Maintained by Difan Deng and Marius Lindauer; Last update: September 23rd 2020

The following list considers papers related to neural architecture search. It is by no means complete. We highlight papers accepted at conferences and journals; this should hopefully provide some guidance towards high-quality papers. If you miss a paper on the list, please let us know.

Update [Sept’19]: Although NAS methods steadily improve, the quality of empirical evaluations in this field are still lagging behind compared to other areas in machine learning, AI and optimization. We would therefore like to share some best practices for empirical evaluations of NAS methods, which we believe will facilitate sustained and measurable progress in the field. If you are interested in a teaser, please read our blog post or directly jump to our checklist.

• Neural Architecture Search with Improved Genetic Algorithm for Image Classification (Ghosh and Dulal Jana; accepted at ComPE2020)
  https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9200164
• Impact of Neural Architecture Design on Cardiac Abnormality Classification Using 12-lead ECG Signals (Fayyazifar et al. 2020; accepted at cinc 2020)
  https://www.cinc.org/2020/Program/accepted/161_CinCFinalPDF.pdf
• UXNet: Searching Multi-level Feature Aggregation for 3D Medical Image Segmentation (Ji et al. 2020)
  https://arxiv.org/abs/2009.07501
• EfficientNet-eLite: Extremely Lightweight and Efficient CNN Models for Edge Devices by Network Candidate Search (Wang et al. 2020)
  https://arxiv.org/abs/2009.07409
• Achieving Real-Time Execution of Transformer-based Large-scale Models on Mobile with Compiler-aware Neural Architecture Optimization(Niu et al. 2020)
  https://arxiv.org/abs/2009.06823
• DANCE: Differentiable Accelerator/Network Co-Exploration (Choi et al. 2020)
  https://arxiv.org/abs/2009.06237
• RelativeNAS: Relative Neural Architecture Search via Slow-Fast Learning (Tan et al. 2020)
  https://arxiv.org/abs/2009.06193
• AutoML for Multilayer Perceptron and FPGA Co-design (Colangelo et al. 2020)
  https://arxiv.org/abs/2009.06156
• From Federated Learning to Federated Neural Architecture Search: a Survey (Zhu et al. 2020)
  https://arxiv.org/pdf/2009.05868.pdf
• Disentangling Neural Architectures and Weights: A Case Study in Supervised Classification (Colombo and Gao 2020)
  https://arxiv.org/abs/2009.05346
• Optimizing Convolutional Neural Network Architecture via Information Field (Wang et al. 2020)
  https://arxiv.org/abs/2009.05236
• Binarized Neural Architecture Search for Efficient Object Recognition (Chen et al. 2020)
  https://arxiv.org/abs/2009.04247
• An Intelligent Analysis Framework for Clinical-Translational MRI Research (Yang 2020)
  https://etd.ohiolink.edu/pg_10?0::NO:10:P10_ACCESSION_NUM:case1592254585828664
• Neural Architecture Search via Bayesian Optimization with a Neural Network Model (White et al. 2019)
  https://www.semanticscholar.org/paper/Neural-Architecture-Search-via-Bayesian-with-a-White-Neiswanger/244476d513f0252215566e25a7fbef3a2d4bf9d0?p2df
• AutoKWS: Keyword Spotting With Differentiable Architecture Search (Zhang et al. 2020)
  https://arxiv.org/abs/2009.03658
• Are Deep Neural Architectures Losing Information? Invertibility Is Indispensable (Liu et al. 2020)
  https://arxiv.org/abs/2009.03173
• AutoTrans: Automating Transformer Design via Reinforced Architecture Search (Zhu et al. 2020)
  https://arxiv.org/abs/2009.02070
• S3NAS: Fast NPU-aware Neural Architecture Search Methodology (Lee et al. 2020)
  https://arxiv.org/abs/2009.02009
• Adversarially Robust Neural Architectures (Dong et al. 2020)
  https://arxiv.org/abs/2009.00902
• Quantum-Inspired Evolutionary Algorithm for Convolutional Neural Networks Architecture Search (Ye et al. 2020; accepted at CEC 2020)
  https://ieeexplore.ieee.org/abstract/document/9185727
• Improving Deep Learning based Optical Character Recognition via Neural Architecture Search (Zhao et al. 2020; accepted at CEC 2020)
  https://ieeexplore.ieee.org/abstract/document/9185798
• NATS-Bench: Benchmarking NAS algorithms for Architecture Topology and Size (Dong et al. 2020)
  https://arxiv.org/abs/2009.00437
• Boosting share routing for multi-task learning (Chen et al. 2020)
  https://arxiv.org/abs/2009.00387
• MNET: Fruit Lesion Classification Convnet Design via Efficient Neural Architecture Search (Chen et al. 2020)
  https://www.csie.ntu.edu.tw/~fuh/personal/MNetFruitLesionClassificationConvnetDesignviaEfficientNeuralArchitectureSearch.pdf
• DATA: Differentiable ArchiTecture Approximation with DIstribution Guided Sampling (Zhang et al. 2020; accepted at IEEE Transactions on Pattern Analysis and Machine Intelligence)
  https://ieeexplore.ieee.org/abstract/document/9181426
• Scaling Up Deep Neural Network Optimization for Edge Inference (Lu et al. 2020)
  https://arxiv.org/abs/2009.00278
• Understanding the wiring evolution in differentiable neural architecture search (Xie et al. 2020)
  https://arxiv.org/abs/2009.01272
• DARTS-: Robustly Stepping our of performance collapse without indicators (Chu et al. 2020)
  https://arxiv.org/abs/2009.01027
• Neural Architecture Search For Keyword Spotting (Mo et al. 2020; accepted at InterSpeech 2020)
  https://arxiv.org/abs/2009.00165
• AutoDLCon: An Approach for Controlling the Automated TUning for Deep Learning Networks (Kotb et al. 2020; accepted at BigDataService)
  https://ieeexplore.ieee.org/abstract/document/9179562
• AutoGesNet: AutoGesture Recognition Network Based on Neural Architecture Search (Li et al. 2020; accepted at ICACI 2020)
  https://ieeexplore.ieee.org/abstract/document/9177723
• A New Training Protocol for Performance Predictors of Evolutionary Neural Architecture Search Algorithms (Sun et al. 2020)
  https://arxiv.org/abs/2008.13187
• Graph Neural Network Architecture Search for Molecular Property Prediction (Jiang and Balaprakash 2020)
  https://arxiv.org/abs/2008.12187
• NASirt: AutoML based learning with instance-level complexity information (Neto et al. 2020)
  https://arxiv.org/abs/2008.11846
• NAS-DIP: Learning Deep Image Prior with Neural Architecture Search (Chen et al. 2020; accepted at ECCV2020)
  https://arxiv.org/abs/2008.11713
• Simplifying Architecture Search for Graph Neural Network (Zhao et al. 2020; accepted at CIKM 2020 workshop)
  https://arxiv.org/abs/2008.11652
• Learned Transferable Architectures Can Surpass Hand-Designed Architectures for Large Scale Speech Recognition (He et al. 2020)
  https://arxiv.org/abs/2008.11589
• A Survey on Evolutionary Neural Architecture Search (Liu et al. 2020)
  https://arxiv.org/abs/2008.10937
• LC-NAS: Latency Constrained Neural Architecture Search for Point Cloud Networks (Li et al. 2020)
  https://arxiv.org/abs/2008.10309
• Automated Search for Resource-Efficient Branched Multi-Task Networks (Bruggemann et al. 2020; accepted at BMVC 2020)
  https://arxiv.org/abs/2008.10292
• Nas-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search (Siems et al. 2020)
  https://arxiv.org/abs/2008.09777
• Searching Multi-Rate and Multi-Modal Temporal Enhanced Networks for Gesture Recognition (Yu et al. 2020)
  https://arxiv.org/abs/2008.09412
• Evolutionary Multi-Objective Bi-Level Optimization for Efficient Deep Neural Network Architecture Design (Lu 2020)
  https://search.proquest.com/openview/5d334c621c03067d93d00ef41cb28f3b/1?pq-origsite=gscholar&cbl=18750&diss=y
• Automated And Lightweight Network Design via Random Search for Remote Sensing Image Scene Classification (Li et al. 2020; accepted  at The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences; Gottingen)
  https://search.proquest.com/docview/2434106697?pq-origsite=gscholar&fromopenview=true
• NASCaps: A Framework for Neural Architecture Search to Optimize the Accuracy and Hardware Efficiency of Convolutional Capsule Networks (Marchisio et al. 2020; accepted at ICCAD’20)
  https://arxiv.org/abs/2008.08476
• Enhanced MRI Reconstruction Network using Neural Architecture Search (Huange et al. 2020)
  https://arxiv.org/abs/2008.08248
• Discovering Multi-Hardware Mobile Models via Architecture Search (Chu et al. 2020)
  https://arxiv.org/abs/2008.08178
• NASE: Learning Knowledge Graph Embedding for Link Prediction via Neural Architecture Search (Kou et al. 2020; accepted at CIKM 2020)
  https://arxiv.org/abs/2008.07723
• Towards Cardiac Intervention Assistance: Hardware-aware Neural Architecture Exploration for Real-Time 3D Cardiac Cine MRI Segmentation (Zeng et al. 2020; accepted at ICCAD 2020)
  https://arxiv.org/abs/2008.07071
• AutoPose: Searching Multi-Scale Branch Aggregation for Pose Estimation (Gong et al. 2020)
  https://arxiv.org/abs/2008.07018
• Finding Fast Transformers: One-Shot Neural Architecture Search by Component Composition(Tsai et al. 2020)
  https://arxiv.org/abs/2008.06808
• Towards Part-aware Monocular 3D Human Pose Estimation: An Architecture Search Approach (Chen et al. 2020; accepted at ECCV 2020)
  http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480715.pdf
• AutoSTR: Efficient Backbone Search for Scene Text Recognition (Zhang et al. 2020; accepted at ECCV 2020)
  https://arxiv.org/abs/2003.06567
• Network Architecture Search for Domain Adaptation (Li and Peng 2020)
  https://arxiv.org/abs/2008.05706
• Evolutionary Algorithm Enhanced Neural Architecture Search for Text-Independent Speaker Verification (Qu et al. 2020; accepted at INTERSPEECH 2020)
  https://arxiv.org/abs/2008.05695
• RARTS: a Relaxed Architecture Search Method (Xue et al. 2020)
  https://arxiv.org/abs/2008.03901
• NASB: Neural Architecture Search for Binary Convolutional Neural Networks (Zhu et al. 2020)
  https://arxiv.org/abs/2008.03515
• A Surgery of the Neural Architecture Evaluators (Ning et al. 2020)
  https://arxiv.org/abs/2008.03064
• TF-NAS: Rethinking Three Search Freedoms of Latency-Constrained Differentiable Neural Architecture Search (Hu et al. 2020; accepted at ECCV 2020)
  https://alfredxiangwu.github.io/papers/hu2020eccv.pdf
• Balanced One-shot Neural Architecture Optimization (Luo et al. 2020)
  https://www.researchgate.net/profile/Renqian_Luo/publication/336018352_Understanding_and_Improving_One-shot_Neural_Architecture_Optimization/links/5f24069992851cd302cb4e83/Understanding-and-Improving-One-shot-Neural-Architecture-Optimization.pdf
• Deep Convolution Features in Non-linear Embedding Space for Fundus Image Classification (Dondeti et al. 2020; accepted at Revue d’Intelligence Artificielle)
  http://www.iieta.org/journals/ria/paper/10.18280/ria.340308
• 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
• Evolving Multi-Resolution Pooling CNN for Monaural Singing Voice Separation (Yuan et al. 2020)
  https://arxiv.org/abs/2008.00816
• Weight-Sharing Neural Architecture Search: A Battle to Shrink the Optimization Gap (Xie et al. 2020)
