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1 | # Awesome Neural Architecture Search Papers
2 |
3 | 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。
4 | ### 目录
5 | - [2020](#2020) (283)
6 | - [2019](#2019) (261)
7 | - [2018](#2018) (72)
8 | - [2017](#2017) (21)
9 | - [2016](#2016) (10)
10 | - [1988~2015](#1988-2015) (12)
11 |
12 | ### 2020
13 |
14 | | 标题 | 标签 | 代码 |
15 | |:--------|:--------:|:--------:|
16 | | [Deep Convolution Features in Non-linear Embedding Space for Fundus Image Classification(Dondeti et al. 2020) ](http://www.iieta.org/journals/ria/paper/10.18280/ria.340308)
*accepted at Revue d’Intelligence Artificielle* | - | - |
17 | | [A Unified Approach to Anomaly Detection(Ball et al. 2020) ](https://www.researchgate.net/profile/Hennie_Kruger/publication/343006753_A_Unified_Approach_to_Anomaly_Detection/links/5f218f1592851cd302c5fb31/A-Unified-Approach-to-Anomaly-Detection.pdf) | - | - |
18 | | [Evolving Multi-Resolution Pooling CNN for Monaural Singing Voice Separation(Yuan et al. 2020) ](https://arxiv.org/abs/2008.00816) | - | - |
19 | | [Weight-Sharing Neural Architecture Search: A Battle to Shrink the Optimization Gap(Xie et al. 2020) ](https://arxiv.org/abs/2008.01475) | - | - |
20 | | [Neural Architecture Search in Graph Neural Networks(Nunes and L.Pappa 2020) ](https://arxiv.org/abs/2008.00077) | - | - |
21 | | [Anti-Bandit Neural Architecture Search for Model Defense(Chen et al. 2020) ](https://arxiv.org/abs/2008.00698)
*accepted at ECCV 2020* | - | - |
22 | | [HMCNAS: Neural Architecture Search Using Hidden Markov Chains And Bayesian Optimization(Lopes and Alexandre 2020) ](https://arxiv.org/abs/2007.16149) | - | - |
23 | | [Neural Architecture Search as Sparse Supernet(Wu et al. 2020) ](https://arxiv.org/abs/2007.16112) | - | - |
24 | | [Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution(Tang et al. 2020) ](https://arxiv.org/abs/2007.16100)
*accepted at ECCV 2020* | - | - |
25 | | [Growing Efficient Deep Networks by Structured Continuous Sparsification(Yuan et al. 2020) ](https://arxiv.org/abs/2007.15353) | - | - |
26 | | [Lidar Data Classification Based on Automatic Designed CNN(Xie and Chen 2020) ](https://ieeexplore.ieee.org/abstract/document/9139215)
*accepted at IEEE Geoscience and Remote Sensing Letters* | - | - |
27 | | [Fusion Mechanisms for Human Activity Recognition using Automated Machine Learning(Popescu et al. 2020) ](https://ieeexplore.ieee.org/document/9153764)
*accepted at IEEE Access* | - | - |
28 | | [Mixed-Precision Quantization for CNN-Based Remote Sensing Scene Classification(Wei et al. 2020) ](https://ieeexplore.ieee.org/abstract/document/9153122/authors#authors)
*accepted at IEEE Geoscience and Remote Sensing Letters* | - | - |
29 | | [Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound(Huang et al. 2020) ](https://arxiv.org/abs/2007.15273)
*accepted at MICCAI 2020* | - | - |
30 | | [TF-NAS: Rethinking Three Search Freedoms of Latency-Constrained Differentiable Neural Architecture Search(Hu et al. 2020) ](https://alfredxiangwu.github.io/papers/hu2020eccv.pdf)
*accepted at ECCV 2020* | - | - |
31 | | [Efficient Oct Image Segmentation Using Neural Architecture Search(Gheshlaghi et al. 2020) ](https://arxiv.org/abs/2007.14790) | - | - |
32 | | [SOTERIA: In Search of Efficient Neural Networks for Private Inference(Aggarwal et al. 2020) ](https://arxiv.org/abs/2007.12934) | - | - |
33 | | [What and Where: Learn to Plug Adapters via NAS for Multi-Domain Learning(Zhao et al. 2020) ](https://arxiv.org/abs/2007.12415) | - | - |
34 | | [CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending(Xu et al. 2020) ](https://arxiv.org/abs/2007.12147)
*accepted at ECCV 2020* | - | - |
35 | | [Representation Sharing for Fast Object Detector Search and Beyond(Zhou et al .2020) ](https://arxiv.org/abs/2007.12075)
*accepted at ECCV 2020* | - | - |
36 | | [AttentionNAS: Spatiotemporal Attention Cell Search for Video Classification(Wang et al. 2020) ](https://arxiv.org/abs/2007.12034)
*accepted at ECCV 2020* | - | - |
37 | | [Weight-Sharing Neural Architecture Search: A Battle to Shrink the Optimization Gap(Xie et al. 2020) ](https://arxiv.org/abs/2008.01475) | - | - |
38 | | [MCUNet: Tiny Deep Learning on IoT Devices(Lin et al. 2020) ](https://arxiv.org/abs/2007.10319) | - | - |
39 | | [Search What You Want: Barrier Panelty NAS for Mixed Precision Quantization(Yu et al. 2020) ](https://arxiv.org/abs/2007.10026)
*accepted at ECCV 2020* | - | - |
40 | | [NSGANetV2: Evolutionary Multi-Objective Surrogate-Assisted Neural Architecture Search(Lu et al. 2020) ](https://arxiv.org/abs/2007.10396)
*accepted at ECCV 2020* | - | - |
41 | | [CATCH: Context-based Meta Reinforcement Learning for Transferrable Architecture Search(Chen et al. 2020) ](https://arxiv.org/abs/2007.09380)
*accepted at ECCV 2020* | - | - |
42 | | [Standing on the Shoulders of Giants: Hardware and Neural Architecture Co-Search with Hot Start(Jiang et al. 2020) ](https://arxiv.org/abs/2007.09087)
*accepted at IEEE Transactions On Computer-Aided Design of Integrated Circuits and System* | - | - |
43 | | [Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search(Tian et al. 2020) ](https://arxiv.org/abs/2007.09180)
*accepted at ECCV 2020* | - | - |
44 | | [Neural Architecture Search for Speech Recognition(Hu et al. 2020) ](https://arxiv.org/abs/2007.08818) | - | - |
45 | | [BRP-NAS: Prediction-based NAS using GCNs(Chau et al .2020) ](https://arxiv.org/abs/2007.08668) | - | - |
46 | | [Finding Non-Uniform Quantization Schemes using Multi-Task Gaussian Processes(do Nascimento et al. 2020) ](https://ui.adsabs.harvard.edu/abs/2020arXiv200707743G/abstract)
*accepted at ECCV 2020* | - | - |
47 | | [One-Shot Neural Architecture Search via Novelty Driven Sampling(Zhang et al. 2020) ](https://www.ijcai.org/Proceedings/2020/0441.pdf)
*accepted at IJCAI 2020* | - | - |
48 | | [Neural Architecture Search in A Proxy Validation Loss Landscape(Li et al. 2020) ](https://proceedings.icml.cc/static/paper_files/icml/2020/439-Paper.pdf)
*accepted at ICML 2020* | - | - |
49 | | [CP-NAS: Child-Parent Neural Architecture Search for 1-bit CNNs(Zhuo et al. 2020) ](https://www.ijcai.org/Proceedings/2020/0144.pdf)
*accepted at IJCAI 2020* | - | - |
50 | | [SI-VDNAS: Semi-Implicit Variational Dropout for Hierarchical One-shot Neural Architecture Search(Wang et al. 2020) ](https://www.ijcai.org/Proceedings/2020/0289.pdf)
*accepted at IJCAI 2020* | - | - |
51 | | [An Empirical Study on the Robustness of NAS based Architectures(Devaguptapu et al. 2020) ](https://arxiv.org/abs/2007.08428) | - | - |
52 | | [MergeNAS: Merge Operations into One for Differentiable Architecture Search(Wang et al. 2020) ](https://www.ijcai.org/Proceedings/2020/0424.pdf)
*accepted at IJCAI 2020* | - | - |
53 | | [DropNAS: Grouped Operation Dropout for Differentiable Architecture Search(Hong et al. 2020) ](https://www.ijcai.org/Proceedings/2020/0322.pdf) | - | - |
54 | | [Evolving Robust Neural Architectures to Defend from Adversarial Attacks(Kotyan and Vargas 2020) ](http://ceur-ws.org/Vol-2640/paper_1.pdf)
*accepted at Proceedings of the Workshop on Artificial Intelligence Safety 2020* | - | - |
55 | | [Architecture Search of Dynamic Cells for Semantic Video Segmentation(Nekrasov et al. 2020) ](https://openaccess.thecvf.com/content_WACV_2020/papers/Nekrasov_Architecture_Search_of_Dynamic_Cells_for_Semantic_Video_Segmentation_WACV_2020_paper.pdf)
*accepted at WACV 2020* | - | - |
56 | | [Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search(Guo et al. 2020) ](https://arxiv.org/abs/2007.07197) | - | - |
57 | | [Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction(Song et al. 2020) ](https://arxiv.org/abs/2007.06434)
*accepted at KDD2020* | - | - |
58 | | [MS-NAS: Multi-Scale Neural Architecture Search for Medical Image Segmentation(Yan et al. 2020) ](https://arxiv.org/abs/2007.06151) | - | - |
59 | | [VINNAS: Variational Inference-based Neural Network Architecture Search(Ferianc et al. 2020) ](https://arxiv.org/abs/2007.06103) | - | - |
60 | | [Multi-Modality Information Fusion for Radiomics-based Neural Architecture Search(Peng et al. 2020) ](https://arxiv.org/abs/2007.06002) | - | - |
61 | | [Graph Neural Architecture Search(Gao et al. 2020) ](https://www.researchgate.net/profile/Chuan_Zhou5/publication/342789484_Graph_Neural_Architecture_Search/links/5f0be495299bf18816197d15/Graph-Neural-Architecture-Search.pdf)
*accepted at IJCAI 2020* | - | - |
62 | | [Ensembles of Networks Produced from Neural Architecture Search(Herron et al. 2020) ](https://keuperj.github.io/MLHPCS/paper/NASEnsemblesFinal.pdf) | - | - |
63 | | [Neural Architecture Search with GBDT(Luo et al. 2020) ](https://arxiv.org/abs/2007.04785) | - | - |
64 | | [A Study on Encodings for Neural Architecture Search(White et al. 2020) ](https://arxiv.org/pdf/2007.04965.pdf) | - | - |
65 | | [NASGEM: Neural Architecture Search via Graph Embedding Method(Cheng et al. 2020) ](https://arxiv.org/abs/2007.04452) | - | - |
66 | | [Neuro-evolution using Game-Driven Cultural Algorithms(Waris and Reynolds) ](https://dl.acm.org/doi/abs/10.1145/3377929.3398093)
*accepted at GECCO 2020* | - | - |
67 | | [An Evolution-based Approach for Efficient Differentiable Architecture Search(Kobayashi and Nagao) ](https://dl.acm.org/doi/abs/10.1145/3377929.3390003)
*accepted at GECCO 2020* | - | - |
68 | | [HyperFDA: a bi-level Optimization Approach to Neural Architecture Search and Hyperparameters’ optimization via fractal decomposition-based algorithm(Souquet et al. 2020) ](https://dl.acm.org/doi/abs/10.1145/3377929.3390056)
*accepted at GECCO 2020* | - | - |
69 | | [Towards Evolving Robust Neural Architectures to Defend From Adversarial Attacks(Kotyan and Vargas) ](https://dl.acm.org/doi/abs/10.1145/3377929.3390004)
*accepted at GECCO 2020* | - | - |
70 | | [A first Step toward Incremental Evolution of Convolutional Neural Networks(Barnes et al. 2020) ](https://dl.acm.org/doi/abs/10.1145/3377929.3389916)
*accepted at GECCO 2020* | - | - |
71 | | [Computational model for neural architecture search(Gottapu 2020) ](https://scholarsmine.mst.edu/cgi/viewcontent.cgi?article=3871&context=doctoral_dissertations) | - | - |
72 | | [Neural Architecture Search for extreme multi-label classification: an evolutionary approach(Pauletto et al. 2020) ](https://hal.archives-ouvertes.fr/hal-02889047/document) | - | - |
73 | | [Hyperparameter Optimization in Neural Networks via Structured Sparse Recovery(Cho et al. 2020) ](https://arxiv.org/abs/2007.04087) | - | - |
74 | | [Journey Towards Tiny Perceptual Super-Resolution(Lee et al. 2020) ](https://arxiv.org/abs/2007.04356) | - | - |
75 | | [Self-supervised Neural Architecture Search(Kaplan and Giryes 2020) ](https://arxiv.org/abs/2007.01500) | - | - |
76 | | [Blocks for Image Classification(Wang et al. 2020) ](https://arxiv.org/abs/2007.01556) | - | - |
77 | | [Multi-Objective Neural Architecture Search Based on Diverse Structures and Adaptive Recommendation(Wang et al. 2020) ](https://arxiv.org/abs/2007.02749) | - | - |
78 | | [Parametric machines: a fresh approach to architecture search(Vertechi et al. 2020) ](https://arxiv.org/abs/2007.02777) | - | - |
79 | | [Discretization-Aware Architecture Search(Tian et al. 2020) ](https://arxiv.org/abs/2007.03154) | - | - |
80 | | [GOLD-NAS: Gradual, One-Level, Differentiable(Bi et al. 2020) ](https://arxiv.org/abs/2007.03331) | - | - |
81 | | [Surrogate-assisted Particle Swarm Optimisation for Evolving Variable-length Transferable(Wang et al. 2020) ](https://arxiv.org/abs/2007.01556) | - | - |
82 | | [M-NAS: Meta Neural Architecture Search(Wang et al. 2020) ](https://aaai.org/ojs/index.php/AAAI/article/view/6084)
*accepted at AAAI 2020* | - | - |
83 | | [FiFTy: Large-scale File Fragment Type Identification using Convolutional Neural Networks(Mittal et al. 2020) ](https://ieeexplore.ieee.org/abstract/document/9122499)
*accepted at IEEE Transactions on Information Forensics and Security* | - | - |
84 | | [RSNet: The Search for Remote Sensing Deep Neural Networks in Recognition Tasks(Wang et al. 2020) ](https://ieeexplore.ieee.org/abstract/document/9123590)
*accepted at IEEE Transactions on Geoscience and Remote Sensing* | - | - |
85 | | [Theory-Inspired Path-Regularized Differential Network Architecture Search(Zhou et al. 2020) ](https://arxiv.org/abs/2006.16537) | - | - |
86 | | [The Heterogeneity Hypothesis: Finding Layer-Wise Dissimilated Network Architecture(Li et al. 2020) ](https://arxiv.org/abs/2006.16242) | - | - |
87 | | [Semi-Discrete Optimization Through Semi-Discrete Optimal Transport: A Framework for Neural Architecture Search(Trillos and Morales 2020) ](https://arxiv.org/abs/2006.15221) | - | - |
88 | | [Traditional And Accelerated Gradient Descent for Neural Architecture Search(Trillos et al. 2020) ](https://arxiv.org/abs/2006.15218) | - | - |
89 | | [AutoSNAP: Automatically Learning Neural Architectures for Instrument Pose Estimation(Kügler et al. 2020) ](https://arxiv.org/abs/2006.14858) | - | - |
90 | | [Evolutionary Recurrent Neural Architecture Search(Tian et al. 2020) ](https://ieeexplore.ieee.org/abstract/document/9129784)
*accepted at IEEE Embedded System Letters* | - | - |
91 | | [Neural-Architecture-Search-Based Multiobjective Cognitive Automation System(Wang et al. 2020) ](https://ieeexplore.ieee.org/abstract/document/9127493)
*accepted at IEEE System Journal* | - | - |
92 | | [Enhancing Model Parallelism in Neural Architecture Search for Multi-device System(Fu et al. 2020) ](https://ieeexplore.ieee.org/abstract/document/9127125)
*accepted at IEEE Micro* | - | - |
93 | | [AutoST: Efficient Neural Architecture Search for Spatio-Temporal Prediction(Li et al. 2020) ](http://urban-computing.com/pdf/AutoST_kdd20_camera_ready.pdf)
