non-stationary Policy Gradient(Chen et al. 2020)	-	-
Efficient Neural Architecture Search: A Broad Version(Ding et al. 2020)	-	-
ENAS U-Net: Evolutionary Neural Architecture Search for Retinal Vessel(Fan et al. 2020)	-	-
FlexiBO: Cost-Aware Multi-Objective Optimization of Deep Neural Networks(Iqbal et al. 2020)	-	-
Up to two billion times acceleration of scientific simulations with deep neural architecture search(Kasim et al. 2020)	-	-
Latency-Aware Differentiable Neural Architecture Search(Xu et al. 2020)	-	-
MixPath: A Unified Approach for One-shot Neural Architecture Search(Chu et al. 2020)	-	-
Neural Architecture Search for Skin Lesion Classification(Kwasigroch et al. 2020) accepted at IEEE Access	-	-
AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search(Chen et al. 2020)	-	-
Neural Architecture Search for Deep Image Prior(Ho et al. 2020)	-	-
Fast Neural Network Adaptation via Parameter Remapping and Architecture Search(Fang et al. 2020) accepted at ICLR’20	-	-
FTT-NAS: Discovering Fault-Tolerant Neural Architecture(Li et al. 2020) accepted at ASP-DAC 2020	-	-
Deeper Insights into Weight Sharing in Neural Architecture Search(Zhang et al. 2020)	-	-
EcoNAS: Finding Proxies for Economical Neural Architecture Search(Zhou et al. 2020) accepted at CVPR’20	-	-
DeepMaker: A multi-objective optimization framework for deep neural networks in embedded systems(Loni et al. 2020) accepted at Microprocessors and Microsystems	-	-
Auto-ORVNet: Orientation-boosted Volumetric Neural Architecture Search for 3D Shape Classification(Ma et al. 2020) accepted at IEEE Access	-	-
NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search(Dong and Yang et al. 2020) accepted at ICLR’20	-	-

2019

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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)	-	-
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	-	-
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	-	-
Neural Architecture Search for Adversarial Medical Image Segmentation(Dong et al. 2019) accepted at MICCAI’19	-	-
Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation(Yang et al. 2019) accepted at MICCAI’19	-	-
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)	-	-
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)	-	-
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	-	-
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	-	-
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)	-	-
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	-	-
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)	-	-
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)	-	-
Evolving Robust Neural Architectures to Defend from Adversarial Attacks(Vargas and Kotyan. 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)	-	-
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)	-	-
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	-	-
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)	-	-
NAS-Unet: Neural Architecture Search for Medical Image Segmentation(Weng et al. 2019) accepted at IEEE Access	-	-
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)	-	-
Architecture Search of Dynamic Cells for Semantic Video Segmentation(Nekrasov et al. 2019)	-	-
Template-Based Automatic Search of Compact Semantic Segmentation Architectures(Nekrasov et al. 2019)	-	-
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	-	-
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	-	-
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	-	-
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

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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	-	-
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	-	-
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	-	-
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)	-	-
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

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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

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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

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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	-	-