  https://arxiv.org/abs/2008.01475
• Neural Architecture Search in Graph Neural Networks (Nunes and L.Pappa 2020)
  https://arxiv.org/abs/2008.00077
• Anti-Bandit Neural Architecture Search for Model Defense (Chen et al. 2020; accepted at ECCV 2020)
  https://arxiv.org/abs/2008.00698
• HMCNAS: Neural Architecture Search Using Hidden Markov Chains And Bayesian Optimization(Lopes and Alexandre 2020)
  https://arxiv.org/abs/2007.16149
• Neural Architecture Search as Sparse Supernet(Wu et al. 2020)
  https://arxiv.org/abs/2007.16112
• Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution (Tang et al. 2020; accepted at ECCV 2020)
  https://arxiv.org/abs/2007.16100
• Growing Efficient Deep Networks by Structured Continuous Sparsification (Yuan et al. 2020)
  https://arxiv.org/abs/2007.15353
• Lidar Data Classification Based on Automatic Designed CNN (Xie and Chen 2020; accepted at IEEE Geoscience and Remote Sensing Letters)
  https://ieeexplore.ieee.org/abstract/document/9139215
• Fusion Mechanisms for Human Activity Recognition using Automated Machine Learning (Popescu et al. 2020; accepted at IEEE Access)
  https://ieeexplore.ieee.org/document/9153764
• Mixed-Precision Quantization for CNN-Based Remote Sensing Scene Classification (Wei et al. 2020; accepted at IEEE Geoscience and Remote Sensing Letters)
  https://ieeexplore.ieee.org/abstract/document/9153122/authors#authors
• Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound (Huang et al. 2020; accepted at MICCAI 2020)
  https://arxiv.org/abs/2007.15273
• TF-NAS: Rethinking Three Search Freedoms of Latency-Constrained Differentiable Neural Architecture Search (Hu et al. 2020; accepted at ECCV 2020)
  https://alfredxiangwu.github.io/papers/hu2020eccv.pdf
• Efficient Oct Image Segmentation Using Neural Architecture Search (Gheshlaghi et al. 2020)
  https://arxiv.org/abs/2007.14790
• SOTERIA: In Search of Efficient Neural Networks for Private Inference (Aggarwal et al. 2020)
  https://arxiv.org/abs/2007.12934
• What and Where: Learn to Plug Adapters via NAS for Multi-Domain Learning (Zhao et al. 2020)
  https://arxiv.org/abs/2007.12415
• CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending (Xu et al. 2020; accepted at ECCV 2020)
  https://arxiv.org/abs/2007.12147
• Representation Sharing for Fast Object Detector Search and Beyond (Zhou et al .2020; accepted at ECCV 2020)
  https://arxiv.org/abs/2007.12075
• AttentionNAS: Spatiotemporal Attention Cell Search for Video Classification (Wang et al. 2020; accepted at ECCV 2020)
  https://arxiv.org/abs/2007.12034
• Weight-Sharing Neural Architecture Search: A Battle to Shrink the Optimization Gap (Xie et al. 2020)
  https://arxiv.org/abs/2008.01475
• MCUNet: Tiny Deep Learning on IoT Devices (Lin et al. 2020)
  https://arxiv.org/abs/2007.10319
• Search What You Want: Barrier Panelty NAS for Mixed Precision Quantization (Yu et al. 2020; accepted at ECCV 2020)
  https://arxiv.org/abs/2007.10026
• NSGANetV2: Evolutionary Multi-Objective Surrogate-Assisted Neural Architecture Search (Lu et al. 2020; accepted at ECCV 2020)
  https://arxiv.org/abs/2007.10396
• CATCH: Context-based Meta Reinforcement Learning for Transferrable Architecture Search (Chen et al. 2020; accepted at ECCV 2020)
  https://arxiv.org/abs/2007.09380
• Standing on the Shoulders of Giants: Hardware and Neural Architecture Co-Search with Hot Start (Jiang et al. 2020; accepted at IEEE Transactions On Computer-Aided Design of Integrated Circuits and System)
  https://arxiv.org/abs/2007.09087
• Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search (Tian et al. 2020; accepted at ECCV 2020)
  https://arxiv.org/abs/2007.09180
• Neural Architecture Search for Speech Recognition (Hu et al. 2020)
  https://arxiv.org/abs/2007.08818
• BRP-NAS: Prediction-based NAS using GCNs (Chau et al .2020)
  https://arxiv.org/abs/2007.08668
• Finding Non-Uniform Quantization Schemes using Multi-Task Gaussian Processes (do Nascimento et al. 2020, accepted at ECCV 2020)
  https://ui.adsabs.harvard.edu/abs/2020arXiv200707743G/abstract
• One-Shot Neural Architecture Search via Novelty Driven Sampling (Zhang et al. 2020; accepted at IJCAI 2020)
  https://www.ijcai.org/Proceedings/2020/0441.pdf
• Neural Architecture Search in A Proxy Validation Loss Landscape (Li et al. 2020; accepted at ICML 2020)
  https://proceedings.icml.cc/static/paper_files/icml/2020/439-Paper.pdf
• CP-NAS: Child-Parent Neural Architecture Search for 1-bit CNNs (Zhuo et al. 2020; accepted at IJCAI 2020)
  https://www.ijcai.org/Proceedings/2020/0144.pdf
• SI-VDNAS: Semi-Implicit Variational Dropout for Hierarchical One-shot Neural Architecture Search (Wang et al. 2020; accepted at IJCAI 2020)
  https://www.ijcai.org/Proceedings/2020/0289.pdf
• An Empirical Study on the Robustness of NAS based Architectures (Devaguptapu et al. 2020)
  https://arxiv.org/abs/2007.08428
• MergeNAS: Merge Operations into One for Differentiable Architecture Search (Wang et al. 2020; accepted at IJCAI 2020)
  https://www.ijcai.org/Proceedings/2020/0424.pdf
• DropNAS: Grouped Operation Dropout for Differentiable Architecture Search (Hong et al. 2020)
  https://www.ijcai.org/Proceedings/2020/0322.pdf
• Evolving Robust Neural Architectures to Defend from Adversarial Attacks (Kotyan and Vargas 2020; accepted at Proceedings of the Workshop on Artificial Intelligence Safety  2020)
  http://ceur-ws.org/Vol-2640/paper_1.pdf
• Architecture Search of Dynamic Cells for Semantic Video Segmentation (Nekrasov et al. 2020; accepted at WACV 2020)
  https://openaccess.thecvf.com/content_WACV_2020/papers/Nekrasov_Architecture_Search_of_Dynamic_Cells_for_Semantic_Video_Segmentation_WACV_2020_paper.pdf
• Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search (Guo et al. 2020)
  https://arxiv.org/abs/2007.07197
• Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction (Song et al. 2020; accepted at KDD2020)
  https://arxiv.org/abs/2007.06434
• MS-NAS: Multi-Scale Neural Architecture Search for Medical Image Segmentation (Yan et al. 2020)
  https://arxiv.org/abs/2007.06151
• VINNAS: Variational Inference-based Neural Network Architecture Search (Ferianc et al. 2020)
  https://arxiv.org/abs/2007.06103
• Multi-Modality Information Fusion for Radiomics-based Neural Architecture Search (Peng et al. 2020)
  https://arxiv.org/abs/2007.06002
• Graph Neural Architecture Search (Gao et al. 2020; accepted at IJCAI 2020)
  https://www.researchgate.net/profile/Chuan_Zhou5/publication/342789484_Graph_Neural_Architecture_Search/links/5f0be495299bf18816197d15/Graph-Neural-Architecture-Search.pdf
• Ensembles of Networks Produced from Neural Architecture Search (Herron et al. 2020)
  https://keuperj.github.io/MLHPCS/paper/NASEnsemblesFinal.pdf
• A Study on Encodings for Neural Architecture Search (White et al. 2020)
  https://arxiv.org/pdf/2007.04965.pdf
• Neural Architecture Search with GBDT (Luo et al. 2020)
  https://arxiv.org/abs/2007.04785
• NASGEM: Neural Architecture Search via Graph Embedding Method (Cheng et al. 2020)
  https://arxiv.org/abs/2007.04452
• Neuro-evolution using Game-Driven Cultural Algorithms (Waris and Reynolds, accepted at GECCO 2020)
  https://dl.acm.org/doi/abs/10.1145/3377929.3398093
• An Evolution-based Approach for Efficient Differentiable Architecture Search (Kobayashi and Nagao; accepted at GECCO 2020)
  https://dl.acm.org/doi/abs/10.1145/3377929.3390003
• HyperFDA: a bi-level Optimization Approach to Neural Architecture Search and Hyperparameters’ optimization via fractal decomposition-based algorithm (Souquet et al. 2020; accepted at GECCO 2020)
  https://dl.acm.org/doi/abs/10.1145/3377929.3390056
• Towards Evolving Robust Neural Architectures to Defend From Adversarial Attacks (Kotyan and Vargas; accepted at GECCO 2020)
  https://dl.acm.org/doi/abs/10.1145/3377929.3390004
• A first Step toward Incremental Evolution of Convolutional Neural Networks (Barnes et al. 2020; accepted at GECCO 2020)
  https://dl.acm.org/doi/abs/10.1145/3377929.3389916
• Computational model for neural architecture search (Gottapu 2020)
  https://scholarsmine.mst.edu/cgi/viewcontent.cgi?article=3871&context=doctoral_dissertations
• Neural Architecture Search for extreme multi-label classification: an evolutionary approach (Pauletto et al. 2020)
  https://hal.archives-ouvertes.fr/hal-02889047/document
• Journey Towards Tiny Perceptual Super-Resolution (Lee et al. 2020)
  https://arxiv.org/abs/2007.04356
• Hyperparameter Optimization in Neural Networks via Structured Sparse Recovery (Cho et al. 2020)
  https://arxiv.org/abs/2007.04087
• GOLD-NAS: Gradual, One-Level, Differentiable (Bi et al. 2020)
  https://arxiv.org/abs/2007.03331
• Discretization-Aware Architecture Search (Tian et al. 2020)
  https://arxiv.org/abs/2007.03154
• Parametric machines: a fresh approach to architecture search (Vertechi et al. 2020)
  https://arxiv.org/abs/2007.02777
• Multi-Objective Neural Architecture Search Based on Diverse Structures and Adaptive Recommendation (Wang et al. 2020)
  https://arxiv.org/abs/2007.02749
• Surrogate-assisted Particle Swarm Optimisation for Evolving Variable-length Transferable(Wang et al. 2020)
  https://arxiv.org/abs/2007.01556
• Self-supervised Neural Architecture Search (Kaplan and Giryes 2020)
  https://arxiv.org/abs/2007.01500
• M-NAS: Meta Neural Architecture Search (Wang et al. 2020; accepted at AAAI 2020)
  https://aaai.org/ojs/index.php/AAAI/article/view/6084
• FiFTy: Large-scale File Fragment Type Identification using Convolutional Neural Networks (Mittal et al. 2020; accepted at IEEE Transactions on Information Forensics and Security)
  https://ieeexplore.ieee.org/abstract/document/9122499
• RSNet: The Search for Remote Sensing Deep Neural Networks in Recognition Tasks (Wang et al. 2020; accepted at IEEE Transactions on Geoscience and Remote Sensing)
  https://ieeexplore.ieee.org/abstract/document/9123590
• Theory-Inspired Path-Regularized Differential Network Architecture Search (Zhou et al. 2020)
  https://arxiv.org/abs/2006.16537
• The Heterogeneity Hypothesis: Finding Layer-Wise Dissimilated Network Architecture (Li et al. 2020)
  https://arxiv.org/abs/2006.16242
• Semi-Discrete Optimization Through Semi-Discrete Optimal Transport: A Framework for Neural Architecture Search (Trillos and Morales 2020)
  https://arxiv.org/abs/2006.15221
• Traditional And Accelerated Gradient Descent for Neural Architecture Search (Trillos et al. 2020)
  https://arxiv.org/abs/2006.15218
• AutoSNAP: Automatically Learning Neural Architectures for Instrument Pose Estimation (Kügler et al. 2020)
  https://arxiv.org/abs/2006.14858
• Evolutionary Recurrent Neural Architecture Search (Tian et al. 2020; accepted at IEEE Embedded System Letters)
  https://ieeexplore.ieee.org/abstract/document/9129784