*accepted at KDD 2020* | - | - |
94 | | [Neural Architecture Search for Sparse DenseNets with Dynamic Compression(O’Neill et al. 2020) ](https://dl.acm.org/doi/abs/10.1145/3377930.3390178)
*accepted at GECCO 2020* | - | - |
95 | | [Searching towards Class-Aware Generators for Conditional Generative Adversarial Networks(Zhou et al. 2020) ](https://arxiv.org/abs/2006.14208) | - | - |
96 | | [Neural Architecture Design for GPU-Efficient Networks(Lin et al. 2020) ](https://arxiv.org/abs/2006.14090) | - | - |
97 | | [Equivalence in Deep Neural Networks via Conjugate Matrix Ensembles(Süzen 2020) ](https://arxiv.org/abs/2006.13687) | - | - |
98 | | [Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL(Zimmer et al. 2020) ](https://arxiv.org/abs/2006.13799) | - | - |
99 | | [NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search(Panda et al. 2020) ](https://arxiv.org/abs/2006.13314) | - | - |
100 | | [Tiny Video Networks: Architecture Search for Efficient Video Models(Piergiovanni et al. 2020) ](https://storage.googleapis.com/pub-tools-public-publication-data/pdf/b2238e19f1f4abdf17c5fff0f3fa824c5eee1e78.pdf)
*accepted at 7th ICML Workshop on Automated Machine Learning, 2020* | - | - |
101 | | [FNA++: Fast Network Adaptation via Parameter Remapping and Architecture Search(Fang et al. 2020) ](https://arxiv.org/abs/2006.12986) | - | - |
102 | | [Neural networks adapting to datasets: learning network size and topology(Janik and Nowak 2020) ](https://arxiv.org/abs/2006.12195) | - | - |
103 | | [AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning(Li et al. 2020) ](https://arxiv.org/abs/2006.11321) | - | - |
104 | | [Reinforcement Learning Aided Network Architecture Generation for JPEG Image Steganalysis(Yang et al. 2020) ](https://dl.acm.org/doi/proceedings/10.1145/3369412)
*accepted at Proceedings of the 2020 ACM Workshop on Information Hiding and Multimedia Security* | - | - |
105 | | [Neural Architecture Search for Time Series Classification(Rakhshani et al. 2020) ](https://germain-forestier.info/publis/ijcnn2020.pdf)
*accepted at ijcnn 2020* | - | - |
106 | | [Cyclic Differentiable Architecture Search(Yu et al. 2020) ](https://arxiv.org/abs/2006.10724) | - | - |
107 | | [Differentially-private Federated Neural Architecture Search(Singh et al. 2020) ](https://arxiv.org/abs/2006.10559) | - | - |
108 | | [DrNAS: Dirichlet Neural Architecture Search(Chen et al. 2020) ](https://arxiv.org/abs/2006.10355) | - | - |
109 | | [Neural Architecture Optimization with Graph VAE(Li et al. 2020) ](https://arxiv.org/abs/2006.10310) | - | - |
110 | | [Fine-Grained Stochastic Architecture Search(Chaudhuri et al. 2020) ](https://arxiv.org/abs/2006.09581) | - | - |
111 | | [Bonsai-Net: One-Shot Neural Architecture Search via Differentiable Pruners(Geada et al. 2020) ](https://arxiv.org/abs/2006.09264) | - | - |
112 | | [AlphaGAN: Fully Differentiable Architecture Search for Generative Adversarial Networks(Tian et al. 2020) ](https://arxiv.org/abs/2006.09134) | - | - |
113 | | [Fine-Tuning DARTS for Image Classification(Tanveer et al. 2020) ](https://arxiv.org/abs/2006.09042) | - | - |
114 | | [Neural Anisotropy Directions(Ortiz-Jiménez et al. 2020) ](https://arxiv.org/abs/2006.09717) | - | - |
115 | | [CryptoNAS: Private Inference on a ReLU Budget(Ghodsi et al. 2020) ](https://arxiv.org/abs/2006.08733) | - | - |
116 | | [Heuristic Architecture Search Using Network Morphism for Chest X-Ray Classification(Radiuk and Kutucu 2020) ](http://ceur-ws.org/Vol-2623/paper11.pdf) | - | - |
117 | | [Task-aware Performance Prediction for Efficient Architecture Search(Kokiopoulou et al. 2020) ](http://ecai2020.eu/papers/256_paper.pdf)
*accepted at ECAI 2020* | - | - |
118 | | [Beyond Network Pruning: a Joint Search-and-Training Approach(Lu et al. 2020) ](http://see.xidian.edu.cn/faculty/wsdong/Papers/Conference/ijcai20.pdf)
*accepted at IJCAI 2020* | - | - |
119 | | [Neural Ensemble Search for Performant and Calibrated Predictions(Zaidi et al. 2020) ](https://arxiv.org/pdf/2006.08573.pdf) | - | - |
120 | | [Multi-fidelity Neural Architecture Search with Knowledge Distillation(Trofimov et al. 2020) ](https://arxiv.org/abs/2006.08341) | - | - |
121 | | [Differentiable Neural Architecture Transformation for Reproducible Architecture Improvement(Kim et al. 2020) ](https://arxiv.org/pdf/2006.08231.pdf) | - | - |
122 | | [Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search(Nguyen et al. 2020) ](https://arxiv.org/abs/2006.07593) | - | - |
123 | | [Neural Architecture Search using Bayesian Optimisation with Weisfeiler-Lehman Kernel(Ru et al. 2020) ](https://arxiv.org/abs/2006.07556) | - | - |
124 | | [NAS-Bench-NLP: Neural Architecture Search Benchmark for Natural Language Processing(Klyuchnikov et al. 2020) ](https://arxiv.org/abs/2006.07116) | - | - |
125 | | [Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?(Yan et el. 2020) ](https://arxiv.org/abs/2006.06936) | - | - |
126 | | [Few-shot Neural Architecture Search(Zhao et al. 2020) ](https://arxiv.org/abs/2006.06863) | - | - |
127 | | [NADS: Neural Architecture Distribution Search for Uncertainty Awareness(Ardywibowo et al. 2020) ](https://arxiv.org/abs/2006.06646) | - | - |
128 | | [Towards Efficient Automated Machine Learning(Li 2020) ](http://reports-archive.adm.cs.cmu.edu/anon/ml2020/CMU-ML-20-104.pdf) | - | - |
129 | | [AMER: Automatic Behavior Modeling and Interaction Exploration in Recommender System(Zhao et al. 2020) ](https://arxiv.org/abs/2006.05933) | - | - |
130 | | [Neuroevolution in Deep Neural Networks: Current Trends and Future Challenges(Galvan and Mooney 2020) ](https://arxiv.org/abs/2006.05415) | - | - |
131 | | [AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks(Fu et al. 2020) ](https://arxiv.org/abs/2006.08198)
*accepted at ICML 2020* | - | - |
132 | | [Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?(Yan et al. 2020) ](https://arxiv.org/abs/2006.06936) | - | - |
133 | | [Hardware-Aware Transformable Architecture Search with Efficient Search Space(Jiang et al. 2020) ](https://ieeexplore.ieee.org/abstract/document/9102721)
*accepted at accpeted at ICME 2020* | - | - |
134 | | [Sparse CNN Archtitecture Search(Yeshwanth et al. 2020) ](https://ieeexplore.ieee.org/abstract/document/9102879)
*accepted at ICME 2020* | - | - |
135 | | [Auto-Generating Neural Networks with Reinforcement Learning for Multi-Purpose Image Forensics(Wei et al. 2020) ](https://ieeexplore.ieee.org/abstract/document/9102943)
*accepted at ICME 2020* | - | - |
136 | | [Neural Architecture Search without Training(Mellor et al. 2020) ](https://arxiv.org/abs/2006.04647) | - | - |
137 | | [Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search(Ru et al. 2020) ](https://arxiv.org/abs/2006.04492) | - | - |
138 | | [Differentiable Neural Input Search for Recommender Systems(Cheng et al. 2020) ](https://arxiv.org/abs/2006.04466) | - | - |
139 | | [Efficient Architecture Search for Continual Learning(Gao et al. 2020) ](https://arxiv.org/abs/2006.04027) | - | - |
140 | | [Conditional Neural Architecture Search(Kao et al. 2020) ](https://arxiv.org/abs/2006.03969) | - | - |
141 | | [AutoHAS: Differentiable Hyper-parameter and Architecture Search(Dong et al. 2020) ](https://arxiv.org/abs/2006.03656) | - | - |
142 | | [Modeling Task-based fMRI Data via Deep Belief Network with Neural Architecture Search(Qiang et al. 2020) ](https://www.sciencedirect.com/science/article/abs/pii/S0895611120300501)
*accepted at Computerized Medical Imaging and Graphics* | - | - |
143 | | [Fast Hardware-Aware Neural Architecture Search(Zhang et al. 2020) ](http://openaccess.thecvf.com/content_CVPRW_2020/papers/w40/Zhang_Fast_Hardware-Aware_Neural_Architecture_Search_CVPRW_2020_paper.pdf)
*accepted at CVPR 2020 workshop* | - | - |
144 | | [Memory-Efficient Hierarchical Neural Architecture Search for Image Denoising(Zhang et al. 2020) ](http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_Memory-Efficient_Hierarchical_Neural_Architecture_Search_for_Image_Denoising_CVPR_2020_paper.pdf)
*accepted at CVPR 2020* | - | - |
145 | | [GP-NAS: Gaussian Process based Neural Architecture Search(Li et al. 2020) ](http://openaccess.thecvf.com/content_CVPR_2020/papers/Li_GP-NAS_Gaussian_Process_Based_Neural_Architecture_Search_CVPR_2020_paper.pdf)
*accepted at CVPR 2020* | - | - |
146 | | [MemNAS: Memory-Efficient Neural Architecture Search with Grow-Trim Learning(Liu et al.2020) ](http://openaccess.thecvf.com/content_CVPR_2020/papers/Liu_MemNAS_Memory-Efficient_Neural_Architecture_Search_With_Grow-Trim_Learning_CVPR_2020_paper.pdf)
*accepted at CVPR 2020* | - | - |
147 | | [Can weight sharing outperform random architecture search? An investigation with TuNAS(Bender et al. 2020) ](http://openaccess.thecvf.com/content_CVPR_2020/papers/Bender_Can_Weight_Sharing_Outperform_Random_Architecture_Search_An_Investigation_With_CVPR_2020_paper.pdf)
*accepted at CVPR 2020* | - | - |
148 | | [Butterfly Transform: An Efficient FFT Based Neural Architecture Design(Alizadeh vahid et al. 2020) ](http://openaccess.thecvf.com/content_CVPR_2020/papers/vahid_Butterfly_Transform_An_Efficient_FFT_Based_Neural_Architecture_Design_CVPR_2020_paper.pdf)
*accepted at CVPR 2020* | - | - |
149 | | [APQ: Joint Search for Network Architecture, Pruning and Quantization Policy(Wang et al.2020) ](http://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_APQ_Joint_Search_for_Network_Architecture_Pruning_and_Quantization_Policy_CVPR_2020_paper.pdf)
*accepted at CVPR 2020* | - | - |
150 | | [SP-NAS: Serial-to-Parallel Backbone Search for Object Detection(Jiang et al. 2020) ](http://openaccess.thecvf.com/content_CVPR_2020/papers/Jiang_SP-NAS_Serial-to-Parallel_Backbone_Search_for_Object_Detection_CVPR_2020_paper.pdf)
*accepted at CVPR 2020* | - | - |
151 | | [All in One Bad Weather Removal using Architectural Search(Li et al. 2020) ](http://openaccess.thecvf.com/content_CVPR_2020/papers/Li_All_in_One_Bad_Weather_Removal_Using_Architectural_Search_CVPR_2020_paper.pdf)
*accepted at CVPR 2020* | - | - |
152 | | [NeuralScale: Efficient Scaling of Neurons for Resource-Constrained Deep Neural Networks(Lee and Lee) ](http://openaccess.thecvf.com/content_CVPR_2020/papers/Lee_NeuralScale_Efficient_Scaling_of_Neurons_for_Resource-Constrained_Deep_Neural_Networks_CVPR_2020_paper.pdf)
*accepted at CVPR 2020* | - | - |
153 | | [On Network Design Spaces for Visual Recognition(Radosavovic et al. 2020) ](https://research.fb.com/wp-content/uploads/2019/08/On-Network-Design-Spaces-for-Visual-Recognition.pdf) | - | - |
154 | | [A Comprehensive Survey of Neural Architecture Search: Challanges and Solutions(Ren et al. 2020) ](https://arxiv.org/abs/2006.02903) | - | - |
155 | | [FBNetV3: Joint Architecture-Recipe Search using Neural Acquisition Function(Dai et al. 2020) ](https://arxiv.org/abs/2006.02049) | - | - |
156 | | [Neural Architecture Search With Reinforce And Masked Attention Autoregressive Density Estimators(Krishna et al. 2020) ](https://arxiv.org/abs/2006.00939) | - | - |
157 | | [Automation of Deep Learning – Theory and Practice(Wistuba et al. 2020) ](https://dl.acm.org/doi/abs/10.1145/3372278.3390739)
*accepted at ICMR 202* | - | - |
158 | | [AdaEn-Net: An Ensemble of Adaptive 2D-3D Fully Convolutional Networks for Medical Image Segmentation(Baldeon Calisto and Lai-Yuen. 2020) ](https://www.sciencedirect.com/science/article/pii/S0893608020300848)
*accepted at Neural Networks* | - | - |
159 | | [DC-NAS: Divide-and-Conquer Neural Architecture Search(Wang et al. 2020) ](https://arxiv.org/abs/2005.14456) | - | - |
160 | | [HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens(Yang et al. 2020) ](https://arxiv.org/abs/2005.14446) | - | - |
161 | | [Designing Resource-Constrained Neural Networks Using Neural Architecture Search Targeting Embedded Devices(Cassimon et al. 2020) ](https://www.sciencedirect.com/science/article/pii/S2542660520300676)
*accepted at IEEE Internet of Things* | - | - |
162 | | [Searching Better Architectures for Neural Machine Translation(Fan et al. 2020) ](https://ieeexplore.ieee.org/abstract/document/9095246)
*accepted at IEEE/ACM Transactions on Audio, Speech, and Language Processing* | - | - |
163 | | [Automated Design of Neural Network Architectures with Reinforcement Learning for Detection of Global Manipulations(Chen et al. 2020) ](https://ieeexplore.ieee.org/abstract/document/9103245)
*accepted at IEEE Journal of Selected Topics in Signal Processing* | - | - |
164 | | [A New Deep Neural Architecture Search Pipeline for Face Recognition(Zhu et al. 2020) ](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9091879)
*accepted at IEEE Access* | - | - |
165 | | [Regularized Evolution for Marco Neural Architecture Search(Kyriakides and Margaritis) ](https://link.springer.com/chapter/10.1007/978-3-030-49186-4_10)
*accepted at AIAI2020* | - | - |
166 | | [Evolutionary NAS with Gene Expression Programming of Cellular Encoding(Broni-Bediako et al. 2020) ](https://arxiv.org/abs/2005.13110) | - | - |
167 | | [Synthetic Petri Dish: A Novel Surrogate Model for Rapid Architecture Search(Rawal et al. 2020) ](https://arxiv.org/abs/2005.13092) | - | - |
168 | | [Designing Convolutional Neural Network Architectures Using Cartesian Genetic Programming(Suganuma et al. 2020) ](https://link.springer.com/chapter/10.1007/978-981-15-3685-4_7)
*accepted at accepted in book on “Deep Neural Evolution”* | - | - |
169 | | [An Introduction to Neural Architecture Search for Convolutional Networks(Kyriakides and Margaritis, 2020) ](https://arxiv.org/abs/2005.11074) | - | - |
170 | | [AutoSegNet: An Automated Neural Network for Image Segmentation(Xu et al. 2020) ](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9095283)
*accepted at IEEE Access* | - | - |