• Neural-Architecture-Search-Based Multiobjective Cognitive Automation System (Wang et al. 2020; accepted at IEEE System Journal)
  https://ieeexplore.ieee.org/abstract/document/9127493
• Enhancing Model Parallelism in Neural Architecture Search for Multi-device System (Fu et al. 2020; accepted at IEEE Micro)
  https://ieeexplore.ieee.org/abstract/document/9127125
• AutoST: Efficient Neural Architecture Search for Spatio-Temporal Prediction (Li et al. 2020; accepted at KDD 2020)
  http://urban-computing.com/pdf/AutoST_kdd20_camera_ready.pdf
• Neural Architecture Search for Sparse DenseNets with Dynamic Compression (O’Neill et al. 2020; accepted at GECCO 2020)
  https://dl.acm.org/doi/abs/10.1145/3377930.3390178
• Searching towards Class-Aware Generators for Conditional Generative Adversarial Networks (Zhou et al. 2020)
  https://arxiv.org/abs/2006.14208
• Neural Architecture Design for GPU-Efficient Networks (Lin et al. 2020)
  https://arxiv.org/abs/2006.14090
• Equivalence in Deep Neural Networks via Conjugate Matrix Ensembles (Süzen 2020)
  https://arxiv.org/abs/2006.13687
• Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL (Zimmer et al. 2020)
  https://arxiv.org/abs/2006.13799
• NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search (Panda et al. 2020)
  https://arxiv.org/abs/2006.13314
• Tiny Video Networks: Architecture Search for Efficient Video Models (Piergiovanni et al. 2020; accepted at 7th ICML Workshop on Automated Machine Learning, 2020)
  https://storage.googleapis.com/pub-tools-public-publication-data/pdf/b2238e19f1f4abdf17c5fff0f3fa824c5eee1e78.pdf
• FNA++: Fast Network Adaptation via Parameter Remapping and Architecture Search (Fang et al. 2020)
  https://arxiv.org/abs/2006.12986
• Neural networks adapting to datasets: learning network size and topology (Janik and Nowak 2020)
  https://arxiv.org/abs/2006.12195
• AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning (Li et al. 2020)
  https://arxiv.org/abs/2006.11321
• Reinforcement Learning Aided Network Architecture Generation for JPEG Image Steganalysis (Yang et al. 2020; accepted at Proceedings of the 2020 ACM Workshop on Information Hiding and Multimedia Security)
  https://dl.acm.org/doi/proceedings/10.1145/3369412
• Neural Architecture Search for Time Series Classification (Rakhshani et al. 2020; accepted at ijcnn 2020)
  https://germain-forestier.info/publis/ijcnn2020.pdf
• Cyclic Differentiable Architecture Search (Yu et al. 2020)
  https://arxiv.org/abs/2006.10724
• Differentially-private Federated Neural Architecture Search (Singh et al. 2020)
  https://arxiv.org/abs/2006.10559
• DrNAS: Dirichlet Neural Architecture Search (Chen et al. 2020)
  https://arxiv.org/abs/2006.10355
• Neural Architecture Optimization with Graph VAE (Li et al. 2020)
  https://arxiv.org/abs/2006.10310
• Fine-Grained Stochastic Architecture Search (Chaudhuri et al. 2020)
  https://arxiv.org/abs/2006.09581
• Bonsai-Net: One-Shot Neural Architecture Search via Differentiable Pruners (Geada et al. 2020)
  https://arxiv.org/abs/2006.09264
• AlphaGAN: Fully Differentiable Architecture Search for Generative Adversarial Networks (Tian et al. 2020)
  https://arxiv.org/abs/2006.09134
• Fine-Tuning DARTS for Image Classification (Tanveer et al. 2020)
  https://arxiv.org/abs/2006.09042
• Neural Anisotropy Directions (Ortiz-Jiménez et al. 2020)
  https://arxiv.org/abs/2006.09717
• CryptoNAS: Private Inference on a ReLU Budget (Ghodsi et al. 2020)
  https://arxiv.org/abs/2006.08733
• Heuristic Architecture Search Using Network Morphism for Chest X-Ray Classification (Radiuk and Kutucu 2020)
  http://ceur-ws.org/Vol-2623/paper11.pdf
• Task-aware Performance Prediction for Efficient Architecture Search (Kokiopoulou et al. 2020; accepted at ECAI 2020)
  http://ecai2020.eu/papers/256_paper.pdf
• Beyond Network Pruning: a Joint Search-and-Training Approach (Lu et al. 2020; accepted at IJCAI 2020)
  http://see.xidian.edu.cn/faculty/wsdong/Papers/Conference/ijcai20.pdf
• Neural Ensemble Search for Performant and Calibrated Predictions (Zaidi et al. 2020)
  https://arxiv.org/pdf/2006.08573.pdf
• Multi-fidelity Neural Architecture Search with Knowledge Distillation (Trofimov et al. 2020)
  https://arxiv.org/abs/2006.08341
• Differentiable Neural Architecture Transformation for Reproducible Architecture Improvement (Kim et al. 2020)
  https://arxiv.org/pdf/2006.08231.pdf
• Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search (Nguyen et al. 2020)
  https://arxiv.org/abs/2006.07593
• Neural Architecture Search using Bayesian Optimisation with Weisfeiler-Lehman Kernel (Ru et al. 2020)
  https://arxiv.org/abs/2006.07556
• NAS-Bench-NLP: Neural Architecture Search Benchmark for Natural Language Processing (Klyuchnikov et al. 2020)
  https://arxiv.org/abs/2006.07116
• Does Unsupervised Architecture Representation Learning Help Neural Architecture Search? (Yan et el. 2020)
  https://arxiv.org/abs/2006.06936
• Few-shot Neural Architecture Search (Zhao et al. 2020)
  https://arxiv.org/abs/2006.06863
• NADS: Neural Architecture Distribution Search for Uncertainty Awareness (Ardywibowo et al. 2020)
  https://arxiv.org/abs/2006.06646
• Towards Efficient Automated Machine Learning (Li 2020)
  http://reports-archive.adm.cs.cmu.edu/anon/ml2020/CMU-ML-20-104.pdf
• AMER: Automatic Behavior Modeling and Interaction Exploration in Recommender System (Zhao et al. 2020)
  https://arxiv.org/abs/2006.05933
• Neuroevolution in Deep Neural Networks: Current Trends and Future Challenges (Galvan and Mooney 2020)
  https://arxiv.org/abs/2006.05415
• AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks (Fu et al. 2020; accepted at ICML 2020)
  https://arxiv.org/abs/2006.08198
• Does Unsupervised Architecture Representation Learning Help Neural Architecture Search? (Yan et al. 2020)
  https://arxiv.org/abs/2006.06936
• Hardware-Aware Transformable Architecture Search with Efficient Search Space (Jiang et al. 2020; accpeted at ICME 2020)
  https://ieeexplore.ieee.org/abstract/document/9102721
• Sparse CNN Archtitecture Search (Scas) (Yeshwanth et al. 2020; accepted at ICME 2020)
  https://ieeexplore.ieee.org/abstract/document/9102879
• Auto-Generating Neural Networks with Reinforcement Learning for Multi-Purpose Image Forensics (Wei et al. 2020; accepted at ICME 2020)
  https://ieeexplore.ieee.org/abstract/document/9102943
• Neural Architecture Search without Training (Mellor et al. 2020)
  https://arxiv.org/abs/2006.04647
• Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search (Ru et al. 2020)
  https://arxiv.org/abs/2006.04492
• Differentiable Neural Input Search for Recommender Systems (Cheng et al. 2020)
  https://arxiv.org/abs/2006.04466
• Efficient Architecture Search for Continual Learning (Gao et al. 2020)
  https://arxiv.org/abs/2006.04027
• Conditional Neural Architecture Search (Kao et al. 2020)
  https://arxiv.org/abs/2006.03969
• AutoHAS: Differentiable Hyper-parameter and Architecture Search (Dong et al. 2020)
  https://arxiv.org/abs/2006.03656
• Modeling Task-based fMRI Data via Deep Belief Network with Neural Architecture Search (Qiang et al. 2020; accepted at Computerized Medical Imaging and Graphics)
  https://www.sciencedirect.com/science/article/abs/pii/S0895611120300501
• Fast Hardware-Aware Neural Architecture Search (Zhang et al. 2020; accepted at CVPR 2020 workshop)
  http://openaccess.thecvf.com/content_CVPRW_2020/papers/w40/Zhang_Fast_Hardware-Aware_Neural_Architecture_Search_CVPRW_2020_paper.pdf
• Memory-Efficient Hierarchical Neural Architecture Search for Image Denoising (Zhang et al. 2020; accepted at CVPR 2020)
  http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_Memory-Efficient_Hierarchical_Neural_Architecture_Search_for_Image_Denoising_CVPR_2020_paper.pdf
• GP-NAS: Gaussian Process based Neural Architecture Search (Li et al. 2020; accepted at CVPR 2020)
  http://openaccess.thecvf.com/content_CVPR_2020/papers/Li_GP-NAS_Gaussian_Process_Based_Neural_Architecture_Search_CVPR_2020_paper.pdf
• MemNAS: Memory-Efficient Neural Architecture Search with Grow-Trim Learning (Liu et al.2020; accepted at CVPR 2020)
  http://openaccess.thecvf.com/content_CVPR_2020/papers/Liu_MemNAS_Memory-Efficient_Neural_Architecture_Search_With_Grow-Trim_Learning_CVPR_2020_paper.pdf
• Can weight sharing outperform random architecture search? An investigation with TuNAS (Bender et al. 2020; accepted at CVPR 2020)
  http://openaccess.thecvf.com/content_CVPR_2020/papers/Bender_Can_Weight_Sharing_Outperform_Random_Architecture_Search_An_Investigation_With_CVPR_2020_paper.pdf
• Butterfly Transform: An Efficient FFT Based Neural Architecture Design (Alizadeh vahid et al. 2020; accepted at CVPR 2020)
  http://openaccess.thecvf.com/content_CVPR_2020/papers/vahid_Butterfly_Transform_An_Efficient_FFT_Based_Neural_Architecture_Design_CVPR_2020_paper.pdf
• APQ: Joint Search for Network Architecture, Pruning and Quantization Policy (Wang et al.2020; accepted at CVPR 2020)
  http://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_APQ_Joint_Search_for_Network_Architecture_Pruning_and_Quantization_Policy_CVPR_2020_paper.pdf
• SP-NAS: Serial-to-Parallel Backbone Search for Object Detection (Jiang et al. 2020; accepted at CVPR 2020)
  http://openaccess.thecvf.com/content_CVPR_2020/papers/Jiang_SP-NAS_Serial-to-Parallel_Backbone_Search_for_Object_Detection_CVPR_2020_paper.pdf
• All in One Bad Weather Removal using Architectural Search (Li et al. 2020; accepted at CVPR 2020)
  http://openaccess.thecvf.com/content_CVPR_2020/papers/Li_All_in_One_Bad_Weather_Removal_Using_Architectural_Search_CVPR_2020_paper.pdf
• NeuralScale: Efficient Scaling of Neurons for Resource-Constrained Deep Neural Networks (Lee and Lee; accepted at CVPR 2020)
  http://openaccess.thecvf.com/content_CVPR_2020/papers/Lee_NeuralScale_Efficient_Scaling_of_Neurons_for_Resource-Constrained_Deep_Neural_Networks_CVPR_2020_paper.pdf
• 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
• A Comprehensive Survey of Neural Architecture Search: Challanges and Solutions (Ren et al. 2020)
  https://arxiv.org/abs/2006.02903
• FBNetV3: Joint Architecture-Recipe Search using Neural Acquisition Function (Dai et al. 2020)
  https://arxiv.org/abs/2006.02049
• Neural Architecture Search With Reinforce And Masked Attention Autoregressive Density Estimators (Krishna et al. 2020)
  https://arxiv.org/abs/2006.00939
• Automation of Deep Learning – Theory and Practice (Wistuba et al. 2020; accepted at ICMR 202)
  https://dl.acm.org/doi/abs/10.1145/3372278.3390739
• AdaEn-Net: An Ensemble of Adaptive 2D-3D Fully Convolutional Networks for Medical Image Segmentation (Baldeon Calisto and Lai-Yuen. 2020; accepted at Neural Networks)
  https://www.sciencedirect.com/science/article/pii/S0893608020300848
• DC-NAS: Divide-and-Conquer Neural Architecture Search (Wang et al. 2020)
  https://arxiv.org/abs/2005.14456
• HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens (Yang et al. 2020)
  https://arxiv.org/abs/2005.14446
• Designing Resource-Constrained Neural Networks Using Neural Architecture Search Targeting Embedded Devices (Cassimon et al. 2020; accepted at IEEE Internet of Things)
  https://www.sciencedirect.com/science/article/pii/S2542660520300676
• Searching Better Architectures for Neural Machine Translation (Fan et al. 2020; accepted at IEEE/ACM Transactions on Audio, Speech, and Language Processing)
  https://ieeexplore.ieee.org/abstract/document/9095246
• Automated Design of Neural Network Architectures with Reinforcement Learning for Detection of Global Manipulations (Chen et al. 2020; accepted at IEEE Journal of Selected Topics in Signal Processing)
  https://ieeexplore.ieee.org/abstract/document/9103245
• A New Deep Neural Architecture Search Pipeline for Face Recognition (Zhu et al. 2020; accepted at IEEE Access)
  https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9091879
• Regularized Evolution for Marco Neural Architecture Search (Kyriakides and Margaritis; accepted at AIAI2020)
  https://link.springer.com/chapter/10.1007/978-3-030-49186-4_10
• Evolutionary NAS with Gene Expression Programming of Cellular Encoding (Broni-Bediako et al. 2020)
  https://arxiv.org/abs/2005.13110
• Synthetic Petri Dish: A Novel Surrogate Model for Rapid Architecture Search (Rawal et al. 2020)
  https://arxiv.org/abs/2005.13092
• Designing Convolutional Neural Network Architectures Using Cartesian Genetic Programming (Suganuma et al. 2020; accepted in book on “Deep Neural Evolution”)
  https://link.springer.com/chapter/10.1007/978-981-15-3685-4_7
• An Introduction to Neural Architecture Search for Convolutional Networks (Kyriakides and Margaritis, 2020)
  https://arxiv.org/abs/2005.11074
• AutoSegNet: An Automated Neural Network for Image Segmentation (Xu et al. 2020; accepted at IEEE Access)
  https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9095283
• DMS: Differentiable Dimension Search for Binary Neural Networks (Li et al. 2020; accepted at 1st Workshop on Neural Architecture Search at ICLR 2020)
  https://xhplus.github.io/publication/conference-paper/iclr2020/dms/DMS.pdf
• Evolving Deep Neural Networks for X-ray Based Detection of Dangerous Objects (Tsukada et al. 2020; accepted in book on “Deep Neural Evolution”)
  https://link.springer.com/chapter/10.1007/978-981-15-3685-4_12
• Powering One-shot Topological NAS with Stabilized Share-parameter Proxy (Guo et al. 2020)
  https://arxiv.org/abs/2005.10511
• Optimize CNN Model for FMRI Signal Classification Via Adanet-Based Neural Architecture Search (Dai et al. 2020; accepted at IEEE ISBI)
  https://ieeexplore.ieee.org/abstract/document/9098574
• Rethinking Performance Estimation in Neural Architecture Search (Zheng et al. 2020; accepted at CVPR 2020)
  https://arxiv.org/abs/2005.09917
• 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
• HNAS: Hierarchical Neural Architecture Search on Mobile Devices (Xia et al. 2020)
  https://arxiv.org/abs/2005.07564
• Improving Neuroevolution Using Island Extinction And Repopulation (Lyu et al. 2020)
  https://arxiv.org/abs/2005.07376
• A Framework for Exploring and Modelling Neural Architecture Search Methods (Radiuk et al. 2020)
  http://ceur-ws.org/Vol-2604/paper70.pdf
• 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
• DARTS-ASR: Differentiable Architecture Search for Multilingual Speech Recognition and Adaptation (Chen et al. 2020)
  https://arxiv.org/abs/2005.07029
• A Semi-Supervised Assessor of Neural Architectures (Tang et al. 2020; accepted at CVPR 2020)
  https://arxiv.org/abs/2005.06821
• Neural Architecture Search for Gliomas Segmentation on Multimodal Magnetic Resonance Imaging (Wang et al. 2020)
  https://arxiv.org/abs/2005.06338
• Binarizing MobileNet via Evolution-based Searching (Phan et al. 2020)
  https://arxiv.org/abs/2005.06305
• Neural Architecture Transfer (Lu et al. 2020)
  https://arxiv.org/abs/2005.05859
• Optimization of deep neural networks: a survey and unified taxonomy (Talbi 2020)
  https://hal.inria.fr/hal-02570804/document
• Auto-Fas: Searching Lightweight Networks for Face Anti-Spoofing (Yu et al. 2020; accetped at ICASSP 2020)
  https://ieeexplore.ieee.org/abstract/document/9053587
• Neuro Evolutional with Game-Driven Cultural Algorithms (Waris and Reynolds 2020; accepted at ACM GECCO 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
• NASIL: Neural Architecture Search With Imitation Learning (Fard et al. 2020; accepted at ICASSP 2020)
  https://ieeexplore.ieee.org/document/9054748
• Noisy Differentiable Architecture Search (Chu et al. 2020)
  https://arxiv.org/abs/2005.03566
• AutoSpeech: Neural Architecture Search for Speaker Recognition (Ding et al. 2020)
  https://arxiv.org/abs/2005.03215
• Learning Architectures from an Extended Search Space for Language Modeling (Li et al. 2020)
  https://arxiv.org/abs/2005.02593
• CP-NAS: Child-Parent Neural Architecture Search for 1-bit CNNs ( Zhuo et al. 2020)
  https://arxiv.org/abs/2005.00057
• Particle Swarm Optimization for Evolving Deep Convolutional Neural Networks for Image Classification: Single- and Multi-Objective Approaches (Wang et al. 2020; accepted in book on “Deep Neural Evolution”)
  https://link.springer.com/chapter/10.1007/978-981-15-3685-4_6
• Optimizing Neural Architecture Search using Limited GPU Time in a Dynamic Search Space: A Gene Expression Programming Approach (Alves and de Oliveira. 2020; accepted at IEEE CEC)
  https://arxiv.org/abs/2005.07669
• Local Search is State of the Art for Neural Architecture Search Benchmarks (White et al. 2020; accepted at AutoML workshop at ICML’20)
  https://arxiv.org/abs/2005.02960
• SIPA: A Simple Framework for Efficient Networks (Lee et al. 2020)
  https://arxiv.org/abs/2004.14476
• Neural Architecture Search Based on Model Statistics for Wildlife Identification (Jia et al. 2020; accepted at Journal of the Franklin Institute)
  https://www.sciencedirect.com/science/article/abs/pii/S0016003220302076
• The effect of reduced training in neural architecture search (Kyriakides and Margaritis. 2020; accepted at Neural Comput & Applic)
  https://link.springer.com/article/10.1007%2Fs00521-020-04915-6
• Efficient Evolutionary Neural Architecture Search (NAS) by Modular Inheritable Crossover (Tan et al. 2020; accepted at BIC-TA’20)
  https://link.springer.com/chapter/10.1007%2F978-981-15-3425-6_61
• MobileDets: Searching for Object Detection Architectures for Mobile Accelerators ( Xiong et al. 2020)
  https://arxiv.org/abs/2004.14525
• Angle-based Search Space Shrinking for Neural Architecture Search (Hu et al. 2020)
  https://arxiv.org/abs/2004.13431
• 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
• Deep Multimodal Neural Architecture Search (Yu et al. 2020)
  https://arxiv.org/abs/2004.12070
• Depth-Wise Neural Architecture Search (Jordao et al. 2020)
  https://arxiv.org/abs/2004.11178
• Recurrent Neural Network Architecture Search for Geophyiscal Emulation (Maulik et al. 2020)
  https://arxiv.org/abs/2004.10928
• Local Search is a Remarkably Strong Baseline for Neural Architecture Search (Ottelander et al. 2020)
  https://arxiv.org/abs/2004.08996
• Superkernel Neural Architecture Search for Image Denoising (Mozejko et al. 2020; accepted at NTIRE2020 Workshop at CVPR 2020)
  https://arxiv.org/abs/2004.08870
• 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
• Fitting the Search Space of Weight-sharing NAS with Graph Convolutional Networks (Chen et al. 2020)
  https://arxiv.org/abs/2004.08423
• A Neural Architecture Search based Framework for Liquid State Machine Design (Tian et al. 2020)
  https://arxiv.org/abs/2004.07864
• Geometry-Aware Gradient Algorithms for Neural Architecture Search (Li et al. 2020)
  https://arxiv.org/abs/2004.07802
• Distributed Evolution of Deep Autoencoders (Hajewski et al. 2020)
  https://arxiv.org/abs/2004.07607
• FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions (Wan et al. 2020)
  https://arxiv.org/abs/2004.05565
• ModuleNet: Knowledge-inherited Neural Architecture Search (Chen et al. 2020)
  https://arxiv.org/abs/2004.05020
• Evolutionary recurrent neural network for image captioning (Wang et al. 2020; accepted at Neurocomputing)
  https://www.sciencedirect.com/science/article/abs/pii/S0925231220304744
• Neural Architecture Search for Lightweight Non-Local Networks (Li et al. 2020)
  https://arxiv.org/abs/2004.01961
• A Generic Graph-based Neural Architecture Encoding Scheme for Predictor-based NAS (Ning et al. 2020; accepted at ECCV’20)
  https://arxiv.org/abs/2004.01899
• FedNAS: Federated Deep Learning via Neural Architecture Search (He et al. 2020; accepted at CVPR 2020 Workshop on Neural Architecture Search and Beyond for Representation Learning)
  https://chaoyanghe.com/publications/FedNAS-CVPR2020-NAS.pdf
• Neural architecture search based on model pool for wildlife identification (Jia et al. 2020; accepted at Neurocomputing)
  https://www.sciencedirect.com/science/article/abs/pii/S092523122030388X
• An Evolutionary Approach to Variational Autoencoders (Hajewski and Oliveira. 2020; accepted at CCWC’20)
  https://ieeexplore.ieee.org/abstract/document/9031239
• A Scalable System for Neural Architecture Search (Hajewski and Oliveira. 2020; accepted at CCWC’20)
  https://ieeexplore.ieee.org/abstract/document/9031181
• Neural Architecture Generator Optimization (Ru et al. 2020)
  https://arxiv.org/abs/2004.01395
• Deep-n-Cheap: An Automated Search Framework for Low Complexity Deep Learning (Dey et al. 2020)
  https://arxiv.org/abs/2004.00974
• MTL-NAS: Task-Agnostic Neural Architecture Search towards General-Purpose Multi-Task Learning (Gao et al. 2020; accepted at CVPR’20)
  https://arxiv.org/abs/2003.14058
• Designing Network Design Spaces (Radosavovic et al. 2020; accepted at CVPR’20)
  https://arxiv.org/abs/2003.13678
• Disturbance-immune Weight Sharing for Neural Architecture Search (Niu et al. 2020)
  https://arxiv.org/abs/2003.13089
• NPENAS:Neural Predictor Guided Evolution for Neural Architecture Search (Wei et al. 2020)
  https://arxiv.org/abs/2003.12857
• DA-NAS: Data Adapted Pruning for Efficient Neural Architecture Search (Dai et al. 2020)
  https://arxiv.org/abs/2003.12563
• MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation (He et al. 2020; accepted at CVPR’20)
  https://arxiv.org/abs/2003.12238
• Are Labels Necessary for Neural Architecture Search? (Liu et al. 2020)
  https://arxiv.org/abs/2003.12056
• DCNAS: Densely Connected Neural Architecture Search for Semantic Image Segmentation (Zhang et al. 2020)
  https://arxiv.org/abs/2003.11883
• Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection (Guo et al. 2020; accepted at CVPR 2020)
  https://arxiv.org/abs/2003.11818