171 | | [DMS: Differentiable Dimension Search for Binary Neural Networks(Li et al. 2020) ](https://xhplus.github.io/publication/conference-paper/iclr2020/dms/DMS.pdf)
*accepted at 1st Workshop on Neural Architecture Search at ICLR 2020* | - | - |
172 | | [Evolving Deep Neural Networks for X-ray Based Detection of Dangerous Objects(Tsukada et al. 2020) ](https://link.springer.com/chapter/10.1007/978-981-15-3685-4_12)
*accepted at accepted in book on “Deep Neural Evolution”* | - | - |
173 | | [Powering One-shot Topological NAS with Stabilized Share-parameter Proxy(Guo et al. 2020) ](https://arxiv.org/abs/2005.10511) | - | - |
174 | | [Optimize CNN Model for FMRI Signal Classification Via Adanet-Based Neural Architecture Search(Dai et al. 2020) ](https://ieeexplore.ieee.org/abstract/document/9098574)
*accepted at IEEE ISBI* | - | - |
175 | | [Rethinking Performance Estimation in Neural Architecture Search(Zheng et al. 2020) ](https://arxiv.org/abs/2005.09917)
*accepted at CVPR 2020* | - | - |
176 | | [Application of a genetic algorithm to search for the optimal convolutional neural network architecture with weight distribution(Radiuk 2020) ](http://elar.khnu.km.ua/jspui/bitstream/123456789/8960/1/%D0%A0%D0%90%D0%94%D0%AE%D0%9A.pdf) | - | - |
177 | | [HNAS: Hierarchical Neural Architecture Search on Mobile Devices(Xia et al. 2020) ](https://arxiv.org/abs/2005.07564) | - | - |
178 | | [Improving Neuroevolution Using Island Extinction And Repopulation(Lyu et al. 2020) ](https://arxiv.org/abs/2005.07376) | - | - |
179 | | [A Framework for Exploring and Modelling Neural Architecture Search Methods(Radiuk et al. 2020) ](http://ceur-ws.org/Vol-2604/paper70.pdf) | - | - |
180 | | [You Only Search Once: A Fast Automation Framework for Single-Stage DNN/Accelerator Co-design(Chen et al. 2020) ](https://arxiv.org/abs/2005.07075) | - | - |
181 | | [DARTS-ASR: Differentiable Architecture Search for Multilingual Speech Recognition and Adaptation(Chen et al. 2020) ](https://arxiv.org/abs/2005.07029) | - | - |
182 | | [A Semi-Supervised Assessor of Neural Architectures(Tang et al. 2020) ](https://arxiv.org/abs/2005.06821)
*accepted at CVPR 2020* | - | - |
183 | | [Neural Architecture Search for Gliomas Segmentation on Multimodal Magnetic Resonance Imaging(Wang et al. 2020) ](https://arxiv.org/abs/2005.06338) | - | - |
184 | | [Binarizing MobileNet via Evolution-based Searching(Phan et al. 2020) ](https://arxiv.org/abs/2005.06305) | - | - |
185 | | [Neural Architecture Transfer(Lu et al. 2020) ](https://arxiv.org/abs/2005.05859) | - | - |
186 | | [Optimization of deep neural networks: a survey and unified taxonomy(Talbi 2020) ](https://hal.inria.fr/hal-02570804/document) | - | - |
187 | | [Auto-Fas: Searching Lightweight Networks for Face Anti-Spoofing(Yu et al. 2020) ](https://ieeexplore.ieee.org/abstract/document/9053587)
*accepted at accetped at ICASSP 2020* | - | - |
188 | | [Neuro Evolutional with Game-Driven Cultural Algorithms(Waris and Reynolds 2020) ](https://www.researchgate.net/profile/Faisal_Waris/publication/341099885_Neuro_Evolutional_with_Game-Driven_Cultural_Algorithms/links/5eadf89c45851592d6b4a953/Neuro-Evolutional-with-Game-Driven-Cultural-Algorithms.pdf)
*accepted at ACM GECCO 2020* | - | - |
189 | | [NASIL: Neural Architecture Search With Imitation Learning(Fard et al. 2020) ](https://ieeexplore.ieee.org/document/9054748)
*accepted at ICASSP 2020* | - | - |
190 | | [Noisy Differentiable Architecture Search(Chu et al. 2020) ](https://arxiv.org/abs/2005.03566) | - | - |
191 | | [AutoSpeech: Neural Architecture Search for Speaker Recognition(Ding et al. 2020) ](https://arxiv.org/abs/2005.03215) | - | - |
192 | | [Learning Architectures from an Extended Search Space for Language Modeling(Li et al. 2020) ](https://arxiv.org/abs/2005.02593) | - | - |
193 | | [CP-NAS: Child-Parent Neural Architecture Search for 1-bit CNNs( Zhuo et al. 2020) ](https://arxiv.org/abs/2005.00057) | - | - |
194 | | [Particle Swarm Optimization for Evolving Deep Convolutional Neural Networks for Image Classification: Single- and Multi-Objective Approaches(Wang et al. 2020) ](https://link.springer.com/chapter/10.1007/978-981-15-3685-4_6)
*accepted at accepted in book on “Deep Neural Evolution”* | - | - |
195 | | [Optimizing Neural Architecture Search using Limited GPU Time in a Dynamic Search Space: A Gene Expression Programming Approach(Alves and de Oliveira. 2020) ](https://arxiv.org/abs/2005.07669)
*accepted at IEEE CEC* | - | - |
196 | | [Local Search is State of the Art for Neural Architecture Search Benchmarks(White et al. 2020) ](https://arxiv.org/abs/2005.02960)
*accepted at AutoML workshop at ICML’20* | - | - |
197 | | [SIPA: A Simple Framework for Efficient Networks(Lee et al. 2020) ](https://arxiv.org/abs/2004.14476) | - | - |
198 | | [Neural Architecture Search Based on Model Statistics for Wildlife Identification(Jia et al. 2020) ](https://www.sciencedirect.com/science/article/abs/pii/S0016003220302076)
*accepted at Journal of the Franklin Institute* | - | - |
199 | | [The effect of reduced training in neural architecture search(Kyriakides and Margaritis. 2020) ](https://link.springer.com/article/10.1007%2Fs00521-020-04915-6)
*accepted at Neural Comput & Applic* | - | - |
200 | | [Efficient Evolutionary Neural Architecture Search(Tan et al. 2020) ](https://link.springer.com/chapter/10.1007%2F978-981-15-3425-6_61)
*accepted at BIC-TA’20* | - | - |
201 | | [MobileDets: Searching for Object Detection Architectures for Mobile Accelerators( Xiong et al. 2020) ](https://arxiv.org/abs/2004.14525) | - | - |
202 | | [Angle-based Search Space Shrinking for Neural Architecture Search(Hu et al. 2020) ](https://arxiv.org/abs/2004.13431) | - | - |
203 | | [AutoHR: A Strong End-to-end Baseline for Remote Heart Rate Measurement with Neural Searching(Yu et al. 2020) ](https://arxiv.org/abs/2004.12292) | - | - |
204 | | [Deep Multimodal Neural Architecture Search(Yu et al. 2020) ](https://arxiv.org/abs/2004.12070) | - | - |
205 | | [Depth-Wise Neural Architecture Search(Jordao et al. 2020) ](https://arxiv.org/abs/2004.11178) | - | - |
206 | | [Recurrent Neural Network Architecture Search for Geophyiscal Emulation(Maulik et al. 2020) ](https://arxiv.org/abs/2004.10928) | - | - |
207 | | [Local Search is a Remarkably Strong Baseline for Neural Architecture Search(Ottelander et al. 2020) ](https://arxiv.org/abs/2004.08996) | - | - |
208 | | [Superkernel Neural Architecture Search for Image Denoising(Mozejko et al. 2020) ](https://arxiv.org/abs/2004.08870)
*accepted at NTIRE2020 Workshop at CVPR 2020* | - | - |
209 | | [Organ at Risk Segmentation for Head and Neck Cancer using Stratified Learning and Neural Architecture Search(Guo et al. 2020) ](https://arxiv.org/abs/2004.08426) | - | - |
210 | | [Fitting the Search Space of Weight-sharing NAS with Graph Convolutional Networks(Chen et al. 2020) ](https://arxiv.org/abs/2004.08423) | - | - |
211 | | [A Neural Architecture Search based Framework for Liquid State Machine Design(Tian et al. 2020) ](https://arxiv.org/abs/2004.07864) | - | - |
212 | | [Geometry-Aware Gradient Algorithms for Neural Architecture Search(Li et al. 2020) ](https://arxiv.org/abs/2004.07802) | - | - |
213 | | [Distributed Evolution of Deep Autoencoders(Hajewski et al. 2020) ](https://arxiv.org/abs/2004.07607) | - | - |
214 | | [FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions(Wan et al. 2020) ](https://arxiv.org/abs/2004.05565) | - | - |
215 | | [ModuleNet: Knowledge-inherited Neural Architecture Search(Chen et al. 2020) ](https://arxiv.org/abs/2004.05020) | - | - |
216 | | [Evolutionary recurrent neural network for image captioning(Wang et al. 2020) ](https://www.sciencedirect.com/science/article/abs/pii/S0925231220304744)
*accepted at Neurocomputing* | - | - |
217 | | [Neural Architecture Search for Lightweight Non-Local Networks(Li et al. 2020) ](https://arxiv.org/abs/2004.01961) | - | - |
218 | | [A Generic Graph-based Neural Architecture Encoding Scheme for Predictor-based NAS(Ning et al. 2020) ](https://arxiv.org/abs/2004.01899)
*accepted at ECCV 2020* | - | [Github](https://github.com/walkerning/aw_nas) |
219 | | [FedNAS: Federated Deep Learning via Neural Architecture Search(He et al. 2020) ](https://chaoyanghe.com/publications/FedNAS-CVPR2020-NAS.pdf)
*accepted at CVPR 2020 Workshop on Neural Architecture Search and Beyond for Representation Learning* | - | - |
220 | | [Neural architecture search based on model pool for wildlife identification(Jia et al. 2020) ](https://www.sciencedirect.com/science/article/abs/pii/S092523122030388X)
*accepted at Neurocomputing* | - | - |
221 | | [An Evolutionary Approach to Variational Autoencoders(Hajewski and Oliveira. 2020) ](https://ieeexplore.ieee.org/abstract/document/9031239)
*accepted at CCWC’20* | - | - |
222 | | [A Scalable System for Neural Architecture Search(Hajewski and Oliveira. 2020) ](https://ieeexplore.ieee.org/abstract/document/9031181)
*accepted at CCWC’20* | - | - |
223 | | [Neural Architecture Generator Optimization(Ru et al. 2020) ](https://arxiv.org/abs/2004.01395) | - | - |
224 | | [Deep-n-Cheap: An Automated Search Framework for Low Complexity Deep Learning(Dey et al. 2020) ](https://arxiv.org/abs/2004.00974) | - | - |
225 | | [MTL-NAS: Task-Agnostic Neural Architecture Search towards General-Purpose Multi-Task Learning(Gao et al. 2020) ](https://arxiv.org/abs/2003.14058)
*accepted at CVPR’20* | - | - |
226 | | [Designing Network Design Spaces(Radosavovic et al. 2020) ](https://arxiv.org/abs/2003.13678)
*accepted at CVPR’20* | - | - |
227 | | [Disturbance-immune Weight Sharing for Neural Architecture Search(Niu et al. 2020) ](https://arxiv.org/abs/2003.13089) | - | - |
228 | | [NPENAS:Neural Predictor Guided Evolution for Neural Architecture Search(Wei et al. 2020) ](https://arxiv.org/abs/2003.12857) | - | - |
229 | | [DA-NAS: Data Adapted Pruning for Efficient Neural Architecture Search(Dai et al. 2020) ](https://arxiv.org/abs/2003.12563) | - | - |
230 | | [MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation(He et al. 2020) ](https://arxiv.org/abs/2003.12238)
*accepted at CVPR’20* | - | - |
231 | | [Are Labels Necessary for Neural Architecture Search?(Liu et al. 2020) ](https://arxiv.org/abs/2003.12056) | - | - |
232 | | [DCNAS: Densely Connected Neural Architecture Search for Semantic Image Segmentation(Zhang et al. 2020) ](https://arxiv.org/abs/2003.11883) | - | - |
233 | | [Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection(Guo et al. 2020) ](https://arxiv.org/abs/2003.11818)
*accepted at CVPR 2020* | - | - |
234 | | [Sampled Training and Node Inheritance for Fast Evolutionary Neural Architecture Search(Zhang et al. 2020) ](https://arxiv.org/abs/2003.11613) | - | - |
235 | | [GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernet(You et al. 2020) ](https://arxiv.org/abs/2003.11236)
*accepted at CVPR’2020* | - | - |
236 | | [BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage Models(Yu et al. 2020) ](https://arxiv.org/abs/2003.11142) | - | - |
237 | | [Steepest Descent Neural Architecture Optimization: Escaping Local Optimum with Signed Neural Splitting(Wu et al. 2020) ](https://arxiv.org/abs/2003.10392) | - | - |
238 | | [BS-NAS: Broadening-and-Shrinking One-Shot NAS with Searchable Numbers of Channels(Shen et al. 2020) ](https://arxiv.org/abs/2003.09821) | - | - |
239 | | [Probabilistic Dual Network Architecture Search on Graphs(Zhao et al. 2020) ](https://arxiv.org/abs/2003.09676) | - | - |
240 | | [GAN Compression: Efficient Architectures for Interactive Conditional GAN(Li et al. 2020) ](https://arxiv.org/abs/2003.08936) | - | - |
241 | | [ElixirNet: Relation-aware Network Architecture Adaptation for Medical Lesion Detection(Jiang et al. 2020) ](https://arxiv.org/abs/2003.08770) | - | - |
242 | | [Lifelong Learning with Searchable Extension Units(Wang et al. 2020) ](https://arxiv.org/abs/2003.08559) | - | - |
243 | | [Efficient Backbone Search for Scene Text Recognition(Zhang et al. 2020) ](https://arxiv.org/abs/2003.06567) | - | - |
244 | | [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data(Erickson et al. 2020) ](https://arxiv.org/abs/2003.06505) | - | - |
245 | | [PONAS: Progressive One-shot Neural Architecture Search for Very Efficient Deployment(Huang and Chu. 2020) ](https://arxiv.org/abs/2003.05112) | - | - |
246 | | [Hierarchical Neural Architecture Search for Single Image Super-Resolution(Guo et al. 2020) ](https://arxiv.org/abs/2003.04619) | - | - |
247 | | [How to Train Your Super-Net: An Analysis of Training Heuristics in Weight-Sharing NAS(Yu et al. 2020) ](https://arxiv.org/abs/2003.04276) | - | - |
248 | | [AutoML-Zero: Evolving Machine Learning Algorithms From Scratch(Real et al. 2020) ](https://arxiv.org/abs/2003.03384) | - | - |
249 | | [Accelerator-Aware Neural Network Design Using AutoML(Gupta and Akin. 2020) ](https://arxiv.org/abs/2003.02838)
*accepted at On-device Intelligence Workshop at MLSys’20* | - | - |
250 | | [Real-time Federated Evolutionary Neural Architecture Search(Zhu and Jin. 2020) ](https://arxiv.org/abs/2003.02793) | - | - |
251 | | [BATS: Binary ArchitecTure Search(Bulat et al. 2020) ](https://arxiv.org/abs/2003.01711)
*accepted at ECCV’20* | - | - |
252 | | [ADWPNAS: Architecture-Driven Weight Prediction for Neural Architecture Search(Zhang et al. 2020) ](https://arxiv.org/abs/2003.01335) | - | - |
253 | | [NAS-Count: Counting-by-Density with Neural Architecture Search(Hu et al. 2020) ](https://arxiv.org/abs/2003.00217) | - | - |
254 | | [ImmuNetNAS: An Immune-network approach for searching Convolutional Neural Network Architectures(Kefan and Pang. 2020) ](https://arxiv.org/abs/2002.12704) | - | - |
255 | | [Neural Inheritance Relation Guided One-Shot Layer Assignment Search(Meng et al. 2020) ](https://arxiv.org/abs/2002.12580) | - | - |