• Sampled Training and Node Inheritance for Fast Evolutionary Neural Architecture Search (Zhang et al. 2020)
  https://arxiv.org/abs/2003.11613
• GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernet (You et al. 2020; accepted at CVPR’2020)
  https://arxiv.org/abs/2003.11236
• BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage Models (Yu et al. 2020)
  https://arxiv.org/abs/2003.11142
• Steepest Descent Neural Architecture Optimization: Escaping Local Optimum with Signed Neural Splitting (Wu et al. 2020)
  https://arxiv.org/abs/2003.10392
• BS-NAS: Broadening-and-Shrinking One-Shot NAS with Searchable Numbers of Channels (Shen et al. 2020)
  https://arxiv.org/abs/2003.09821
• Probabilistic Dual Network Architecture Search on Graphs (Zhao et al. 2020)
  https://arxiv.org/abs/2003.09676
• GAN Compression: Efficient Architectures for Interactive Conditional GAN (Li et al. 2020)
  https://arxiv.org/abs/2003.08936
• ElixirNet: Relation-aware Network Architecture Adaptation for Medical Lesion Detection (Jiang et al. 2020)
  https://arxiv.org/abs/2003.08770
• Lifelong Learning with Searchable Extension Units (Wang et al. 2020)
  https://arxiv.org/abs/2003.08559
• Efficient Backbone Search for Scene Text Recognition (Zhang et al. 2020)
  https://arxiv.org/abs/2003.06567
• AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data (Erickson  et al. 2020)
  https://arxiv.org/abs/2003.06505
• PONAS: Progressive One-shot Neural Architecture Search for Very Efficient Deployment (Huang and Chu. 2020)
  https://arxiv.org/abs/2003.05112
• Hierarchical Neural Architecture Search for Single Image Super-Resolution (Guo et al. 2020)
  https://arxiv.org/abs/2003.04619
• 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
• AutoML-Zero: Evolving Machine Learning Algorithms From Scratch (Real et al. 2020)
  https://arxiv.org/abs/2003.03384
• Accelerator-Aware Neural Network Design Using AutoML (Gupta and Akin. 2020; accepted at On-device Intelligence Workshop at MLSys’20)
  https://arxiv.org/abs/2003.02838
• Real-time Federated Evolutionary Neural Architecture Search (Zhu and Jin. 2020)
  https://arxiv.org/abs/2003.02793
• BATS: Binary ArchitecTure Search (Bulat et al. 2020; accepted at ECCV’20)
  https://arxiv.org/abs/2003.01711
• ADWPNAS: Architecture-Driven Weight Prediction for Neural Architecture Search (Zhang et al. 2020)
  https://arxiv.org/abs/2003.01335
• NAS-Count: Counting-by-Density with Neural Architecture Search (Hu et al. 2020)
  https://arxiv.org/abs/2003.00217
• ImmuNetNAS: An Immune-network approach for searching Convolutional Neural Network Architectures (Kefan and Pang. 2020)
  https://arxiv.org/abs/2002.12704
• Neural Inheritance Relation Guided One-Shot Layer Assignment Search (Meng et al. 2020)
  https://arxiv.org/abs/2002.12580
• Automatically Searching for U-Net Image Translator Architecture (Shu and Wang. 2020)
  https://arxiv.org/abs/2002.11581
• AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations (Zhao  et al. 2020)
  https://arxiv.org/abs/2002.11252
• Memory-Efficient Models for Scene Text Recognition via Neural Architecture Search (Hong et al. 2020; accepted at WACV’20 workshop)
  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
• Search for Winograd-Aware Quantized Networks (Fernandez-Marques et al. 2020)
  https://arxiv.org/abs/2002.10711
• Semi-Supervised Neural Architecture Search (Luo et al. 2020)
  https://arxiv.org/abs/2002.10389
• Neural Architecture Search for Compressed Sensing Magnetic Resonance Image Reconstruction (Yan et al. 2020)
  https://arxiv.org/abs/2002.09625
• DSNAS: Direct Neural Architecture Search without Parameter Retraining (Hu et al. 2020)
  https://arxiv.org/abs/2002.09128
• Neural Architecture Search For Fault Diagnosis (Li et al. 2020; accepted at ESREL’20)
  https://arxiv.org/abs/2002.07997
• Learning Architectures for Binary Networks (Kim et al. 2020; accepted at ECCV’20)
  https://arxiv.org/abs/2002.06963
• Efficient Evolutionary Architecture Search for CNN Optimization on GTSRB (Johner and Wassner. 2020; accepted at ICMLA’19)
  https://ieeexplore.ieee.org/abstract/document/8999305/
• Automating Deep Neural Network Model Selection for Edge Inference (Lu et al. 2020; accepted at CogMI’20)
  https://ieeexplore.ieee.org/abstract/document/8998995 
• Neural Architecture Search over Decentralized Data (Xu et al. 2020)
  https://arxiv.org/abs/2002.06352
• Automatic Structural Search for Multi-task Learning VALPs (Garciarena et al. 2020; accepted at OLA’20)
  https://link.springer.com/chapter/10.1007/978-3-030-41913-4_3
• RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning (Alletto et al. 2020; accepted at Meta-Eval 2020 workshop)
  http://eval.how/aaai-2020/REAIS19_p9.pdf
• Classifying the classifier: dissecting the weight space of neural networks (Eilertsen et al. 2020)
  https://arxiv.org/pdf/2002.05688.pdf
• Stabilizing Differentiable Architecture Search via Perturbation-based Regularization (Chen and Hsieh. 2020)
  https://arxiv.org/abs/2002.05283
• Best of Both Worlds: AutoML Codesign of a CNN and its Hardware Accelerator (Abdelfattah et al. 2020; accepted at DAC’20)
  https://arxiv.org/abs/2002.05022
• Variational Depth Search in ResNets (Antoran et al. 2020)
  https://arxiv.org/abs/2002.02797
• Co-Exploration of Neural Architectures and Heterogeneous ASIC Accelerator Designs Targeting Multiple Tasks (Yang et al. 2020; accepted at DAC’20)
  https://arxiv.org/abs/2002.04116
• FPNet: Customized Convolutional Neural Network for FPGA Platforms (Yang et al. 2020; accepted at FPT’20)
  https://ieeexplore.ieee.org/abstract/document/8977837
• AutoFCL: Automatically Tuning Fully Connected Layers for Transfer Learning (Basha et al. 2020)
  https://arxiv.org/abs/2001.11951
• NASS: Optimizing Secure Inference via Neural Architecture Search (Bian et al. 2020; accepted at ECAI’20)
  https://arxiv.org/abs/2001.11854
• Search for Better Students to Learn Distilled Knowledge (Gu et al. 2020)
  https://arxiv.org/abs/2001.11612
• Bayesian Neural Architecture Search using A Training-Free Performance Metric (Camero et al. 2020)
  https://arxiv.org/abs/2001.10726
• NAS-Bench-1Shot1: Benchmarking and Dissecting One-Short Neural Architecture Search (Zela et al. 2020; accepted at ICLR’20)
  https://arxiv.org/abs/2001.10422
• Convolution Neural Network Architecture Learning for Remote Sensing Scene Classification (Chen et al. 2010)
  https://arxiv.org/abs/2001.09614
• Multi-objective Neural Architecture Search via Non-stationary Policy Gradient (Chen et al. 2020)
  https://arxiv.org/abs/2001.08437
• Efficient Neural Architecture Search: A Broad Version (Ding et al. 2020)
  https://arxiv.org/abs/2001.06679
• ENAS U-Net: Evolutionary Neural Architecture Search for Retinal Vessel (Fan et al. 2020)
  https://arxiv.org/abs/2001.06678
• FlexiBO: Cost-Aware Multi-Objective Optimization of Deep Neural Networks (Iqbal et al. 2020)
  https://arxiv.org/abs/2001.06588
• Up to two billion times acceleration of scientific simulations with deep neural architecture search (Kasim et al. 2020)
  https://arxiv.org/abs/2001.08055
• Latency-Aware Differentiable Neural Architecture Search (Xu et al. 2020)
  https://arxiv.org/abs/2001.06392
• MixPath: A Unified Approach for One-shot Neural Architecture Search (Chu et al. 2020)
  https://arxiv.org/abs/2001.05887
• Neural Architecture Search for Skin Lesion Classification (Kwasigroch et al. 2020; accepted at IEEE Access)
  https://ieeexplore.ieee.org/document/8950333
• AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search (Chen et al. 2020)
  https://arxiv.org/abs/2001.04246
• Neural Architecture Search for Deep Image Prior (Ho et al. 2020)
  https://arxiv.org/abs/2001.04776
• Fast Neural Network Adaptation via Parameter Remapping and Architecture Search (Fang et al. 2020; accepted at ICLR’20)
  https://arxiv.org/abs/2001.02525
• FTT-NAS: Discovering Fault-Tolerant Neural Architecture (Li et al. 2020; accepted at ASP-DAC 2020)
  http://nicsefc.ee.tsinghua.edu.cn/media/publications/2020/ASPDAC20_293_6p4Ghq4.pdf
• Deeper Insights into Weight Sharing in Neural Architecture Search (Zhang et al. 2020)
  https://arxiv.org/abs/2001.01431
• EcoNAS: Finding Proxies for Economical Neural Architecture Search (Zhou et al. 2020; accepted at CVPR’20)
  https://arxiv.org/abs/2001.01233
• DeepMaker: A multi-objective optimization framework for deep neural networks in embedded systems (Loni et al. 2020; accepted at Microprocessors and Microsystems)
  https://www.sciencedirect.com/science/article/abs/pii/S0141933119301176 
• Auto-ORVNet: Orientation-boosted Volumetric Neural Architecture Search for 3D Shape Classification (Ma et al. 2020; accepted at IEEE Access)
  https://ieeexplore.ieee.org/abstract/document/8939365
• NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search (Dong and Yang et al. 2020; accepted at ICLR’20)
  https://arxiv.org/abs/2001.00326
• Scalable NAS with Factorizable Architectural Parameters (Wang et al. 2019)
  https://arxiv.org/abs/1912.13256
• Modeling Neural Architecture Search Methods for Deep Networks (Malekhosseini et al. 2019)
  https://arxiv.org/abs/1912.13183
• Searching for Stage-wise Neural Graphs in the Limit (Zhou et al. 2019)
  https://arxiv.org/abs/1912.12860
• Neural Architecture Search on Acoustic Scene Classification (Li et al. 2019; accepted at InterSpeech’20)
  https://arxiv.org/abs/1912.12825
• RC-DARTS: Resource Constrained Differentiable Architecture Search (Jin et al. 2019)
  https://arxiv.org/abs/1912.12814
• NAS Evaluation is frustatingly hard (Yang et al. 2019; accepted at ICLR’20)
  https://arxiv.org/abs/1912.12522
• 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
• BetaNAS: Balanced Training and Selective Drop for Neural Architecture Search (Fang et al. 2019)
  https://arxiv.org/abs/1912.11191
• Progressive DARTS: Bridging the Optimization Gap for NAS in the Wild (Chen et al. 2019)
  https://arxiv.org/abs/1912.10952
• TextNAS: A Neural Architecture Search Space tailored for Text Representation (Wang et al. 2019)
  https://arxiv.org/abs/1912.10729
• AtomNAS: Fine-Grined End-To-End Neural Architecture Search (Mei et al. 2019; accepted at ICLR’20)
  https://arxiv.org/abs/1912.09640
• C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation (Yu et al. 2019)
  https://arxiv.org/abs/1912.09628
• A Reinforcement Neural Architecture Search Method for Rolling Bearing Fault Diagnosis (Wang et al. 2019; accepted at Measurement)
  https://www.sciencedirect.com/science/article/pii/S0263224119312849
• Neural Architecture Search for Optimizing Deep Belief Network Models of fMRI Data (Quiang et al. 2019; accepted at MMMI’19)
  https://link.springer.com/chapter/10.1007/978-3-030-37969-8_4
• QoS-aware Neural Architecture Search (Cheng et al. 2019; accepted at NeurIPS’19)