256 | | [Automatically Searching for U-Net Image Translator Architecture(Shu and Wang. 2020) ](https://arxiv.org/abs/2002.11581) | - | - |
257 | | [AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations(Zhao et al. 2020) ](https://arxiv.org/abs/2002.11252) | - | - |
258 | | [Memory-Efficient Models for Scene Text Recognition via Neural Architecture Search(Hong et al. 2020) ](http://openaccess.thecvf.com/content_WACVW_2020/papers/w3/Hong_Memory-Efficient_Models_for_Scene_Text_Recognition_via_Neural_Architecture_Search_WACVW_2020_paper.pdf)
*accepted at WACV’20 workshop* | - | - |
259 | | [Search for Winograd-Aware Quantized Networks(Fernandez-Marques et al. 2020) ](https://arxiv.org/abs/2002.10711) | - | - |
260 | | [Semi-Supervised Neural Architecture Search(Luo et al. 2020) ](https://arxiv.org/abs/2002.10389) | - | - |
261 | | [Neural Architecture Search for Compressed Sensing Magnetic Resonance Image Reconstruction(Yan et al. 2020) ](https://arxiv.org/abs/2002.09625) | - | - |
262 | | [DSNAS: Direct Neural Architecture Search without Parameter Retraining(Hu et al. 2020) ](https://arxiv.org/abs/2002.09128) | - | - |
263 | | [Neural Architecture Search For Fault Diagnosis(Li et al. 2020) ](https://arxiv.org/abs/2002.07997)
*accepted at ESREL’20* | - | - |
264 | | [Learning Architectures for Binary Networks(Kim et al. 2020) ](https://arxiv.org/abs/2002.06963)
*accepted at ECCV’20* | - | - |
265 | | [Efficient Evolutionary Architecture Search for CNN Optimization on GTSRB(Johner and Wassner. 2020) ](https://ieeexplore.ieee.org/abstract/document/8999305/)
*accepted at ICMLA’19* | - | - |
266 | | [Automating Deep Neural Network Model Selection for Edge Inference(Lu et al. 2020) ](https://ieeexplore.ieee.org/abstract/document/8998995)
*accepted at CogMI’20* | - | - |
267 | | [Neural Architecture Search over Decentralized Data(Xu et al. 2020) ](https://arxiv.org/abs/2002.06352) | - | - |
268 | | [Automatic Structural Search for Multi-task Learning VALPs(Garciarena et al. 2020) ](https://link.springer.com/chapter/10.1007/978-3-030-41913-4_3)
*accepted at OLA’20* | - | - |
269 | | [RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning(Alletto et al. 2020) ](http://eval.how/aaai-2020/REAIS19_p9.pdf)
*accepted at Meta-Eval 2020 workshop* | - | - |
270 | | [Classifying the classifier: dissecting the weight space of neural networks(Eilertsen et al. 2020) ](https://arxiv.org/pdf/2002.05688.pdf) | - | - |
271 | | [Stabilizing Differentiable Architecture Search via Perturbation-based Regularization(Chen and Hsieh. 2020) ](https://arxiv.org/abs/2002.05283) | - | - |
272 | | [Best of Both Worlds: AutoML Codesign of a CNN and its Hardware Accelerator(Abdelfattah et al. 2020) ](https://arxiv.org/abs/2002.05022)
*accepted at DAC’20* | - | - |
273 | | [Variational Depth Search in ResNets(Antoran et al. 2020) ](https://arxiv.org/abs/2002.02797) | - | - |
274 | | [Co-Exploration of Neural Architectures and Heterogeneous ASIC Accelerator Designs Targeting Multiple Tasks(Yang et al. 2020) ](https://arxiv.org/abs/2002.04116)
*accepted at DAC’20* | - | - |
275 | | [FPNet: Customized Convolutional Neural Network for FPGA Platforms(Yang et al. 2020) ](https://ieeexplore.ieee.org/abstract/document/8977837)
*accepted at FPT’20* | - | - |
276 | | [AutoFCL: Automatically Tuning Fully Connected Layers for Transfer Learning(Basha et al. 2020) ](https://arxiv.org/abs/2001.11951) | - | - |
277 | | [NASS: Optimizing Secure Inference via Neural Architecture Search(Bian et al. 2020) ](https://arxiv.org/abs/2001.11854)
*accepted at ECAI’20* | - | - |
278 | | [Search for Better Students to Learn Distilled Knowledge(Gu et al. 2020) ](https://arxiv.org/abs/2001.11612) | - | - |
279 | | [Bayesian Neural Architecture Search using A Training-Free Performance Metric(Camero et al. 2020) ](https://arxiv.org/abs/2001.10726) | - | - |
280 | | [NAS-Bench-1Shot1: Benchmarking and Dissecting One-Short Neural Architecture Search(Zela et al. 2020) ](https://arxiv.org/abs/2001.10422)
*accepted at ICLR’20* | - | - |
281 | | [Convolution Neural Network Architecture Learning for Remote Sensing Scene Classification(Chen et al. 2010) ](https://arxiv.org/abs/2001.09614) | - | - |
282 | | [Multi-objective Neural Architecture Search via Non-stationary Policy Gradient(Chen et al. 2020) ](https://arxiv.org/abs/2001.08437) | - | - |
283 | | [Efficient Neural Architecture Search: A Broad Version(Ding et al. 2020) ](https://arxiv.org/abs/2001.06679) | - | - |
284 | | [ENAS U-Net: Evolutionary Neural Architecture Search for Retinal Vessel(Fan et al. 2020) ](https://arxiv.org/abs/2001.06678) | - | - |
285 | | [FlexiBO: Cost-Aware Multi-Objective Optimization of Deep Neural Networks(Iqbal et al. 2020) ](https://arxiv.org/abs/2001.06588) | - | - |
286 | | [Up to two billion times acceleration of scientific simulations with deep neural architecture search(Kasim et al. 2020) ](https://arxiv.org/abs/2001.08055) | - | - |
287 | | [Latency-Aware Differentiable Neural Architecture Search(Xu et al. 2020) ](https://arxiv.org/abs/2001.06392) | - | - |
288 | | [MixPath: A Unified Approach for One-shot Neural Architecture Search(Chu et al. 2020) ](https://arxiv.org/abs/2001.05887) | - | - |
289 | | [Neural Architecture Search for Skin Lesion Classification(Kwasigroch et al. 2020) ](https://ieeexplore.ieee.org/document/8950333)
*accepted at IEEE Access* | - | - |
290 | | [AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search(Chen et al. 2020) ](https://arxiv.org/abs/2001.04246) | - | - |
291 | | [Neural Architecture Search for Deep Image Prior(Ho et al. 2020) ](https://arxiv.org/abs/2001.04776) | - | - |
292 | | [Fast Neural Network Adaptation via Parameter Remapping and Architecture Search(Fang et al. 2020) ](https://arxiv.org/abs/2001.02525)
*accepted at ICLR’20* | - | - |
293 | | [FTT-NAS: Discovering Fault-Tolerant Neural Architecture(Li et al. 2020) ](http://nicsefc.ee.tsinghua.edu.cn/media/publications/2020/ASPDAC20_293_6p4Ghq4.pdf)
*accepted at ASP-DAC 2020* | - | - |
294 | | [Deeper Insights into Weight Sharing in Neural Architecture Search(Zhang et al. 2020) ](https://arxiv.org/abs/2001.01431) | - | - |
295 | | [EcoNAS: Finding Proxies for Economical Neural Architecture Search(Zhou et al. 2020) ](https://arxiv.org/abs/2001.01233)
*accepted at CVPR’20* | - | - |
296 | | [DeepMaker: A multi-objective optimization framework for deep neural networks in embedded systems(Loni et al. 2020) ](https://www.sciencedirect.com/science/article/abs/pii/S0141933119301176)
*accepted at Microprocessors and Microsystems* | - | - |
297 | | [Auto-ORVNet: Orientation-boosted Volumetric Neural Architecture Search for 3D Shape Classification(Ma et al. 2020) ](https://ieeexplore.ieee.org/abstract/document/8939365)
*accepted at IEEE Access* | - | - |
298 | | [NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search(Dong and Yang et al. 2020) ](https://arxiv.org/abs/2001.00326)
*accepted at ICLR’20* | - | - |
299 |
300 | ### 2019
301 |
302 | - [back to top](#2020)
303 |
304 | | Title | Tags | Code |
305 | |:--------|:--------:|:--------:|
306 | | [Scalable NAS with Factorizable Architectural Parameters(Wang et al. 2019) ](https://arxiv.org/abs/1912.13256) | - | - |
307 | | [Modeling Neural Architecture Search Methods for Deep Networks(Malekhosseini et al. 2019) ](https://arxiv.org/abs/1912.13183) | - | - |
308 | | [Searching for Stage-wise Neural Graphs in the Limit(Zhou et al. 2019) ](https://arxiv.org/abs/1912.12860) | - | - |
309 | | [Neural Architecture Search on Acoustic Scene Classification(Li et al. 2019) ](https://arxiv.org/abs/1912.12825) | - | - |
310 | | [RC-DARTS: Resource Constrained Differentiable Architecture Search(Jin et al. 2019) ](https://arxiv.org/abs/1912.12814) | - | - |
311 | | [NAS Evaluation is frustatingly hard(Yang et al. 2019) ](https://arxiv.org/abs/1912.12522)
*accepted at ICLR’20* | - | - |
312 | | [A Genetic Algorithm based Kernel-size Selection Approach for a Multi-column Convolutional Neural Network(Singh et al. 2019) ](https://arxiv.org/abs/1912.12405) | - | - |
313 | | [BetaNAS: Balanced Training and Selective Drop for Neural Architecture Search(Fang et al. 2019) ](https://arxiv.org/abs/1912.11191) | - | - |
314 | | [Progressive DARTS: Bridging the Optimization Gap for NAS in the Wild(Chen et al. 2019) ](https://arxiv.org/abs/1912.10952) | - | - |
315 | | [TextNAS: A Neural Architecture Search Space tailored for Text Representation(Wang et al. 2019) ](https://arxiv.org/abs/1912.10729) | - | - |
316 | | [AtomNAS: Fine-Grined End-To-End Neural Architecture Search(Mei et al. 2019) ](https://arxiv.org/abs/1912.09640)
*accepted at ICLR’20* | - | - |
317 | | [C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation(Yu et al. 2019) ](https://arxiv.org/abs/1912.09628) | - | - |
318 | | [A Reinforcement Neural Architecture Search Method for Rolling Bearing Fault Diagnosis(Wang et al. 2019) ](https://www.sciencedirect.com/science/article/pii/S0263224119312849)
*accepted at Measurement* | - | - |
319 | | [Neural Architecture Search for Optimizing Deep Belief Network Models of fMRI Data(Quiang et al. 2019) ](https://link.springer.com/chapter/10.1007/978-3-030-37969-8_4)
*accepted at MMMI’19* | - | - |
320 | | [QoS-aware Neural Architecture Search(Cheng et al. 2019) ](http://mlforsystems.org/assets/papers/neurips2019/qosnas_cheng_2019.pdf)
*accepted at NeurIPS’19* | - | - |
321 | | [Neural-Hardware Architecture Search(Lin et al. 2019) ](http://mlforsystems.org/assets/papers/neurips2019/neural_hardware_lin_2019.pdf)
*accepted at NeurIPS’19* | - | - |
322 | | [Preventing Information Leakage with Neural Architecture Search(Zhang et al. 2019) ](https://arxiv.org/abs/1912.08421) | - | - |
323 | | [Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data(Such et al. 2019) ](https://arxiv.org/abs/1912.07768) | - | - |
324 | | [UNAS: Differentiable Architecture Search Meets Reinforcement Learning(Vahdat et al. 2019) ](https://arxiv.org/abs/1912.07651) | - | - |
325 | | [Efficient network architecture search via multiobjective particle swarm optimization based on decomposition(Jiang et al. 2019) ](https://www.sciencedirect.com/science/article/abs/pii/S0893608019303971) | - | - |
326 | | [Deep Uncertainty Estimation for Model-based Neural Architecture Search(White et al. 2019) ](http://bayesiandeeplearning.org/2019/papers/26.pdf)
*accepted at workshop on Bayesian Deep Learning at NeurIPS’19* | - | - |
327 | | [A Variational-Sequential Graph Autoencoder for Neural Architecture Performance Prediction(Friede et al. 2019) ](https://arxiv.org/abs/1912.05317) | - | - |
328 | | [STEERAGE: Synthesis of Neural Networks Using Architecture Search and Grow-and-Prune Methods(Hassantabar et al. 2019) ](https://arxiv.org/abs/1912.05831) | - | - |
329 | | [Leveraging End-to-End Speech Recognition with Neural Architecture Search(Baruwa et al. 2019) ](https://arxiv.org/abs/1912.05946) | - | - |
330 | | [Efficient Differentiable Neural Architecture Search with Meta Kernels(Chen et al. 2019) ](https://arxiv.org/abs/1912.04749) | - | - |
331 | | [Neural architecture search for image saliency fusion(Bianco et al. 2019) ](https://www.sciencedirect.com/science/article/abs/pii/S1566253519302374)
*accepted at Information Fusion* | - | - |
332 | | [Ultrafast Photorealistic Style Transfer via Neural Architecture Search(An et al. 2019) ](https://arxiv.org/abs/1912.02398) | - | - |
333 | | [AdversarialNAS: Adversarial Neural Architecture Search for GANs(Gao et al. 2019) ](https://arxiv.org/abs/1912.02037) | - | - |
334 | | [MetAdapt: Meta-Learned Task-Adaptive Architecture for Few-Shot Classification(Doveh et al. 2019) ](https://arxiv.org/abs/1912.00412) | - | - |
335 | | [SGAS: Sequential Greedy Architecture Search(Li et al. 2019) ](https://arxiv.org/abs/1912.00195)
*accepted at CVPR’20* | - | - |
336 | | [Blockwisely Supervised Neural Architecture Search with Knowledge Distillation(Li et al. 2019) ](https://arxiv.org/abs/1911.13053) | - | - |
337 | | [Towards Oracle Knowledge Distillation with Neural Architecture Search(Kang et al. 2019) ](https://arxiv.org/abs/1911.13019) | - | - |
338 | | [AutoML for Architecting Efficient and Specialized Neural Networks(Cai et al. 2019) ](https://ieeexplore.ieee.org/abstract/document/8897011)
*accepted at IEEE Micro* | - | - |
339 | | [Artificial Neural Network and Accelerator Co-design using Evolutionary Algorithms(Colangelo et al. 2019) ](https://ieeexplore.ieee.org/abstract/document/8916533)
*accepted at HPEC’19* | - | - |
340 | | [Auto-creation of Effective Neural Network Architecture by Evolutionary Algorithm and ResNet for Image Classification(Chen et al. 2019) ](https://ieeexplore.ieee.org/abstract/document/8914267)
*accepted at SMC’19* | - | - |
341 | | [Performance Prediction Based on Neural Architecture Features(Long et al. 2019) ](https://ieeexplore.ieee.org/abstract/document/8901943)
*accepted at CCHI’19* | - | - |
342 | | [Fair DARTS: Eliminating Unfair Advantages in Differentiable Architecture Search(Chu et al. 2019) ](https://arxiv.org/abs/1911.12126)
*accepted at ECCV’20* | - | - |
343 | | [EDAS: Efficient and Differentiable Architecture Search(Hong et al. 2019) ](https://arxiv.org/abs/1912.01237) | - | - |
344 | | [SGAS: Sequential Greedy Architecture Search(Li et al. 2019) ](https://arxiv.org/abs/1912.00195) | - | - |
345 | | [Ranking architectures using meta-learning(Dubatovka et al. 2019) ](https://arxiv.org/abs/1911.11481) | - | - |
346 | | [Meta-Learning of Neural Architectures for Few-Shot Learning(Elsken et al. 2019) ](https://arxiv.org/abs/1911.11090) | - | - |
347 | | [When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks(Guo et al. 2019) ](https://arxiv.org/abs/1911.10695) | - | - |
348 | | [Exploiting Operation Importance for Differentiable Neural Architecture Search(Xie et al. 2019) ](https://arxiv.org/abs/1911.10511) | - | - |
349 | | [SM-NAS: Structural-to-Modular Neural Architecture Search for Object Detection(Yao et al. 2019) ](https://arxiv.org/abs/1911.09929) | - | - |
350 | | [Multi-Objective Neural Architecture Search via Predictive Network Performance Optimization(Shi et al. 2019) ](https://arxiv.org/abs/1911.09336) | - | - |
351 | | [Data Proxy Generation for Fast and Efficient Neural Architecture Search(Park. 2019) ](https://arxiv.org/abs/1911.09322) | - | - |
352 | | [AutoShrink: A Topology-aware NAS for Discovering Efficient Neural Architecture(Zhang et al. 2019) ](https://arxiv.org/abs/1911.09251) | - | - |
353 | | [Search to Distill: Pearls are Everywhere but not the Eyes(Liu et al. 2019) ](https://arxiv.org/abs/1911.09074) | - | - |
354 | | [EfficientDet: Scalable and Efficient Object Detection(EfficientDet: Scalable and Efficient Object Detection) ](https://arxiv.org/abs/1911.09070) | - | - |
355 | | [Periodic Spectral Ergodicity: A Complexity Measure for Deep Neural Networks and Neural Architecture Search(Süzen et al. 2019) ](https://arxiv.org/abs/1911.07831) | - | - |
356 | | [IMMUNECS: Neural Committee Search by an Artificial Immune System(IMMUNECS: Neural Committee Search by an Artificial Immune System) ](https://arxiv.org/abs/1911.07729) | - | - |
357 | | [NAIS: Neural Architecture and Implementation Search and its Applications in Autonomous Driving(Hao et al. 2019) ](https://arxiv.org/abs/1911.07446) | - | - |
358 | | [Neural Recurrent Structure Search for Knowledge Graph Embedding(Zhang et al. 2019) ](https://arxiv.org/abs/1911.07132) | - | - |
359 | | [S2DNAS: Transforming Static CNN Model for Dynamic Inference via Neural Architecture Search(Yuan et al. 2019) ](https://arxiv.org/abs/1911.07033) | - | - |
360 | | [Automatic Design of CNNs via Differentiable Neural Architecture Search for PolSAR Image Classification(Dong et al. 2019) ](https://arxiv.org/abs/1911.06993) | - | - |
361 | | [Enhancing Neural Architecture Search with Speciation and Inter-Epoch Crossover(Baughmann and Wozniak. 2019) ](https://sc19.supercomputing.org/proceedings/src_poster/src_poster_pages/spostg145.html)
*accepted at Supercomputing’19* | - | - |
362 | | [RAPDARTS: Resource-Aware Progressive Differentiable Architecture Search(Green et al. 2019) ](https://arxiv.org/abs/1911.05704) | - | - |
363 | | [AutoPrune: Automatic Network Pruning by Regularizing Auxiliary Parameters(Xiao et al. 2019) ](http://papers.nips.cc/paper/9521-autoprune-automatic-network-pruning-by-regularizing-auxiliary-parameters.pdf)
*accepted at NeurIPS’19* | - | - |
364 | | [DATA: Differentiable ArchiTecture Approximation(Chang et al. 2019) ](https://papers.nips.cc/paper/8374-data-differentiable-architecture-approximation.pdf)
*accepted at NeurIPS’19* | - | - |
365 | | [Learning to reinforcement learn for Neural Architecture Search(Robles and Vanschoren. 2019) ](https://arxiv.org/pdf/1911.03769.pdf) | - | - |
366 | | [An Automated Approach for Developing a Convolutional Neural Network Using a Modified Firefly Algorithm for Image Classification(Sharaf ad Radwan. 2019) ](https://link.springer.com/chapter/10.1007/978-981-15-0306-1_5)
*accepted at accepted book chapter* | - | - |
367 | | [ENAS Oriented Layer Adaptive Data Scheduling Strategy for Resource Limited Hardware(Li et al. 2019) ](https://www.sciencedirect.com/science/article/abs/pii/S0925231219315620)
*accepted at Neurocomputing Journal* | - | - |
368 | | [Improved Differentiable Architecture Search for Language Modeling and Named Entity Recognition(Jiang et al. 2019) ](https://www.aclweb.org/anthology/D19-1367/)
*accepted at EMNLP-IJCNLP’19* | - | - |
369 | | [Device-Circuit-Architecture Co-Exploration for Computing-in-Memory Neural Accelerators(Jiang et al. 2019) ](https://arxiv.org/abs/1911.00139) | - | - |
370 | | [On Neural Architecture Search for Resource-Constrained Hardware Platforms(Lu et al. 2020) ](https://arxiv.org/abs/1911.00105)
*accepted at ICCAD’19* | - | - |
371 | | [NAT: Neural Architecture Transformer for Accurate and Compact Architectures(Guo et al. 2019) ](https://arxiv.org/abs/1910.14488) | - | - |
372 | | [Deep neural network architecture search using network morphism(Kwasigroch et al. 2019) ](https://ieeexplore.ieee.org/abstract/document/8864624)
*accepted at accepted MMAR’19* | - | - |
373 | | [Person Re-identification with Neural Architecture Search(Zhang et al. 2019) ](https://link.springer.com/chapter/10.1007/978-3-030-31654-9_46)
*accepted at accepted PRCV’19* | - | - |
374 | | [Resource Constrained Neural Network Architecture Search: Will a Submodularity Assumption Help?(Xiong et al. 2019) ](http://openaccess.thecvf.com/content_ICCV_2019/papers/Xiong_Resource_Constrained_Neural_Network_Architecture_Search_Will_a_Submodularity_Assumption_ICCV_2019_paper.pdf)
*accepted at ICCV’19* | - | - |
375 | | [Auto-FPN: Automatic Network Architecture Adaptation for Object Detection Beyond Classification(Xu et al. 2019) ](http://openaccess.thecvf.com/content_ICCV_2019/papers/Xu_Auto-FPN_Automatic_Network_Architecture_Adaptation_for_Object_Detection_Beyond_Classification_ICCV_2019_paper.pdf)
*accepted at ICCV’19* | - | - |
376 | | [BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search(White et al. 2019) ](https://arxiv.org/abs/1910.11858) | - | - |
377 | | [Stabilizing DARTS with Amended Gradient Estimation on Architectural Parameters(Bi et al. 2019) ](https://arxiv.org/abs/1910.11831) | - | - |
378 | | [An End-to-End HW/SW Co-Design Methodology to Design Efficient Deep Neural Network Systems using Virtual Models(Klaiber et al. 2019) ](https://arxiv.org/abs/1910.11632) | - | - |
379 | | [Hardware-aware one-short Neural Architecture Search in Coordinate Ascent Framework(Hardware-aware one-short Neural Architecture Search in Coordinate Ascent Framework) ](https://arxiv.org/abs/1910.11609) | - | - |
380 | | [Efficient Structured Pruning and Architecture Searching for Group Convolution(Zhao and Luk. 2019) ](http://openaccess.thecvf.com/content_ICCVW_2019/papers/NeurArch/Zhao_Efficient_Structured_Pruning_and_Architecture_Searching_for_Group_Convolution_ICCVW_2019_paper.pdf)
*accepted at ICCV’19 workshop* | - | - |
381 | | [On-Device Image Classification with Proxyless Neural Architecture Search and Quantization-Aware Fine-tuning(Cai et al. 2019) ](http://openaccess.thecvf.com/content_ICCVW_2019/papers/LPCV/Cai_On-Device_Image_Classification_with_Proxyless_Neural_Architecture_Search_and_Quantization-Aware_ICCVW_2019_paper.pdf)
*accepted at ICCV’19 workshop* | - | - |
382 | | [MSNet: Structural Wired Neural Architecture Search for Internet of Things(Cheng et al. 2019) ](http://openaccess.thecvf.com/content_ICCVW_2019/papers/NeurArch/Cheng_MSNet_Structural_Wired_Neural_Architecture_Search_for_Internet_of_Things_ICCVW_2019_paper.pdf)
*accepted at ICCV’19 workshop* | - | - |
383 | | [Efficient Decoupled Neural Architecture Search by Structure and Operation Sampling(Lee et al. 2019) ](https://arxiv.org/abs/1910.10397) | - | - |
384 | | [Using Neural Architecture Search to Optimize Neural Networks for Embedded Devices(Cassimon et al. 2019) ](https://link.springer.com/chapter/10.1007/978-3-030-33509-0_64)
*accepted at 3PGCIC’19* | - | - |
385 | | [NASIB: Neural Architecture Search withIn Budget(Singh et al. 2019) ](https://arxiv.org/abs/1910.08665) | - | - |
386 | | [State of Compact Architecture Search For Deep Neural Networks(Shafiee et al. 2019) ](https://arxiv.org/abs/1910.06466) | - | - |
387 | | [One-Shot Neural Architecture Search via Self-Evaluated Template Network(Dong and Yang. 2019) ](https://arxiv.org/abs/1910.05733) | - | - |
388 | | [Scalable Neural Architecture Search for 3D Medical Image Segmentation(Kim et al. 2019) ](https://arxiv.org/abs/1906.05956)
*accepted at MICCAI’19* | - | - |
389 | | [Neural Architecture Search for Adversarial Medical Image Segmentation(Dong et al. 2019) ](https://link.springer.com/chapter/10.1007/978-3-030-32226-7_92)
*accepted at MICCAI’19* | - | - |
390 | | [Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation(Yang et al. 2019) ](https://link.springer.com/chapter/10.1007/978-3-030-32245-8_1)
*accepted at MICCAI’19* | - | - |
391 | | [Identify Hierarchical Structures from Task-Based fMRI Data via Hybrid Spatiotemporal Neural Architecture Search Net(Zhang et al. 2019) ](https://link.springer.com/chapter/10.1007/978-3-030-32248-9_83)
*accepted at MICCAI’19* | - | - |
392 | | [Energy-aware Neural Architecture Optimization with Fast Splitting Steepest Descent(Wang et al. 2019) ](https://arxiv.org/abs/1910.03103)
*accepted at accepted EMC2 workshop’19* | - | - |
393 | | [Improving one-shot NAS by Surppressing the Posterior Fading(Li et al. 2019) ](https://arxiv.org/abs/1910.02543) | - | - |
394 | | [Splitting Steepest Descent for Growing Neural Architectures(Liu et al. 2019) ](https://arxiv.org/abs/1910.02366) | - | - |
395 | | [A Novel Automatic CNN Architecture Design Approach Based on Genetic Algorithm(Ahmed et al. 2019) ](https://link.springer.com/chapter/10.1007/978-3-030-31129-2_43)
*accepted at AISI’19* | - | - |
396 | | [RNAS: Architecture Ranking for Powerful Networks(Xu et al. 2019) ](https://arxiv.org/abs/1910.01523) | - | - |
397 | | [Towards Unifying Neural Architecture Space Exploration and Generalization(Bhardwaj and Marculescu) ](https://arxiv.org/abs/1910.00780) | - | - |
398 | | [Sub-Architecture Ensemble Pruning in Neural Architecture Search(Bia et al. 2019) ](https://arxiv.org/abs/1910.00370) | - | - |
399 | | [Towards modular and programmable architecture search(Negrinho et al. 2019) ](https://arxiv.org/abs/1909.13404)
*accepted at NeurIPS’19* | - | - |
400 | | [Automated design of error-resilient and hardware-efficient deep neural networks(Schorn et al. 2019) ](https://arxiv.org/abs/1909.13844) | - | - |
401 | | [STACNAS: Towards Stable and Consistent Optimization for Differentiable Neural Architecture Search(Guilin et al. 2019) ](https://arxiv.org/abs/1909.11926) | - | - |
402 | | [Efficient Residual Dense Block Search for Image Super-Resolution(Song et al. 2019) ](https://arxiv.org/abs/1909.11409) | - | - |
403 | | [Understanding and Improving One-shot Neural Architecture Optimization(Luo et al. 2019) ](https://arxiv.org/abs/1909.10815) | - | - |
404 | | [Scheduled Differentiable Architecture Search for Visual Recognition(Qui et al. 2019) ](https://arxiv.org/abs/1909.10236) | - | - |
405 | | [Understanding and Robustifying Differentiable Architecture Search(Zela et al. 2019) ](https://arxiv.org/abs/1909.09656)
*accepted at ICLR’20* | - | - |
406 | | [Genetic Neural Architecture Search for automatic assessment of human sperm images(Miahi et al. 2019) ](https://arxiv.org/abs/1909.09432) | - | - |
407 | | [IR-NAS: Neural Architecture Search for Image Restoration(Zhang et al. 2019) ](https://arxiv.org/abs/1909.08228) | - | - |
408 | | [Pose Neural Fabrics Search(Yang et al. 2019) ](https://arxiv.org/abs/1909.07068) | - | - |
409 | | [SegNAS3D: Network Architecture Search with Derivative-Free Global Optimization for 3D Image Segmentation(Wong and Moradi. 2019) ](https://arxiv.org/abs/1909.05962) | - | - |
410 | | [DARTS+: Improved Differentiable Architecture Search with Early Stopping(Liang et al. 2019) ](https://arxiv.org/abs/1909.06035) | - | - |
411 | | [Searching for Accurate Binary Neural Architectures(Shen et al. 2019) ](https://arxiv.org/abs/1909.07378)
*accepted at ICCV’19 Neural Architects workshop* | - | - |
412 | | [Improving Keyword Spotting and Language Identification via Neural Architecture Search at Scale(Mazzawi et al. 2019) ](https://www.isca-speech.org/archive/Interspeech_2019/pdfs/1916.pdf)
*accepted at INTERSPEECH 2019* | - | - |
413 | | [Neural Architecture Search for Class-incremental Learning(Huang et al. 2019) ](https://arxiv.org/abs/1909.06686) | - | - |
414 | | [Graph-guided Architecture Search for Real-time Semantic Segmentation(Lin et al. 2019) ](https://arxiv.org/abs/1909.06793) | - | - |
415 | | [CARS: Continuous Evolution for Efficient Neural Architecture Search(Yang et al. 2019) ](https://arxiv.org/abs/1909.04977)
*accepted at CVPR’20* | - | - |
416 | | [Bayesian Optimization of Neural Architectures for Human Activity Recognition(Osmani and Hamidi. 2019) ](https://link.springer.com/chapter/10.1007/978-3-030-13001-5_12)
*accepted at Human Activity Sensing* | - | - |
417 | | [Compute-Efficient Neural Network Architecture Optimization by a Genetic Algorithm(Litzinger et al. 2019) ](https://link.springer.com/chapter/10.1007/978-3-030-30484-3_32)
*accepted at ICANN’19* | - | - |
418 | | [Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study(Faes et al. 2019) ](https://www.sciencedirect.com/science/article/pii/S2589750019301086)
*accepted at The Lancet Digital Health* | - | - |
419 | | [A greedy constructive algorithm for the optimization of neural network architectures(Pasini et al. 2019) ](https://arxiv.org/abs/1909.03306) | - | - |
420 | | [Differentiable Mask Pruning for Neural Networks(Ramakrishnan et al. 2019) ](https://arxiv.org/abs/1909.04567) | - | - |
421 | | [Neural Architecture Search in Embedding Space(Liu. 2019) ](https://arxiv.org/abs/1909.03615) | - | - |
422 | | [Auto-GNN: Neural Architecture Search of Graph Neural Networks(Zhou et al. 2019) ](https://arxiv.org/abs/1909.03184) | - | - |
423 | | [Best Practices for Scientific Research on Neural Architecture Search(Lindauer and Hutter. 2019) ](https://arxiv.org/abs/1909.02453) | - | - |
424 | | [Efficient Neural Architecture Transformation Search in Channel-Level for Object Detection(Peng et al. 2019) ](https://arxiv.org/abs/1909.02293) | - | - |
425 | | [Training compact neural networks via auxiliary overparameterization(Liu et al. 2019) ](https://arxiv.org/abs/1909.02214) | - | - |