  http://mlforsystems.org/assets/papers/neurips2019/qosnas_cheng_2019.pdf
• Neural-Hardware Architecture Search (Lin et al. 2019; accepted at NeurIPS’19)
  http://mlforsystems.org/assets/papers/neurips2019/neural_hardware_lin_2019.pdf
• Preventing Information Leakage with Neural Architecture Search (Zhang et al. 2019)
  https://arxiv.org/abs/1912.08421
• Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data (Such et al. 2019)
  https://arxiv.org/abs/1912.07768
• UNAS: Differentiable Architecture Search Meets Reinforcement Learning (Vahdat et al. 2019)
  https://arxiv.org/abs/1912.07651
• 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
• Deep Uncertainty Estimation for Model-based Neural Architecture Search (White et al. 2019; accepted at workshop on Bayesian Deep Learning at NeurIPS’19)
  http://bayesiandeeplearning.org/2019/papers/26.pdf
• A Variational-Sequential Graph Autoencoder for Neural Architecture Performance Prediction (Friede et al. 2019)
  https://arxiv.org/abs/1912.05317
• STEERAGE: Synthesis of Neural Networks Using Architecture Search and Grow-and-Prune Methods (Hassantabar et al. 2019)
  https://arxiv.org/abs/1912.05831
• Leveraging End-to-End Speech Recognition with Neural Architecture Search (Baruwa et al. 2019)
  https://arxiv.org/abs/1912.05946
• Efficient Differentiable Neural Architecture Search with Meta Kernels (Chen et al. 2019)
  https://arxiv.org/abs/1912.04749
• Neural architecture search for image saliency fusion (Bianco et al. 2019; accepted at Information Fusion)
  https://www.sciencedirect.com/science/article/abs/pii/S1566253519302374
• Ultrafast Photorealistic Style Transfer via Neural Architecture Search (An et al. 2019)
  https://arxiv.org/abs/1912.02398
• AdversarialNAS: Adversarial Neural Architecture Search for GANs (Gao et al. 2019)
  https://arxiv.org/abs/1912.02037
• MetAdapt: Meta-Learned Task-Adaptive Architecture for Few-Shot Classification (Doveh et al. 2019)
  https://arxiv.org/abs/1912.00412
• SGAS: Sequential Greedy Architecture Search (Li et al. 2019; accepted at CVPR’20)
  https://arxiv.org/abs/1912.00195
• Blockwisely Supervised Neural Architecture Search with Knowledge Distillation (Li et al. 2019)
  https://arxiv.org/abs/1911.13053
• Towards Oracle Knowledge Distillation with Neural Architecture Search (Kang et al. 2019)
  https://arxiv.org/abs/1911.13019
• AutoML for Architecting Efficient and Specialized Neural Networks (Cai et al. 2019; accepted at IEEE Micro)
  https://ieeexplore.ieee.org/abstract/document/8897011
• Artificial Neural Network and Accelerator Co-design using Evolutionary Algorithms (Colangelo et al. 2019; accepted at HPEC’19)
  https://ieeexplore.ieee.org/abstract/document/8916533
• Auto-creation of Effective Neural Network Architecture by Evolutionary Algorithm and ResNet for Image Classification (Chen et al. 2019; accepted at SMC’19)
  https://ieeexplore.ieee.org/abstract/document/8914267
• Performance Prediction Based on Neural Architecture Features (Long et al. 2019; accepted at CCHI’19)
  https://ieeexplore.ieee.org/abstract/document/8901943
• Fair DARTS: Eliminating Unfair Advantages in Differentiable Architecture Search (Chu et al. 2019; accepted at ECCV’20)
  https://arxiv.org/abs/1911.12126
• EDAS: Efficient and Differentiable Architecture Search (Hong et al. 2019)
  https://arxiv.org/abs/1912.01237
• SGAS: Sequential Greedy Architecture Search (Li et al. 2019)
  https://arxiv.org/abs/1912.00195
• Ranking architectures using meta-learning (Dubatovka et al. 2019)
  https://arxiv.org/abs/1911.11481
• Meta-Learning of Neural Architectures for Few-Shot Learning (Elsken et al. 2019)
  https://arxiv.org/abs/1911.11090
• When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks (Guo et al. 2019)
  https://arxiv.org/abs/1911.10695
• Exploiting Operation Importance for Differentiable Neural Architecture Search (Xie et al. 2019)
  https://arxiv.org/abs/1911.10511
• SM-NAS: Structural-to-Modular Neural Architecture Search for Object Detection (Yao et al. 2019)
  https://arxiv.org/abs/1911.09929
• Multi-Objective Neural Architecture Search via Predictive Network Performance Optimization (Shi et al. 2019)
  https://arxiv.org/abs/1911.09336
• Data Proxy Generation for Fast and Efficient Neural Architecture Search (Park. 2019)
  https://arxiv.org/abs/1911.09322
• AutoShrink: A Topology-aware NAS for Discovering Efficient Neural Architecture (Zhang et al. 2019)
  https://arxiv.org/abs/1911.09251
• Search to Distill: Pearls are Everywhere but not the Eyes (Liu et al. 2019)
  https://arxiv.org/abs/1911.09074
• EfficientDet: Scalable and Efficient Object Detection
  https://arxiv.org/abs/1911.09070
• Periodic Spectral Ergodicity: A Complexity Measure for Deep Neural Networks and Neural Architecture Search (Süzen et al. 2019)
  https://arxiv.org/abs/1911.07831
• IMMUNECS: Neural Committee Search by an Artificial Immune System
  https://arxiv.org/abs/1911.07729
• NAIS: Neural Architecture and Implementation Search and its Applications in Autonomous Driving (Hao et al. 2019)
  https://arxiv.org/abs/1911.07446
• Neural Recurrent Structure Search for Knowledge Graph Embedding (Zhang et al. 2019)
  https://arxiv.org/abs/1911.07132
• S2DNAS: Transforming Static CNN Model for Dynamic Inference via Neural Architecture Search (Yuan et al. 2019)
  https://arxiv.org/abs/1911.07033
• Automatic Design of CNNs via Differentiable Neural Architecture Search for PolSAR Image Classification (Dong et al. 2019)
  https://arxiv.org/abs/1911.06993
• Enhancing Neural Architecture Search with Speciation and Inter-Epoch Crossover (Baughmann and Wozniak. 2019; accepted at Supercomputing’19)
  https://sc19.supercomputing.org/proceedings/src_poster/src_poster_pages/spostg145.html
• RAPDARTS: Resource-Aware Progressive Differentiable Architecture Search (Green et al. 2019)
  https://arxiv.org/abs/1911.05704
• AutoPrune: Automatic Network Pruning by Regularizing Auxiliary Parameters (Xiao et al. 2019; accepted at NeurIPS’19)
  http://papers.nips.cc/paper/9521-autoprune-automatic-network-pruning-by-regularizing-auxiliary-parameters.pdf
• DATA: Differentiable ArchiTecture Approximation (Chang et al. 2019; accepted at NeurIPS’19)
  https://papers.nips.cc/paper/8374-data-differentiable-architecture-approximation.pdf
• Learning to reinforcement learn for Neural Architecture Search (Robles and Vanschoren. 2019)
  https://arxiv.org/pdf/1911.03769.pdf
• An Automated Approach for Developing a Convolutional Neural Network Using a Modified Firefly Algorithm for Image Classification (Sharaf ad Radwan. 2019; accepted book chapter)
  https://link.springer.com/chapter/10.1007/978-981-15-0306-1_5
• ENAS Oriented Layer Adaptive Data Scheduling Strategy for Resource Limited Hardware (Li et al. 2019; accepted at Neurocomputing Journal)
  https://www.sciencedirect.com/science/article/abs/pii/S0925231219315620
• Improved Differentiable Architecture Search for Language Modeling and Named Entity Recognition (Jiang et al. 2019; accepted at EMNLP-IJCNLP’19)
  https://www.aclweb.org/anthology/D19-1367/
• Device-Circuit-Architecture Co-Exploration for Computing-in-Memory Neural Accelerators (Jiang et al. 2019) 
  https://arxiv.org/abs/1911.00139
• On Neural Architecture Search for Resource-Constrained Hardware Platforms (Lu et al. 2020; accepted at ICCAD’19)
  https://arxiv.org/abs/1911.00105 
• NAT: Neural Architecture Transformer for Accurate and Compact Architectures (Guo et al. 2019)
  https://arxiv.org/abs/1910.14488
• Deep neural network architecture search using network morphism (Kwasigroch et al. 2019; accepted MMAR’19)
  https://ieeexplore.ieee.org/abstract/document/8864624
• Person Re-identification with Neural Architecture Search (Zhang et al. 2019; accepted PRCV’19)
  https://link.springer.com/chapter/10.1007/978-3-030-31654-9_46
• Resource Constrained Neural Network Architecture Search: Will a Submodularity Assumption Help? (Xiong et al. 2019; accepted at ICCV’19)
  http://openaccess.thecvf.com/content_ICCV_2019/papers/Xiong_Resource_Constrained_Neural_Network_Architecture_Search_Will_a_Submodularity_Assumption_ICCV_2019_paper.pdf
• Auto-FPN: Automatic Network Architecture Adaptation for Object Detection Beyond Classification (Xu et al. 2019; accepted at ICCV’19)
  http://openaccess.thecvf.com/content_ICCV_2019/papers/Xu_Auto-FPN_Automatic_Network_Architecture_Adaptation_for_Object_Detection_Beyond_Classification_ICCV_2019_paper.pdf
• BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search (White et al. 2019)
  https://arxiv.org/abs/1910.11858
• Stabilizing DARTS with Amended Gradient Estimation on Architectural Parameters (Bi et al. 2019)
  https://arxiv.org/abs/1910.11831
• 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
• Hardware-aware one-short Neural Architecture Search in Coordinate Ascent Framework
  https://arxiv.org/abs/1910.11609
• Efficient Structured Pruning and Architecture Searching for Group Convolution (Zhao and Luk. 2019; accepted at ICCV’19 workshop)
  http://openaccess.thecvf.com/content_ICCVW_2019/papers/NeurArch/Zhao_Efficient_Structured_Pruning_and_Architecture_Searching_for_Group_Convolution_ICCVW_2019_paper.pdf 
• On-Device Image Classification with Proxyless Neural Architecture Search and Quantization-Aware Fine-tuning (Cai et al. 2019; accepted at ICCV’19 workshop)
  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
• MSNet: Structural Wired Neural Architecture Search for Internet of Things (Cheng et al. 2019; accepted at ICCV’19 workshop)
  http://openaccess.thecvf.com/content_ICCVW_2019/papers/NeurArch/Cheng_MSNet_Structural_Wired_Neural_Architecture_Search_for_Internet_of_Things_ICCVW_2019_paper.pdf
• Efficient Decoupled Neural Architecture Search by Structure and Operation Sampling (Lee et al. 2019)
  https://arxiv.org/abs/1910.10397
• Using Neural Architecture Search to Optimize Neural Networks for Embedded Devices (Cassimon et al. 2019; accepted at 3PGCIC’19)
  https://link.springer.com/chapter/10.1007/978-3-030-33509-0_64 
• NASIB: Neural Architecture Search withIn Budget (Singh et al. 2019)
  https://arxiv.org/abs/1910.08665
• State of Compact Architecture Search For Deep Neural Networks (Shafiee et al. 2019)
  https://arxiv.org/abs/1910.06466
• One-Shot Neural Architecture Search via Self-Evaluated Template Network (Dong and Yang. 2019)
  https://arxiv.org/abs/1910.05733
• Scalable Neural Architecture Search for 3D Medical Image Segmentation (Kim et al. 2019; accepted at MICCAI’19)
  https://arxiv.org/abs/1906.05956
• Neural Architecture Search for Adversarial Medical Image Segmentation (Dong et al. 2019; accepted at MICCAI’19)
  https://link.springer.com/chapter/10.1007/978-3-030-32226-7_92
• Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation (Yang et al. 2019; accepted at MICCAI’19)
  https://link.springer.com/chapter/10.1007/978-3-030-32245-8_1