426 | | [Rethinking the Number of Channels for Convolutional Neural Networks(Zhu et al. 2019) ](https://arxiv.org/abs/1909.01861) | - | - |
427 | | [MANAS: Multi-Agent Neural Architecture Search(Carlucci et al. 2019) ](https://arxiv.org/abs/1909.01051) | - | - |
428 | | [Resource Optimized Neural Architecture Search for 3D Medical Image Segmentation(Bae et al. 2019) ](https://arxiv.org/abs/1909.00548)
*accepted at MICCAI’19* | - | - |
429 | | [Neural Architecture Search for Joint Optimization of Predictive Power and Biological Knowledge(Zhang et al. 2019) ](https://arxiv.org/abs/1909.00337) | - | - |
430 | | [Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research(Balaprakash et al. 2019) ](https://arxiv.org/abs/1909.00311)
*accepted at SC’19* | - | - |
431 | | [Automatic Neural Network Search Method for Open Set Recognition(Sun et al. 2019) ](https://ieeexplore.ieee.org/abstract/document/8803605)
*accepted at ICIP’19* | - | - |
432 | | [HM-NAS: Efficient Neural Architecture Search via Hierarchical Masking(Yan et al. 2019) ](https://arxiv.org/abs/1909.00122)
*accepted at ICCV’19 Neural Architects Workshop* | - | - |
433 | | [Once for All: Train One Network and Specialize it for Efficient Deployment(Cai et al. 2019) ](https://arxiv.org/abs/1908.09791) | - | - |
434 | | [Refactoring Neural Networks for Verification(Shriver et al. 2019) ](https://arxiv.org/abs/1908.08026) | - | - |
435 | | [CNASV: A Convolutional Neural Architecture Search-Train Prototype for Computer Vision Task(Zhou and Yang. 2019) ](https://link.springer.com/chapter/10.1007/978-3-030-30146-0_26)
*accepted at CollaborateCom’19* | - | - |
436 | | [Automatic Design of Deep Networks with Neural Blocks(Zhong et al. 2019) ](https://link.springer.com/article/10.1007/s12559-019-09677-5)
*accepted at Cognitive Computation* | - | - |
437 | | [Differentiable Learning-to-Group Channels via Groupable Convolutional Neural Networks(Zhang et al. 2019) ](https://arxiv.org/abs/1908.05867) | - | - |
438 | | [SCARLET-NAS: Bridging the gap Between Scalability and Fairness in Neural Architecture Search(Chu et al. 2019) ](https://arxiv.org/abs/1908.06022) | - | - |
439 | | [A Novel Encoding Scheme for Complex Neural Architecture Search(Ahmad et al. 2019) ](https://ieeexplore.ieee.org/document/8793329)
*accepted at ITC-CSCC* | - | - |
440 | | [A Graph-Based Encoding for Evolutionary Convolutional Neural Network Architecture Design(Irwin-Harris et al. 2019) ](https://ieeexplore.ieee.org/document/8790093)
*accepted at accepted CEC’19* | - | - |
441 | | [A Novel Framework for Neural Architecture Search in the Hill Climbing Domain(Verma et al. 2019) ](https://ieeexplore.ieee.org/abstract/document/8791709)
*accepted at AIKE’19* | - | - |
442 | | [Automated Neural Network Construction with Similarity Sensitive Evolutionary Algorithms(Tian et al. 2019) ](http://rvc.eng.miami.edu/Paper/2019/IRI19_EA.pdf) | - | - |
443 | | [AutoGAN: Neural Architecture Search for Generative Adversarial Networks(Gong et al. 2019) ](https://arxiv.org/abs/1908.03835)
*accepted at ICCV’19* | - | - |
444 | | [Refining the Structure of Neural Networks Using Matrix Conditioning(Yousefzadeh and O’Leary. 2019) ](https://arxiv.org/abs/1908.02400) | - | - |
445 | | [SqueezeNAS: Fast neural architecture search for faster semantic segmentation(Shaw et al. 2019) ](https://arxiv.org/abs/1908.01748) | - | - |
446 | | [MoGA: Searching Beyond MobileNetV3(Chu et al. 2019) ](https://arxiv.org/abs/1908.01314)
*accepted at ICASSP’20* | - | - |
447 | | [Evolving deep neural networks by multi-objective particle swarm optimization for image classification(Wang et al. 2019) ](https://arxiv.org/abs/1904.09035)
*accepted at GECCO’19* | - | - |
448 | | [Particle Swarm Optimisation for Evolving Deep Neural Networks for Image Classification by Evolving and Stacking Transferable Blocks(Wang et al. 2019) ](https://arxiv.org/abs/1907.12659)
*accepted at IEEE CEC’20* | - | - |
449 | | [Self-Adaptive 2D-3D Ensemble of Fully Convolutional Networks for Medical Image Segmentation(Calisto and Lai-Yuen. 2019) ](https://arxiv.org/abs/1907.11587)
*accepted at SPIE Medical Imaging’20* | - | - |
450 | | [MemNet: Memory-Efficiency Guided Neural Architecture Search with Augment-Trim learning(by Liu et al. 2019) ](https://arxiv.org/abs/1907.09569) | - | - |
451 | | [Efficient Novelty-Driven Neural Architecture Search(Zhang et al. 2019) ](https://arxiv.org/abs/1907.09109) | - | - |
452 | | [PC-DARTS: Partial Channel Connections for Memory-Efficient Differentiable Architecture Search(Xu et al. 2019) ](https://arxiv.org/abs/1907.05737) | - | - |
453 | | [Hardware/Software Co-Exploration of Neural Architectures(Jiang et al. 2019) ](https://arxiv.org/abs/1907.04650) | - | - |
454 | | [EPNAS: Efficient Progressive Neural Architecture Search(Zhou et al. 2019) ](https://arxiv.org/abs/1907.04648) | - | - |
455 | | [Video Action Recognition via Neural Architecture Searching(Peng et al. 2019) ](https://arxiv.org/abs/1907.04632) | - | - |
456 | | [Hardware/Software Co-Exploration of Neural Architectures(Jiang et al. 2019) ](https://arxiv.org/abs/1907.04650)
*accepted at ASP-DAC’20* | - | - |
457 | | [When Neural Architecture Search Meets Hardware Implementation: from Hardware Awareness to Co-Design(Zhang et al. 2019) ](https://ieeexplore.ieee.org/document/8839421)
*accepted at ISVLSI’19* | - | - |
458 | | [Reinforcement Learning for Neural Architecture Search: A Review(Jaafra et al. 2019 accepted at Image and Vision Computing) ](https://www.sciencedirect.com/science/article/pii/S026288561930088) | - | - |
459 | | [Architecture Search for Image Inpainting(Li and King. 2019. accepted at International Symposium on Neural Networks) ](https://link.springer.com/chapter/10.1007/978-3-030-22796-8_12) | - | - |
460 | | [Neural Network Architecture Search with Differentiable Cartesian Genetic Programming for Regression(Märtens and Izzo. 2019) ](https://arxiv.org/abs/1907.01939) | - | - |
461 | | [FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural Architecture Search(Chu et al. 2019) ](https://arxiv.org/abs/1907.01845) | - | - |
462 | | [HyperNOMAD: Hyperparameter optimization of deep neural networks using mesh adaptive direct search(Lakhmiri et al. 2019) ](https://arxiv.org/pdf/1907.01698.pdf) | - | - |
463 | | [Evolving Robust Neural Architectures to Defend from Adversarial Attacks(Vargas and Kotyan. 2019) ](https://arxiv.org/abs/1906.11667) | - | - |
464 | | [Surrogate-Assisted Evolutionary Deep Learning Using an End-to-End Random Forest-based Performance Predictor(Sun et al. 2019) ](https://ieeexplore.ieee.org/document/8744404)
*accepted at accepted by IEEE Transactions on Evolutionary Computation* | - | - |
465 | | [Adaptive Genomic Evolution of Neural Network Topologies(Behjat et al. 2019) ](https://arxiv.org/abs/1903.07107)
*accepted at accepted and presented in ICRA 2019* | - | - |
466 | | [Densely Connected Search Space for More Flexible Neural Architecture Search(Fang et al. 2019) ](https://arxiv.org/abs/1906.09607) | - | - |
467 | | [Posterior-Guided Neural Architecture Search(Zhou et al. 2020) ](https://arxiv.org/abs/1906.09557)
*accepted at AAAI* | - | - |
468 | | [SwiftNet: Using Graph Propagation as Meta-knowledge to Search Highly Representative Neural Architectures(Cheng et al. 2019) ](https://arxiv.org/abs/1906.08305) | - | - |
469 | | [Transfer NAS: Knowledge Transfer between Search Spaces with Transformer Agents(Borsos et al. 2019) ](https://arxiv.org/abs/1906.08102) | - | - |
470 | | [XNAS: Neural Architecture Search with Expert Advice(Nayman et al. 2019) ](https://arxiv.org/abs/1906.08031)
*accepted at NeurIPS’19* | - | - |
471 | | [A Study of the Learning Progress in Neural Architecture Search Techniques(Singh et al. 2019) ](https://arxiv.org/abs/1906.07590) | - | - |
472 | | [Hardware aware Neural Network Architectures(Srinivas et al. 2019) ](https://arxiv.org/abs/1906.07214) | - | - |
473 | | [Sample-Efficient Neural Architecture Search by Learning Action Space(Wang et al. 2019) ](https://arxiv.org/abs/1906.06832) | - | - |
474 | | [SwiftNet: Using Graph Propagation as Meta-knowledge to Search Highly Representative Neural Architectures(Cheng et al. 2019) ](https://arxiv.org/abs/1906.08305) | - | - |
475 | | [Automatic Modulation Recognition Using Neural Architecture Search(Wei et al. 2019) ](https://ieeexplore.ieee.org/abstract/document/8735458)
*accepted at accepted High Performance Big Data and Intelligent Systems* | - | - |
476 | | [Continual and Multi-Task Architecture Search(Pasunuru and Bansal. 2019) ](https://arxiv.org/abs/1906.05226) | - | - |
477 | | [AutoGrow: Automatic Layer Growing in Deep Convolutional Networks(Wen et al. 2019) ](https://arxiv.org/abs/1906.02909) | - | - |
478 | | [One-Short Neural Architecture Search via Compressing Sensing(Cho et al. 2019) ](https://arxiv.org/abs/1906.02869) | - | - |
479 | | [V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation(Zhu et al. 2019) ](https://arxiv.org/abs/1906.02817) | - | - |
480 | | [StyleNAS: An Empirical Study of Neural Architecture Search to Uncover Surprisingly Fast End-to-End Universal Style Transfer Networks(An et al. 2019) ](https://arxiv.org/abs/1906.02470) | - | - |
481 | | [Efficient Forward Architecture Search(Hu et al. 2019) ](https://arxiv.org/abs/1905.13360)
*accepted at NeurIPS’19* | - | - |
482 | | [Differentiable Neural Architecture Search via Proximal Iterations(Yao et al. 2019) ](https://arxiv.org/abs/1905.13577) | - | - |
483 | | [Dynamic Distribution Pruning for Efficient Network Architecture Search(Zheng et al. 2019) ](https://arxiv.org/abs/1905.13543) | - | - |
484 | | [Particle swarm optimization of deep neural networks architectures for image classification(Fernandes Junior and Yen. 2019. accepted at Swarm and Evolutionary Computation) ](https://www.sciencedirect.com/science/article/abs/pii/S2210650218309246) | - | - |
485 | | [On Network Design Spaces for Visual Recognition(Radosavovic et al. 2019) ](https://arxiv.org/abs/1905.13214)
*accepted at ICCV’20* | - | - |
486 | | [AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures(Ryoo et al. 2019) ](https://arxiv.org/abs/1905.13209) | - | - |
487 | | [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks(Tan and Le) ](http://proceedings.mlr.press/v97/tan19a/tan19a.pdf)
*accepted at ICML’19. 2019* | - | - |
488 | | [Structure Learning for Neural Module Networks(Pahuja et al. 2019) ](https://arxiv.org/abs/1905.11532) | - | - |
489 | | [SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers(Fedorov et al. 2019) ](https://arxiv.org/abs/1905.12107)
*accepted at NeurIPS’19* | - | - |
490 | | [Network Pruning via Transformable Architecture Search(Dong and Yang. 2019) ](https://arxiv.org/abs/1905.09717)
*accepted at NeurIPS’19* | - | - |
491 | | [DEEP-BO for Hyperparameter Optimization of Deep Networks(Cho et al. 2019) ](https://arxiv.org/abs/1905.09680) | - | - |
492 | | [Constrained Design of Deep Iris Networks(Nguyen et al. 2019) ](https://arxiv.org/abs/1905.09481) | - | - |
493 | | [Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search(Akimoto et al. 2019) ](https://arxiv.org/abs/1905.08537)
*accepted at ICML’19* | - | - |
494 | | [Multinomial Distribution Learning for Effective Neural Architecture Search(Zheng et al. 2019) ](https://arxiv.org/abs/1905.07529) | - | - |
495 | | [EENA: Efficient Evolution of Neural Architecture(Zhu et al. 2019) ](https://arxiv.org/abs/1905.07320)
*accepted at ICCV’19 Neural Architects Workshop* | - | - |
496 | | [DeepSwarm: Optimising Convolutional Neural Networks using Swarm Intelligence(Byla and Pang. 2019) ](https://arxiv.org/abs/1905.07350) | - | - |
497 | | [AutoDispNet: Improving Disparity Estimation with AutoML(Saikia et al. 2019) ](https://arxiv.org/abs/1905.07443) | - | - |
498 | | [Online Hyper-parameter Learning for Auto-Augmentation Strategy(Lin et al. 2019) ](https://arxiv.org/abs/1905.07373) | - | - |
499 | | [Regularized Evolutionary Algorithm for Dynamic Neural Topology Search(Saltori et al. 2019) ](https://arxiv.org/abs/1905.06252) | - | - |
500 | | [Deep Neural Architecture Search with Deep Graph Bayesian Optimization(Ma et al. 2019) ](https://arxiv.org/abs/1905.06159) | - | - |
501 | | [Automatic Model Selection for Neural Networks(Laredo et al. 2019) ](https://arxiv.org/abs/1905.06010) | - | - |
502 | | [Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization(Klein and Hutter. 2019) ](https://arxiv.org/abs/1905.04970) | - | - |
503 | | [BayesNAS: A Bayesian Approach for Neural Architecture Search(Zhou et al. 2019) ](https://arxiv.org/abs/1905.04919)
*accepted at ICML’19* | - | - |
504 | | [Single-Path NAS: Device-Aware Efficient ConvNet Design(Stamoulis et al. 2019) ](https://arxiv.org/abs/1905.04159) | - | - |
505 | | [Automatic Design of Artificial Neural Networks for Gamma-Ray Detection(Assuncao et al. 2019) ](https://arxiv.org/abs/1905.03532) | - | - |
506 | | [Neural Architecture Refinement: A Practical Way for Avoiding Overfitting in NAS(Jiang et al. 2019) ](https://arxiv.org/abs/1905.02341) | - | - |
507 | | [Fast and Reliable Architecture Selection for Convolutional Neural Networks(Hahn et al. 2019) ](https://arxiv.org/abs/1905.01924) | - | - |
508 | | [Differentiable Architecture Search with Ensemble Gumbel-Softmax(Chang et al. 2019) ](https://arxiv.org/abs/1905.01786) | - | - |
509 | | [Searching for A Robust Neural Architecture in Four GPU Hours(Dong and Yang 2019) ](https://xuanyidong.com/publication/cvpr-2019-gradient-based-diff-sampler/)
*accepted at CVPR’19* | - | - |
510 | | [Evolving unsupervised deep neural networks for learning meaningful representations(Sun et al. 2019, accepted by IEEE Transactions on Evolutionary Computation) ](https://arxiv.org/abs/1712.05043) | - | - |