• Identify Hierarchical Structures from Task-Based fMRI Data via Hybrid Spatiotemporal Neural Architecture Search Net (Zhang et al. 2019; accepted at MICCAI’19)
  https://link.springer.com/chapter/10.1007/978-3-030-32248-9_83
• Energy-aware Neural Architecture Optimization with Fast Splitting Steepest Descent (Wang et al. 2019; accepted EMC2 workshop’19)
  https://arxiv.org/abs/1910.03103
  https://www.emc2-workshop.com/assets/docs/neurips-19/emc2-neurips19-paper-39.pdf
• Improving one-shot NAS by Surppressing the Posterior Fading (Li et al. 2019)
  https://arxiv.org/abs/1910.02543
• Splitting Steepest Descent for Growing Neural Architectures (Liu et al. 2019)
  https://arxiv.org/abs/1910.02366
• A Novel Automatic CNN Architecture Design Approach Based on Genetic Algorithm (Ahmed et al. 2019; accepted at AISI’19)
  https://link.springer.com/chapter/10.1007/978-3-030-31129-2_43
• RNAS: Architecture Ranking for Powerful Networks (Xu et al. 2019)
  https://arxiv.org/abs/1910.01523
• Towards Unifying Neural Architecture Space Exploration and Generalization (Bhardwaj and Marculescu)
  https://arxiv.org/abs/1910.00780
• Sub-Architecture Ensemble Pruning in Neural Architecture Search (Bia et al. 2019)
  https://arxiv.org/abs/1910.00370
• Towards modular and programmable architecture search (Negrinho et al. 2019; accepted at NeurIPS’19)
  https://arxiv.org/abs/1909.13404
• Automated design of error-resilient and hardware-efficient deep neural networks (Schorn et al. 2019)
  https://arxiv.org/abs/1909.13844
• STACNAS: Towards Stable and Consistent Optimization for Differentiable Neural Architecture Search (Guilin et al. 2019)
  https://arxiv.org/abs/1909.11926
• Efficient Residual Dense Block Search for Image Super-Resolution (Song et al. 2019)
  https://arxiv.org/abs/1909.11409
• Understanding and Improving One-shot Neural Architecture Optimization (Luo et al. 2019)
  https://arxiv.org/abs/1909.10815
• Scheduled Differentiable Architecture Search for Visual Recognition (Qui et al. 2019)
  https://arxiv.org/abs/1909.10236
• Understanding and Robustifying Differentiable Architecture Search (Zela et al. 2019; accepted at ICLR’20) 
  https://arxiv.org/abs/1909.09656
• Genetic Neural Architecture Search for automatic assessment of human sperm images (Miahi et al. 2019)
  https://arxiv.org/abs/1909.09432
• IR-NAS: Neural Architecture Search for Image Restoration (Zhang et al. 2019)
  https://arxiv.org/abs/1909.08228
• Pose Neural Fabrics Search (Yang et al. 2019)
  https://arxiv.org/abs/1909.07068
• SegNAS3D: Network Architecture Search with Derivative-Free Global Optimization for 3D Image Segmentation (Wong and Moradi. 2019)
  https://arxiv.org/abs/1909.05962
• DARTS+: Improved Differentiable Architecture Search with Early Stopping (Liang et al. 2019)
  https://arxiv.org/abs/1909.06035
• Searching for Accurate Binary Neural Architectures (Shen et al. 2019; accepted at ICCV’19 Neural Architects workshop)
  https://arxiv.org/abs/1909.07378
• Improving Keyword Spotting and Language Identification via Neural Architecture Search at Scale (Mazzawi et al. 2019; accepted at INTERSPEECH 2019)
  https://www.isca-speech.org/archive/Interspeech_2019/pdfs/1916.pdf
• Neural Architecture Search for Class-incremental Learning (Huang et al. 2019)
  https://arxiv.org/abs/1909.06686
• Graph-guided Architecture Search for Real-time Semantic Segmentation (Lin et al. 2019)
  https://arxiv.org/abs/1909.06793
• CARS: Continuous Evolution for Efficient Neural Architecture Search (Yang et al. 2019; accepted at CVPR’20)
  https://arxiv.org/abs/1909.04977
• Bayesian Optimization of Neural Architectures for Human Activity Recognition (Osmani and Hamidi. 2019; accepted at Human Activity Sensing)
  https://link.springer.com/chapter/10.1007/978-3-030-13001-5_12
• Compute-Efficient Neural Network Architecture Optimization by a Genetic Algorithm (Litzinger et al. 2019; accepted at ICANN’19)
  https://link.springer.com/chapter/10.1007/978-3-030-30484-3_32
• Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study (Faes et al. 2019; accepted at The Lancet Digital Health)
  https://www.sciencedirect.com/science/article/pii/S2589750019301086
• A greedy constructive algorithm for the optimization of neural network architectures (Pasini et al. 2019)
  https://arxiv.org/abs/1909.03306 
• Differentiable Mask Pruning for Neural Networks (Ramakrishnan et al. 2019)
  https://arxiv.org/abs/1909.04567
• Neural Architecture Search in Embedding Space (Liu. 2019)
  https://arxiv.org/abs/1909.03615
• Auto-GNN: Neural Architecture Search of Graph Neural Networks (Zhou et al. 2019)
  https://arxiv.org/abs/1909.03184
• Best Practices for Scientific Research on Neural Architecture Search (Lindauer and Hutter. 2019)
  https://arxiv.org/abs/1909.02453
• Efficient Neural Architecture Transformation Search in Channel-Level for Object Detection (Peng et al. 2019)
  https://arxiv.org/abs/1909.02293
• Training compact neural networks via auxiliary overparameterization (Liu et al. 2019)
  https://arxiv.org/abs/1909.02214
• Rethinking the Number of Channels for Convolutional Neural Networks (Zhu et al. 2019)
  https://arxiv.org/abs/1909.01861
• MANAS: Multi-Agent Neural Architecture Search (Carlucci et al. 2019)
  https://arxiv.org/abs/1909.01051
• Resource Optimized Neural Architecture Search for 3D Medical Image Segmentation (Bae et al. 2019; accepted at MICCAI’19)
  https://arxiv.org/abs/1909.00548
• Neural Architecture Search for Joint Optimization of Predictive Power and Biological Knowledge (Zhang et al. 2019)
  https://arxiv.org/abs/1909.00337
• Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research (Balaprakash et al. 2019; accepted at SC’19)
  https://arxiv.org/abs/1909.00311
• Automatic Neural Network Search Method for Open Set Recognition (Sun et al. 2019; accepted at ICIP’19)
  https://ieeexplore.ieee.org/abstract/document/8803605
• HM-NAS: Efficient Neural Architecture Search via Hierarchical Masking (Yan et al. 2019; accepted at ICCV’19 Neural Architects Workshop)
  https://arxiv.org/abs/1909.00122
• Once for All: Train One Network and Specialize it for Efficient Deployment (Cai et al. 2019)
  https://arxiv.org/abs/1908.09791
• Refactoring Neural Networks for Verification (Shriver et al. 2019)
  https://arxiv.org/abs/1908.08026
• CNASV: A Convolutional Neural Architecture Search-Train Prototype for Computer Vision Task (Zhou and Yang. 2019; accepted at CollaborateCom’19)
• Automatic Design of Deep Networks with Neural Blocks (Zhong et al. 2019; accepted at Cognitive Computation)
  https://link.springer.com/article/10.1007/s12559-019-09677-5
• Differentiable Learning-to-Group Channels via Groupable Convolutional Neural Networks (Zhang et al. 2019)
  https://arxiv.org/abs/1908.05867
• SCARLET-NAS: Bridging the gap Between Scalability and Fairness in Neural Architecture Search (Chu et al. 2019)
  https://arxiv.org/abs/1908.06022
• A Novel Encoding Scheme for Complex Neural Architecture Search (Ahmad et al. 2019; accepted at ITC-CSCC)
  https://ieeexplore.ieee.org/document/8793329
• A Graph-Based Encoding for Evolutionary Convolutional Neural Network Architecture Design (Irwin-Harris et al. 2019; accepted CEC’19)
  https://ieeexplore.ieee.org/document/8790093
• A Novel Framework for Neural Architecture Search in the Hill Climbing Domain (Verma et al. 2019; accepted at AIKE’19)
  https://ieeexplore.ieee.org/abstract/document/8791709
• Automated Neural Network Construction with Similarity Sensitive Evolutionary
  Algorithms (Tian et al. 2019)
  http://rvc.eng.miami.edu/Paper/2019/IRI19_EA.pdf
• AutoGAN: Neural Architecture Search for Generative Adversarial Networks (Gong et al. 2019; accepted at ICCV’19)
  https://arxiv.org/abs/1908.03835
• Refining the Structure of Neural Networks Using Matrix Conditioning (Yousefzadeh and O’Leary. 2019)
  https://arxiv.org/abs/1908.02400
• SqueezeNAS: Fast neural architecture search for faster semantic segmentation (Shaw et al. 2019)
  https://arxiv.org/abs/1908.01748
• MoGA: Searching Beyond MobileNetV3 (Chu et al. 2019; accepted at ICASSP’20)
  https://arxiv.org/abs/1908.01314
• Evolving deep neural networks by multi-objective particle swarm optimization for image classification (Wang et al. 2019; accepted at GECCO’19)
  https://arxiv.org/abs/1904.09035
• Particle Swarm Optimisation for Evolving Deep Neural Networks for Image Classification by Evolving and Stacking Transferable Blocks (Wang et al. 2019; accepted at IEEE CEC’20)
  https://arxiv.org/abs/1907.12659
• Self-Adaptive 2D-3D Ensemble of Fully Convolutional Networks for Medical Image Segmentation (Calisto and Lai-Yuen. 2019; accepted at SPIE Medical Imaging’20)
  https://arxiv.org/abs/1907.11587
• MemNet: Memory-Efficiency Guided Neural Architecture Search with Augment-Trim learning (by Liu et al. 2019)
  https://arxiv.org/abs/1907.09569 
• Efficient Novelty-Driven Neural Architecture Search (Zhang et al. 2019)
  https://arxiv.org/abs/1907.09109
• PC-DARTS: Partial Channel Connections for Memory-Efficient Differentiable Architecture Search (Xu et al. 2019)
  https://arxiv.org/abs/1907.05737
• Hardware/Software Co-Exploration of Neural Architectures (Jiang et al. 2019)
  https://arxiv.org/abs/1907.04650
• EPNAS: Efficient Progressive Neural Architecture Search (Zhou et al. 2019)
  https://arxiv.org/abs/1907.04648
• Video Action Recognition via Neural Architecture Searching (Peng et al. 2019)
  https://arxiv.org/abs/1907.04632
• Hardware/Software Co-Exploration of Neural Architectures (Jiang et al. 2019; accepted at ASP-DAC’20)
  https://arxiv.org/abs/1907.04650
• When Neural Architecture Search Meets Hardware Implementation: from Hardware Awareness to Co-Design (Zhang et al. 2019; accepted at ISVLSI’19)
  https://ieeexplore.ieee.org/document/8839421
• Reinforcement Learning for Neural Architecture Search: A Review (Jaafra et al. 2019 accepted at Image and Vision Computing)
  https://www.sciencedirect.com/science/article/pii/S026288561930088
• Architecture Search for Image Inpainting (Li and King. 2019. accepted at International Symposium on Neural Networks)
  https://link.springer.com/chapter/10.1007/978-3-030-22796-8_12
• Neural Network Architecture Search with Differentiable Cartesian Genetic Programming for Regression (Märtens and Izzo. 2019)
  https://arxiv.org/abs/1907.01939
• FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural Architecture Search (Chu et al. 2019)
  https://arxiv.org/abs/1907.01845
• HyperNOMAD: Hyperparameter optimization of deep neural networks using mesh adaptive direct search (Lakhmiri et al. 2019)
  https://arxiv.org/pdf/1907.01698.pdf
• Evolving Robust Neural Architectures to Defend from Adversarial Attacks (Vargas and Kotyan. 2019)
  https://arxiv.org/abs/1906.11667