511 | | [Evolving Deep Convolutional Neural Networks for Image Classification(Sun et al. 2019, accepted by IEEE Transactions on Evolutionary Computation) ](https://arxiv.org/abs/1710.10741) | - | - |
512 | | [AdaResU-Net: Multiobjective Adaptive Convolutional Neural Network for Medical Image Segmentation(Baldeon-Calisto and Lai-Yuen. 2019.) ](https://www.sciencedirect.com/science/article/pii/S0925231219304679)
*accepted at Neurocomputing* | - | - |
513 | | [Automatic Design of Convolutional Neural Network for Hyperspectral Image Classification(Chen et al. 2019) ](https://ieeexplore.ieee.org/abstract/document/8703410)
*accepted at IEEE Transactions on Geoscience and Remote Sensing* | - | - |
514 | | [Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation(Chen et al. 2019) ](https://arxiv.org/abs/1904.12760) | - | - |
515 | | [Design Automation for Efficient Deep Learning Computing(Han et al. 2019) ](https://arxiv.org/abs/1904.10616) | - | - |
516 | | [CascadeML: An Automatic Neural Network Architecture Evolution and Training Algorithm for Multi-label Classification(Pakrashi and Namee 2019) ](https://arxiv.org/abs/1904.10551) | - | - |
517 | | [GraphNAS: Graph Neural Architecture Search with Reinforcement Learning(Gao et al. 2019) ](https://arxiv.org/abs/1904.09981) | - | - |
518 | | [Neural Architecture Search for Deep Face Recognition(Zhu. 2019) ](https://arxiv.org/abs/1904.09523) | - | - |
519 | | [Efficient Neural Architecture Search on Low-Dimensional Data for OCT Image Segmentation(Gessert and Schlaefer. 2019) ](https://openreview.net/forum?id=Syg3FDjntN) | - | - |
520 | | [NAS-Unet: Neural Architecture Search for Medical Image Segmentation(Weng et al. 2019) ](https://ieeexplore.ieee.org/document/8681706)
*accepted at IEEE Access* | - | - |
521 | | [Fast DENSER: Efficient Deep NeuroEvolution(Assunção et al. 2019) ](https://link.springer.com/chapter/10.1007/978-3-030-16670-0_13)
*accepted at ECGP’19* | - | - |
522 | | [NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection(Ghaisi et al. 2019) ](https://arxiv.org/abs/1904.07392)
*accepted at CVPR’19* | - | - |
523 | | [Automated Search for Configurations of Deep Neural Network Architectures(Ghamizi et al. 2019) ](https://arxiv.org/abs/1904.04612)
*accepted at SPLC’19* | - | - |
524 | | [WeNet: Weighted Networks for Recurrent Network Architecture Search(Huang and Xiang. 2019) ](https://arxiv.org/abs/1904.03819) | - | - |
525 | | [Resource Constrained Neural Network Architecture Search(Xiong et al. 2019) ](https://arxiv.org/abs/1904.03786) | - | - |
526 | | [Size/Accuracy Trade-Off in Convolutional Neural Networks: An Evolutionary Approach(Cetto et al. 2019) ](https://link.springer.com/chapter/10.1007/978-3-030-16841-4_3)
*accepted at INNSBDDL* | - | - |
527 | | [ASAP: Architecture Search, Anneal and Prune(Noy et al. 2019) ](https://arxiv.org/abs/1904.04123) | - | - |
528 | | [Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours(Stamoulis et al. 2019) ](https://arxiv.org/abs/1904.02877) | - | - |
529 | | [Architecture Search of Dynamic Cells for Semantic Video Segmentation(Nekrasov et al. 2019) ](https://arxiv.org/abs/1904.02371) | - | - |
530 | | [Template-Based Automatic Search of Compact Semantic Segmentation Architectures(Nekrasov et al. 2019) ](https://arxiv.org/abs/1904.02365) | - | - |
531 | | [Exploring Randomly Wired Neural Networks for Image Recognition(Xie et al. 2019) ](https://arxiv.org/abs/1904.01569) | - | - |
532 | | [Understanding Neural Architecture Search Techniques(Adam and Lorraine 2019) ](https://arxiv.org/abs/1904.00438) | - | - |
533 | | [Automatic Convolutional Neural Architecture Search for Image Classification Under Different Scenes(Weng et al. 2019) ](https://ieeexplore.ieee.org/document/8676019)
*accepted at accepted for IEEE Access* | - | - |
534 | | [Single Path One-Shot Neural Architecture Search with Uniform Sampling(Guo et al. 2019) ](https://arxiv.org/abs/1904.00420) | - | - |
535 | | [Network Slimming by Slimmable Networks: Towards One-Shot Architecture Search for Channel Numbers(Yu and Huang 2019) ](https://arxiv.org/abs/1903.11728) | - | - |
536 | | [sharpDARTS: Faster and More Accurate Differentiable Architecture Search(Hundt et al. 2019) ](https://arxiv.org/abs/1903.09900) | - | - |
537 | | [DetNAS: Neural Architecture Search on Object Detection(Chen et al. 2019) ](https://arxiv.org/abs/1903.10979)
*accepted at NeurIPS’19* | - | - |
538 | | [Evolution of Deep Convolutional Neural Networks Using Cartesian Genetic Programming(Suganuma et al. 2019) ](https://www.mitpressjournals.org/doi/abs/10.1162/evco_a_00253)
*accepted at Evolutionary Computation* | - | - |
539 | | [Deep Evolutionary Networks with Expedited Genetic Algorithm for Medical Image Denoising(Liu et al. 2019) ](https://www.sciencedirect.com/science/article/abs/pii/S1361841518307734)
*accepted at Medical Image Analysis* | - | - |
540 | | [Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly(Kandasamy et al. 2019) ](https://arxiv.org/abs/1903.06694) | - | - |
541 | | [AttoNets: Compact and Efficient Deep Neural Networks for the Edge via Human-Machine Collaborative Design(Wong et al. 2019) ](https://arxiv.org/abs/1903.07209) | - | - |
542 | | [Improving Neural Architecture Search Image Classifiers via Ensemble Learning(Macko et al. 2019) ](https://arxiv.org/abs/1903.06236) | - | - |
543 | | [Software-Defined Design Space Exploration for an Efficient AI Accelerator Architecture(Yu et al. 2019) ](https://arxiv.org/abs/1903.07676) | - | - |
544 | | [MFAS: Multimodal Fusion Architecture Search(Pérez-Rúa et al. 2019) ](https://hal.archives-ouvertes.fr/hal-02068293/document)
*accepted at CVPR’19* | - | - |
545 | | [A Hybrid GA-PSO Method for Evolving Architecture and Short Connections of Deep Convolutional Neural Networks(Wang et al. 2019) ](https://arxiv.org/abs/1903.03893)
*accepted at PRICAI’19* | - | - |
546 | | [Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search(Li et al. 2019) ](https://arxiv.org/abs/1903.03777) | - | - |
547 | | [Inductive Transfer for Neural Architecture Optimization(Wistuba and Pedapati 2019) ](https://arxiv.org/abs/1903.03536) | - | - |
548 | | [Evolutionary Cell Aided Design for Neural Network(Colangelo et al. 2019) ](https://arxiv.org/abs/1903.02130) | - | - |
549 | | [Automated Architecture-Modeling for Convolutional Neural Networks(Duong 2019) ](https://btw.informatik.uni-rostock.de/download/workshopband/D1-1.pdf) | - | - |
550 | | [Learning Implicitly Recurrent CNNs Through Parameter Sharing(Savarese and Maire) ](https://arxiv.org/abs/1902.09701)
*accepted at ICLR’19* | - | - |
551 | | [Evaluating the Search Phase of Neural Architecture Searc(Sciuto et al. 2019) ](https://arxiv.org/abs/1902.08142) | - | - |
552 | | [Random Search and Reproducibility for Neural Architecture Search(Li and Talwalkar 2019) ](https://arxiv.org/abs/1902.07638) | - | - |
553 | | [Evolutionary Neural AutoML for Deep Learning(Liang et al. 2019) ](https://arxiv.org/abs/1902.06827) | - | - |
554 | | [Fast Task-Aware Architecture Inference(Kokiopoulou et al. 2019) ](https://arxiv.org/abs/1902.05781) | - | - |
555 | | [Probabilistic Neural Architecture Search(Casale et al. 2019) ](https://arxiv.org/abs/1902.05116) | - | - |
556 | | [Investigating Recurrent Neural Network Memory Structures using Neuro-Evolution(Ororbia et al. 2019) ](https://arxiv.org/abs/1902.02390) | - | - |
557 | | [Accuracy vs. Efficiency: Achieving Both through FPGA-Implementation Aware Neural Architecture Search(Jiang et al. 2019) ](https://arxiv.org/abs/1901.11211)
*accepted at DAC’19* | - | - |
558 | | [The Evolved Transformer(So et al. 2019) ](https://arxiv.org/abs/1901.11117) | - | - |
559 | | [Designing neural networks through neuroevolution(Stanley et al. 2019) ](https://www.nature.com/articles/s42256-018-0006-z)
*accepted at Nature Machine Intelligence* | - | - |
560 | | [NeuNetS: An Automated Synthesis Engine for Neural Network Design(Sood et al. 2019) ](https://arxiv.org/abs/1901.06261) | - | - |
561 | | [Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search(Chu et al. 2019) ](https://arxiv.org/abs/1901.07261)
*accepted at ICPR’20* | - | - |
562 | | [EAT-NAS: Elastic Architecture Transfer for Accelerating Large-scale Neural Architecture Search(Fang et al. 2019) ](https://arxiv.org/abs/1901.05884) | - | - |
563 | | [Bayesian Learning of Neural Network Architectures(Dikov et al. 2019) ](https://arxiv.org/abs/1901.04436) | - | - |
564 | | [Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation(Liu et al. 2019) ](https://arxiv.org/abs/1901.02985)
*accepted at CVPR’19* | - | - |
565 | | [The Art of Getting Deep Neural Networks in Shape(Mammadli et al. 2019) ](https://dl.acm.org/citation.cfm?id=3291053)
*accepted at TACO Journal* | - | - |
566 | | [Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search(Chu et al. 2019) ](https://arxiv.org/abs/1901.01074) | - | - |
567 |
568 | ### 2018
569 |
570 | - [back to top](#2020)
571 |
572 | | Title | Tags | Code |
573 | |:--------|:--------:|:--------:|
574 | | [A particle swarm optimization-based flexible convolutional auto-encoder for image classification(Sun et al. 2018, published by IEEE Transactions on Neural Networks and Learning Systems) ](https://arxiv.org/abs/1712.05042) | - | - |
575 | | [SNAS: Stochastic Neural Architecture Search(Xie et al. 2018) ](https://arxiv.org/abs/1812.09926)
*accepted at ICLR’19* | - | - |
576 | | [Graph Hypernetworks for Neural Architecture Search(Zhang et al. 2018) ](https://arxiv.org/abs/1810.05749)
*accepted at Accepted at ICLR’19* | - | - |
577 | | [Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution(Elsken et al. 2018) ](https://arxiv.org/abs/1804.09081)
*accepted at ICLR’19* | - | - |
578 | | [Macro Neural Architecture Search Revisited(Hu et al. 2018) ](http://metalearning.ml/2018/papers/metalearn2018_paper16.pdf)
*accepted at Meta-Learn NeurIPS workshop’18* | - | - |
579 | | [AMLA: an AutoML frAmework for Neural Network Design(Kamath et al. 2018) ](http://pkamath.com/publications/papers/amla_automl18.pdf)
*accepted at at ICML AutoML workshop* | - | - |
580 | | [ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation(Dai et al. 2018) ](https://arxiv.org/abs/1812.08934) | - | - |
581 | | [Neural Architecture Search Over a Graph Search Space(de Laroussilhe et al. 2018) ](https://arxiv.org/abs/1812.10666) | - | - |
582 | | [A Review of Meta-Reinforcement Learning for Deep Neural Networks Architecture Search(Jaafra et al. 2018) ](https://arxiv.org/abs/1812.07995) | - | - |
583 | | [Evolutionary Neural Architecture Search for Image Restoration(van Wyk and Bosman 2018) ](https://arxiv.org/abs/1812.05866) | - | - |
584 | | [IRLAS: Inverse Reinforcement Learning for Architecture Search(Guo et al. 2018) ](https://arxiv.org/abs/1812.05285)
*accepted at CVPR’19* | - | - |
585 | | [FBNet: Hardware-Aware Efficient ConvNet Designvia Differentiable Neural Architecture Search(Wu et al. 2018) ](https://arxiv.org/abs/1812.03443)
*accepted at CVPR’19* | - | - |
586 | | [ShuffleNASNets: Efficient CNN models throughmodified Efficient Neural Architecture Search(Laube et al. 2018) ](https://arxiv.org/abs/1812.02975) | - | - |
587 | | [ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware(Cai et al. 2018) ](https://arxiv.org/abs/1812.00332)
*accepted at ICLR’19* | - | - |
588 | | [Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search(Wu et al. 2018) ](https://arxiv.org/abs/1812.00090) | - | - |
589 | | [Evolving Deep Convolutional Neural Networks by Variable-length Particle Swarm Optimization for Image Classification(Wang et al. 2018) ](https://arxiv.org/abs/1803.06492)
*accepted at CEC’18* | - | - |
590 | | [A Hybrid Differential Evolution Approach to Designing Deep Convolutional Neural Networks for Image Classification(Wang et al. 2018) ](https://arxiv.org/abs/1808.06661)
*accepted at accepted AI’18* | - | - |
591 | | [TEA-DNN: the Quest for Time-Energy-Accuracy Co-optimized Deep Neural Networks(Cai et al. 2018) ](https://arxiv.org/abs/1811.12065) | - | - |
592 | | [Evolving Space-Time Neural Architectures for Videos(Piergiovanni et al. 2018) ](https://arxiv.org/abs/1811.10636)
*accepted at ICCV’19* | - | - |
593 | | [InstaNAS: Instance-aware Neural Architecture Search(Cheng et al. 2018) ](https://arxiv.org/abs/1811.10201) | - | - |
594 | | [Evolutionary-Neural Hybrid Agents for Architecture Search(Maziarz et al. 2018) ](https://arxiv.org/abs/1811.09828)
*accepted at ICML’19 workshop on AutoML* | - | - |
595 | | [Joint Neural Architecture Search and Quantization(Chen et al. 2018) ](https://arxiv.org/abs/1811.09426) | - | - |
596 | | [Transfer Learning with Neural AutoML(Wong et al. 2018) ](http://papers.nips.cc/paper/8056-transfer-learning-with-neural-automl.pdf)
*accepted at NeurIPS’18* | - | - |
597 | | [Evolving Image Classification Architectures with Enhanced Particle Swarm Optimisation(Fielding and Zhang 2018) ](https://ieeexplore.ieee.org/document/8533601) | - | - |
598 | | [Deep Active Learning with a Neural Architecture Search(Geifman and El-Yaniv 2018) ](https://arxiv.org/abs/1811.07579)
*accepted at NeurIPS’19* | - | - |
599 | | [Stochastic Adaptive Neural Architecture Search for Keyword Spotting(Véniat et al. 2018) ](https://arxiv.org/abs/1811.06753) | - | - |
600 | | [NSGA-NET: A Multi-Objective Genetic Algorithm for Neural Architecture Search(Lu et al. 2018) ](https://arxiv.org/abs/1810.03522) | - | - |
601 | | [You only search once: Single Shot Neural Architecture Search via Direct Sparse Optimization(Zhang et al. 2018) ](https://arxiv.org/abs/1811.01567) | - | - |
602 | | [Automatically Evolving CNN Architectures Based on Blocks(Sun et al. 2018) ](https://arxiv.org/abs/1810.11875)
*accepted at accepted by IEEE Transactions on Neural Networks and Learning Systems* | - | - |
603 | | [The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints(Hundt et al. 2018) ](https://arxiv.org/abs/1810.11714)
*accepted at IROS’19* | - | - |
604 | | [Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells(Nekrasov et al. 2018) ](https://arxiv.org/abs/1810.10804)
*accepted at CVPR’19* | - | - |
605 | | [Automatic Configuration of Deep Neural Networks with Parallel Efficient Global Optimization(van Stein et al. 2018) ](https://arxiv.org/abs/1810.05526) | - | - |
606 | | [Gradient Based Evolution to Optimize the Structure of Convolutional Neural Networks(Mitschke et al. 2018) ](https://ieeexplore.ieee.org/document/8451394) | - | - |
607 | | [Searching Toward Pareto-Optimal Device-Aware Neural Architectures(Cheng et al. 2018) ](https://arxiv.org/abs/1808.09830) | - | - |
608 | | [Neural Architecture Optimization(Luo et al. 2018) ](https://arxiv.org/abs/1808.07233)
*accepted at NeurIPS’18* | - | - |
609 | | [Exploring Shared Structures and Hierarchies for Multiple NLP Tasks(Chen et al. 2018) ](https://arxiv.org/abs/1808.07658) | - | - |
610 | | [Neural Architecture Search: A Survey(Elsken et al. 2018) ](https://arxiv.org/abs/1808.05377) | - | - |
611 | | [BlockQNN: Efficient Block-wise Neural Network Architecture Generation(Zhong et al. 2018) ](https://arxiv.org/abs/1808.05584) | - | - |
612 | | [Automatically Designing CNN Architectures Using Genetic Algorithm for Image Classification(Sunet al. 2018) ](https://arxiv.org/abs/1808.03818) | - | - |
613 | | [Reinforced Evolutionary Neural Architecture Search(Chen et al. 2018) ](https://arxiv.org/abs/1808.00193)
*accepted at CVPR’19* | - | - |
614 | | [Teacher Guided Architecture Search(Bashivan et al. 2018) ](https://arxiv.org/abs/1808.01405) | - | - |
615 | | [Efficient Progressive Neural Architecture Search(Perez-Rua et al. 2018) ](https://arxiv.org/abs/1808.00391) | - | - |
616 | | [MnasNet: Platform-Aware Neural Architecture Search for Mobile(Tan et al. 2018) ](https://arxiv.org/abs/1807.11626)
*accepted at CVPR’19* | - | - |
617 | | [Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search(Zela et al. 2018) ](https://arxiv.org/abs/1807.06906) | - | - |
618 | | [Automatically Designing CNN Architectures for Medical Image Segmentation(Mortazi and Bagci 2018) ](https://arxiv.org/abs/1807.07663) | - | - |
619 | | [MONAS: Multi-Objective Neural Architecture Search using Reinforcement Learning(Hsu et al. 2018) ](https://arxiv.org/abs/1806.10332) | - | - |
620 | | [Path-Level Network Transformation for Efficient Architecture Search(Cai et al. 2018) ](https://arxiv.org/abs/1806.02639)
*accepted at ICML’18* | - | - |
621 | | [Lamarckian Evolution of Convolutional Neural Networks(Prellberg and Kramer, 2018) ](https://arxiv.org/abs/1806.08099) | - | - |
622 | | [Deep Learning Architecture Search by Neuro-Cell-based Evolution with Function-Preserving Mutations(Wistuba, 2018) ](http://www.ecmlpkdd2018.org/wp-content/uploads/2018/09/108.pdf) | - | - |
623 | | [DARTS: Differentiable Architecture Search(Liu et al. 2018) ](https://arxiv.org/abs/1806.09055)
*accepted at ICLR’19* | - | - |
624 | | [Constructing Deep Neural Networks by Bayesian Network Structure Learning(Rohekar et al. 2018) ](https://arxiv.org/abs/1806.09141) | - | - |
625 | | [Resource-Efficient Neural Architect(Zhou et al. 2018) ](https://arxiv.org/abs/1806.07912) | - | - |
626 | | [Efficient Neural Architecture Search with Network Morphism(Jin et al. 2018) ](https://arxiv.org/abs/1806.10282) | - | - |
627 | | [TAPAS: Train-less Accuracy Predictor for Architecture Search(Istrate et al. 2018) ](https://arxiv.org/abs/1806.00250) | - | - |
628 | | [Neural Architecture Search using Deep Neural Networks and Monte Carlo Tree Search(Wang et al 2018) ](https://arxiv.org/abs/1805.07440)
*accepted at AAAI’20* | - | - |
629 | | [Multi-objective Architecture Search for CNNs(Elsken et al. 2018) ](https://arxiv.org/abs/1804.09081) | - | - |
630 | | [GNAS: A Greedy Neural Architecture Search Method for Multi-Attribute Learning(Huang et al 2018) ](https://arxiv.org/abs/1804.06964) | - | - |
631 | | [Evolutionary Architecture Search For Deep Multitask Networks(Liang et al. 2018) ](https://arxiv.org/abs/1803.03745) | - | - |
632 | | [From Nodes to Networks: Evolving Recurrent Neural Networks(Rawal et al. 2018) ](https://arxiv.org/abs/1803.04439) | - | - |
633 | | [Neural Architecture Construction using EnvelopeNets(Kamath et al. 2018) ](https://arxiv.org/abs/1803.06744) | - | - |
634 | | [Transfer Automatic Machine Learning(Wong et al. 2018) ](https://arxiv.org/abs/1803.02780) | - | - |
635 | | [Neural Architecture Search with Bayesian Optimisation and Optimal Transport(Kandasamy et al. 2018) ](https://arxiv.org/abs/1802.07191) | - | - |
636 | | [Efficient Neural Architecture Search via Parameter Sharing(Pham et al. 2018) ](https://arxiv.org/abs/1802.03268)
*accepted at ICML’18* | - | - |
637 | | [Regularized Evolution for Image Classifier Architecture Search(Real et al. 2018) ](https://arxiv.org/abs/1802.01548) | - | - |
638 | | [Effective Building Block Design for Deep Convolutional Neural Networks using Search(Dutta et al. 2018) ](https://arxiv.org/abs/1801.08577) | - | - |
639 | | [Combination of Hyperband and Bayesian Optimization for Hyperparameter Optimization in Deep Learning(Wang et al. 2018) ](https://arxiv.org/abs/1801.01596) | - | - |
640 | | [Memetic Evolution of Deep Neural Networks(Lorenzo and Nalepa 2018) ](https://dl.acm.org/citation.cfm?id=3205631) | - | - |
641 | | [Understanding and Simplifying One-Shot Architecture Search(Bender et al. 2018) ](http://proceedings.mlr.press/v80/bender18a/bender18a.pdf)
*accepted at ICML’18* | - | - |
642 | | [Differentiable Neural Network Architecture Search(Shin et al. 2018) ](https://openreview.net/pdf?id=BJ-MRKkwG)
*accepted at ICLR’18 workshop* | - | - |
643 | | [PPP-Net: Platform-aware progressive search for pareto-optimal neural architectures(Dong et al. 2018) ](https://openreview.net/pdf?id=B1NT3TAIM)
*accepted at ICLR’18 workshop* | - | - |
644 | | [Speeding up the Hyperparameter Optimization of Deep Convolutional Neural Networks(Hinz et al. 2018) ](https://www.worldscientific.com/doi/abs/10.1142/S1469026818500086) | - | - |
645 | | [Gitgraph – From Computational Subgraphs to Smaller Architecture search spaces(Bennani-Smires et al. 2018) ](https://openreview.net/pdf?id=rkiO1_1Pz) | - | - |
646 |
647 | ### 2017
648 |
649 | - [back to top](#2020)
650 |
651 | | Title | Tags | Code |
652 | |:--------|:--------:|:--------:|
653 | | [N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning(Ashok et al. 2017) ](https://arxiv.org/abs/1709.06030)
*accepted at ICLR’18* | - | - |
654 | | [Genetic CNN(Xie and Yuille, 2017) ](https://arxiv.org/abs/1703.01513)
*accepted at ICCV’17* | - | - |
655 | | [MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks(Gordon et al. 2017) ](https://arxiv.org/abs/1711.06798) | - | - |
656 | | [MaskConnect: Connectivity Learning by Gradient Descent(Ahmed and Torresani. 2017) ](https://arxiv.org/abs/1709.09582)
*accepted at ECCV’18* | - | - |
657 | | [A Flexible Approach to Automated RNN Architecture Generation(Schrimpf et al. 2017) ](https://arxiv.org/abs/1712.07316) | - | - |
658 | | [DeepArchitect: Automatically Designing and Training Deep Architectures(Negrinho and Gordon 2017) ](https://arxiv.org/abs/1704.08792) | - | - |
659 | | [A Genetic Programming Approach to Designing Convolutional Neural Network Architectures(Suganuma et al. 2017) ](https://arxiv.org/abs/1704.00764)
*accepted at GECCO’17* | - | - |
660 | | [Practical Block-wise Neural Network Architecture Generation(Zhong et al. 2017) ](https://arxiv.org/abs/1708.05552)
*accepted at CVPR’18* | - | - |
661 | | [Accelerating Neural Architecture Search using Performance Prediction(Baker et al. 2017) ](https://arxiv.org/abs/1705.10823)
*accepted at NeurIPS worshop on Meta-Learning 2017* | - | - |
662 | | [Large-Scale Evolution of Image Classifiers(Real et al. 2017) ](https://arxiv.org/abs/1703.01041)
*accepted at ICML’17* | - | - |
663 | | [Hierarchical Representations for Efficient Architecture Search(Liu et al. 2017) ](https://arxiv.org/abs/1711.00436)
*accepted at ICLR’18* | - | - |
664 | | [Neural Optimizer Search with Reinforcement Learning(Bello et al. 2017) ](https://arxiv.org/abs/1709.07417) | - | - |
665 | | [Progressive Neural Architecture Search(Liu et al. 2017) ](https://arxiv.org/abs/1712.00559)
*accepted at ECCV’18* | - | - |
666 | | [Learning Transferable Architectures for Scalable Image Recognition(Zoph et al. 2017) ](https://arxiv.org/abs/1707.07012)
*accepted at CVPR’18* | - | - |
667 | | [Simple And Efficient Architecture Search for Convolutional Neural Networks(Elsken et al. 2017) ](https://arxiv.org/abs/1711.04528)
*accepted at NeurIPS workshop on Meta-Learning’17* | - | - |
668 | | [Bayesian Optimization Combined with Incremental Evaluation for Neural Network Architecture Optimization(Wistuba, 2017) ](https://www.semanticscholar.org/paper/Bayesian-Optimization-Combined-with-Successive-for-Wistuba/ddb182533c91f0941f088e1e298c52a111253554) | - | - |
669 | | [Finding Competitive Network Architectures Within a Day Using UCT(Wistuba 2017) ](https://arxiv.org/abs/1712.07420) | - | - |
670 | | [Hyperparameter Optimization: A Spectral Approach(Hazan et al. 2017) ](https://arxiv.org/abs/1706.00764) | - | - |
671 | | [SMASH: One-Shot Model Architecture Search through HyperNetworks(Brock et al. 2017) ](https://arxiv.org/abs/1708.05344)
*accepted at NeurIPS workshop on Meta-Learning’17* | - | - |
672 | | [Efficient Architecture Search by Network Transformation(Cai et al. 2017) ](https://arxiv.org/abs/1707.04873)
*accepted at AAAI’18* | - | - |
673 | | [Modularized Morphing of Neural Networks(Wei et al. 2017) ](https://arxiv.org/abs/1701.03281) | - | - |
674 |
675 | ### 2016
676 |
677 | - [back to top](#2020)
678 |
679 | | Title | Tags | Code |
680 | |:--------|:--------:|:--------:|
681 | | [Towards Automatically-Tuned Neural Networks(Mendoza et al. 2016) ](http://proceedings.mlr.press/v64/mendoza_towards_2016.html)
*accepted at ICML AutoML workshop* | - | - |
682 | | [Neural Networks Designing Neural Networks: Multi-Objective Hyper-Parameter Optimization(Smithson et al. 2016) ](https://arxiv.org/abs/1611.02120) | - | - |
683 | | [AdaNet: Adaptive Structural Learning of Artificial Neural Networks(Cortes et al. 2016) ](https://arxiv.org/abs/1607.01097) | - | - |
684 | | [Network Morphism(Wei et al. 2016) ](https://arxiv.org/abs/1603.01670) | - | - |
685 | | [Convolutional Neural Fabrics(Saxena and Verbeek 2016) ](https://arxiv.org/abs/1606.02492)
*accepted at NeurIPS’16* | - | - |
686 | | [CMA-ES for Hyperparameter Optimization of Deep Neural Networks(Loshchilov and Hutter 2016) ](https://arxiv.org/abs/1604.07269) | - | - |
687 | | [Designing Neural Network Architectures using Reinforcement Learning(Baker et al. 2016) ](https://arxiv.org/abs/1611.02167)
*accepted at ICLR’17* | - | - |
688 | | [Neural Architecture Search with Reinforcement Learning(Zoph and Le. 2016) ](https://arxiv.org/abs/1611.01578)
*accepted at ICLR’17* | - | - |
689 | | [Learning curve prediction with Bayesian Neural Networks(Klein et al. 2017: accepted at ICLR’17) ](http://ml.informatik.uni-freiburg.de/papers/17-ICLR-LCNet.pdf) | - | - |
690 | | [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization(Li et al. 2016) ](https://arxiv.org/abs/1603.06560) | - | - |
691 |
692 | ### 1988-2015
693 |
694 | - [back to top](#2020)
695 |
696 | | Title | Tags | Code |
697 | |:--------|:--------:|:--------:|
698 | | [Net2Net: Accelerating Learning via Knowledge Transfer(Chen et al. 2015) ](https://arxiv.org/abs/1511.05641)
*accepted at ICLR’16* | - | - |
699 | | [Optimizing deep learning hyper-parameters through an evolutionary algorithm(Young et al. 2015) ](https://dl.acm.org/citation.cfm?id=2834896) | - | - |
700 | | [Practical Bayesian Optimization of Machine Learning Algorithms(Snoek et al. 2012) ](https://papers.nips.cc/paper/4522-practical-bayesian-optimization-of-machine-learning-algorithms.pdf)
*accepted at NeurIPS’12* | - | - |
701 | | [A Hypercube-based Encoding for Evolving large-scale Neural Networks(Stanley et al. 2009) ](https://ieeexplore.ieee.org/document/6792316/) | - | - |
702 | | [Neuroevolution: From Architectures to Learning(Floreano et al. 2008) ](https://link.springer.com/article/10.1007/s12065-007-0002-4)
*accepted at Evolutionary Intelligence’08* | - | - |
703 | | [Evolving Neural Networks through Augmenting Topologies(Stanley and Miikkulainen, 2002) ](http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf)
*accepted at Evolutionary Computation’02* | - | - |
704 | | [Evolving Artificial Neural Networks(Yao, 1999) ](https://ieeexplore.ieee.org/document/784219/)
*accepted at IEEE* | - | - |
705 | | [An Evolutionary Algorithm that Constructs Recurrent Neural Networks(Angeline et al. 1994) ](https://ieeexplore.ieee.org/document/265960/) | - | - |
706 | | [Designing Neural Networks Using Genetic Algorithms with Graph Generation System(Kitano, 1990) ](http://www.complex-systems.com/abstracts/v04_i04_a06/) | - | - |
707 | | [Designing Neural Networks using Genetic Algorithms(Miller et al. 1989) ](https://dl.acm.org/citation.cfm?id=94034)
*accepted at ICGA’89* | - | - |
708 | | [The Cascade-Correlation Learning Architecture(Fahlman and Leblere, 1989) ](https://papers.nips.cc/paper/207-the-cascade-correlation-learning-architecture)
*accepted at NeurIPS’89* | - | - |
709 | | [Self Organizing Neural Networks for the Identification Problem(Tenorio and Lee, 1988) ](https://papers.nips.cc/paper/149-self-organizing-neural-networks-for-the-identification-problem)
*accepted at NeurIPS’88* | - | - |
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