• Surrogate-Assisted Evolutionary Deep Learning Using an End-to-End Random Forest-based Performance Predictor (Sun et al. 2019; accepted by IEEE Transactions on Evolutionary Computation)
  https://ieeexplore.ieee.org/document/8744404
• Adaptive Genomic Evolution of Neural Network Topologies (AGENT) for State-to-Action Mapping in Autonomous Agents (Behjat et al. 2019; accepted and presented in ICRA 2019)
  https://arxiv.org/abs/1903.07107
• Densely Connected Search Space for More Flexible Neural Architecture Search (Fang et al. 2019)
  https://arxiv.org/abs/1906.09607
• Posterior-Guided Neural Architecture Search (Zhou et al. 2020; accepted at AAAI)
  https://arxiv.org/abs/1906.09557
• SwiftNet: Using Graph Propagation as Meta-knowledge to Search Highly Representative Neural Architectures (Cheng et al. 2019)
  https://arxiv.org/abs/1906.08305
• Transfer NAS: Knowledge Transfer between Search Spaces with Transformer Agents (Borsos et al. 2019)
  https://arxiv.org/abs/1906.08102
• XNAS: Neural Architecture Search with Expert Advice (Nayman et al. 2019; accepted at NeurIPS’19)
  https://arxiv.org/abs/1906.08031
• A Study of the Learning Progress in Neural Architecture Search Techniques (Singh et al. 2019)
  https://arxiv.org/abs/1906.07590
• Hardware aware Neural Network Architectures (using FBNet) (Srinivas et al. 2019)
  https://arxiv.org/abs/1906.07214
• Sample-Efficient Neural Architecture Search by Learning Action Space (Wang et al. 2019)
  https://arxiv.org/abs/1906.06832
• SwiftNet: Using Graph Propagation as Meta-knowledge to Search Highly Representative Neural Architectures (Cheng et al. 2019)
  https://arxiv.org/abs/1906.08305
• Automatic Modulation Recognition Using Neural Architecture Search (Wei et al. 2019; accepted High Performance Big Data and Intelligent Systems)
  https://ieeexplore.ieee.org/abstract/document/8735458
• Continual and Multi-Task Architecture Search (Pasunuru and Bansal. 2019)
  https://arxiv.org/abs/1906.05226
• AutoGrow: Automatic Layer Growing in Deep Convolutional Networks (Wen et al. 2019)
  https://arxiv.org/abs/1906.02909
• One-Short Neural Architecture Search via Compressing Sensing (Cho et al. 2019)
  https://arxiv.org/abs/1906.02869
• V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation (Zhu et al. 2019)
  https://arxiv.org/abs/1906.02817
• StyleNAS: An Empirical Study of Neural Architecture Search to Uncover Surprisingly Fast End-to-End Universal Style Transfer Networks (An et al. 2019)
  https://arxiv.org/abs/1906.02470
• Efficient Forward Architecture Search (Hu et al. 2019; accepted at NeurIPS’19)
  https://arxiv.org/abs/1905.13360
• Differentiable Neural Architecture Search via Proximal Iterations (Yao et al. 2019)
  https://arxiv.org/abs/1905.13577
• Dynamic Distribution Pruning for Efficient Network Architecture Search (Zheng et al. 2019)
  https://arxiv.org/abs/1905.13543
• Particle swarm optimization of deep neural networks architectures for image classification (Fernandes Junior and Yen. 2019. accepted at Swarm and Evolutionary Computation)
  https://www.sciencedirect.com/science/article/abs/pii/S2210650218309246
• On Network Design Spaces for Visual Recognition (Radosavovic et al. 2019; accepted at ICCV’20)
  https://arxiv.org/abs/1905.13214
• AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures (Ryoo et al. 2019)
  https://arxiv.org/abs/1905.13209
• EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (Tan and Le, accepted at ICML’19. 2019)
  http://proceedings.mlr.press/v97/tan19a/tan19a.pdf
• Structure Learning for Neural Module Networks (Pahuja et al. 2019)
  https://arxiv.org/abs/1905.11532
• SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers (Fedorov et al. 2019; accepted at NeurIPS’19)
  https://arxiv.org/abs/1905.12107
• Network Pruning via Transformable Architecture Search (Dong and Yang. 2019; accepted at NeurIPS’19)
  https://arxiv.org/abs/1905.09717
• DEEP-BO for Hyperparameter Optimization of Deep Networks (Cho et al. 2019)
  https://arxiv.org/abs/1905.09680
• Constrained Design of Deep Iris Networks (Nguyen et al. 2019)
  https://arxiv.org/abs/1905.09481
• Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search (Akimoto et al. 2019; accepted at ICML’19)
  https://arxiv.org/abs/1905.08537
• Multinomial Distribution Learning for Effective Neural Architecture Search (Zheng et al. 2019)
  https://arxiv.org/abs/1905.07529
• EENA: Efficient Evolution of Neural Architecture (Zhu et al. 2019; accepted at ICCV’19 Neural Architects Workshop)
  https://arxiv.org/abs/1905.07320
• DeepSwarm: Optimising Convolutional Neural Networks using Swarm Intelligence (Byla and Pang. 2019)
  https://arxiv.org/abs/1905.07350
• AutoDispNet: Improving Disparity Estimation with AutoML (Saikia et al. 2019)
  https://arxiv.org/abs/1905.07443
• Online Hyper-parameter Learning for Auto-Augmentation Strategy (Lin et al. 2019)
  https://arxiv.org/abs/1905.07373
• Regularized Evolutionary Algorithm for Dynamic Neural Topology Search (Saltori et al. 2019)
  https://arxiv.org/abs/1905.06252
• Deep Neural Architecture Search with Deep Graph Bayesian Optimization (Ma et al. 2019)
  https://arxiv.org/abs/1905.06159
• Automatic Model Selection for Neural Networks (Laredo et al. 2019)
  https://arxiv.org/abs/1905.06010
• Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization (Klein and Hutter. 2019)
  https://arxiv.org/abs/1905.04970
• BayesNAS: A Bayesian Approach for Neural Architecture Search (Zhou et al. 2019, accepted at ICML’19)
  https://arxiv.org/abs/1905.04919
• Single-Path NAS: Device-Aware Efficient ConvNet Design (Stamoulis et al. 2019)
  https://arxiv.org/abs/1905.04159
• Automatic Design of Artificial Neural Networks for Gamma-Ray Detection (Assuncao et al. 2019)
  https://arxiv.org/abs/1905.03532
• Neural Architecture Refinement: A Practical Way for Avoiding Overfitting in NAS (Jiang et al. 2019)
  https://arxiv.org/abs/1905.02341
• Fast and Reliable Architecture Selection for Convolutional Neural Networks (Hahn et al. 2019)
  https://arxiv.org/abs/1905.01924
• Differentiable Architecture Search with Ensemble Gumbel-Softmax (Chang et al. 2019)
  https://arxiv.org/abs/1905.01786
• Searching for A Robust Neural Architecture in Four GPU Hours (Dong and Yang 2019, accepted at CVPR’19)
  https://xuanyidong.com/publication/cvpr-2019-gradient-based-diff-sampler/
• Evolving unsupervised deep neural networks for learning meaningful representations (Sun et al. 2019, accepted by IEEE Transactions on Evolutionary Computation)
  https://arxiv.org/abs/1712.05043
• Evolving Deep Convolutional Neural Networks for Image Classification (Sun et al. 2019, accepted by IEEE Transactions on Evolutionary Computation)
  https://arxiv.org/abs/1710.10741
• AdaResU-Net: Multiobjective Adaptive Convolutional Neural Network for Medical Image Segmentation (Baldeon-Calisto and Lai-Yuen. 2019.; accepted at Neurocomputing)
  https://www.sciencedirect.com/science/article/pii/S0925231219304679
• Automatic Design of Convolutional Neural Network for Hyperspectral Image Classification (Chen et al. 2019; accepted at IEEE Transactions on Geoscience and Remote Sensing)
  https://ieeexplore.ieee.org/abstract/document/8703410
• Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation (Chen et al. 2019)
  https://arxiv.org/abs/1904.12760
• Design Automation for Efficient Deep Learning Computing (Han et al. 2019)
  https://arxiv.org/abs/1904.10616
• CascadeML: An Automatic Neural Network Architecture Evolution and Training Algorithm for Multi-label Classification (Pakrashi and Namee 2019)
  https://arxiv.org/abs/1904.10551
• GraphNAS: Graph Neural Architecture Search with Reinforcement Learning (Gao et al. 2019)
  https://arxiv.org/abs/1904.09981
• Neural Architecture Search for Deep Face Recognition (Zhu. 2019)
  https://arxiv.org/abs/1904.09523
• Efficient Neural Architecture Search on Low-Dimensional
  Data for OCT Image Segmentation (Gessert and Schlaefer. 2019)
  https://openreview.net/forum?id=Syg3FDjntN
• NAS-Unet: Neural Architecture Search for Medical Image Segmentation (Weng et al. 2019; accepted at IEEE Access)
  https://ieeexplore.ieee.org/document/8681706
• Fast DENSER: Efficient Deep NeuroEvolution (Assunção et al. 2019; accepted at ECGP’19)
  https://link.springer.com/chapter/10.1007/978-3-030-16670-0_13
• NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection (Ghaisi et al. 2019; accepted at CVPR’19)
  https://arxiv.org/abs/1904.07392
• Automated Search for Configurations of Deep Neural Network Architectures (Ghamizi et al. 2019; accepted at SPLC’19)
  https://arxiv.org/abs/1904.04612
• WeNet: Weighted Networks for Recurrent Network Architecture Search (Huang and Xiang. 2019)
  https://arxiv.org/abs/1904.03819
• Resource Constrained Neural Network Architecture Search (Xiong et al. 2019)
  https://arxiv.org/abs/1904.03786
• Size/Accuracy Trade-Off in Convolutional Neural Networks: An Evolutionary Approach (Cetto et al. 2019; accepted at INNSBDDL) 
  https://link.springer.com/chapter/10.1007/978-3-030-16841-4_3
• ASAP: Architecture Search, Anneal and Prune (Noy et al. 2019)
  https://arxiv.org/abs/1904.04123
• Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours (Stamoulis et al. 2019)
  https://arxiv.org/abs/1904.02877
• Architecture Search of Dynamic Cells for Semantic Video Segmentation (Nekrasov et al. 2019)
  https://arxiv.org/abs/1904.02371
• Template-Based Automatic Search of Compact Semantic Segmentation Architectures (Nekrasov et al. 2019)
  https://arxiv.org/abs/1904.02365
• Exploring Randomly Wired Neural Networks for Image Recognition (Xie et al. 2019)
  https://arxiv.org/abs/1904.01569
• Understanding Neural Architecture Search Techniques (Adam and Lorraine 2019)
  https://arxiv.org/abs/1904.00438
• Automatic Convolutional Neural Architecture Search for Image Classification Under Different Scenes (Weng et al. 2019; accepted for IEEE Access)
  https://ieeexplore.ieee.org/document/8676019
• Single Path One-Shot Neural Architecture Search with Uniform Sampling (Guo et al. 2019)
  https://arxiv.org/abs/1904.00420
• Network Slimming by Slimmable Networks: Towards One-Shot Architecture Search for Channel Numbers (Yu and Huang 2019)
  https://arxiv.org/abs/1903.11728
• sharpDARTS: Faster and More Accurate Differentiable Architecture Search (Hundt et al. 2019)
  https://arxiv.org/abs/1903.09900
• DetNAS: Neural Architecture Search on Object Detection (Chen et al. 2019; accepted at NeurIPS’19)
  https://arxiv.org/abs/1903.10979
• Evolution of Deep Convolutional Neural Networks Using Cartesian Genetic Programming (Suganuma et al. 2019; accepted at Evolutionary Computation)
  https://www.mitpressjournals.org/doi/abs/10.1162/evco_a_00253
• Deep Evolutionary Networks with Expedited Genetic Algorithm for Medical Image Denoising (Liu et al. 2019; accepted at Medical Image Analysis)
  https://www.sciencedirect.com/science/article/abs/pii/S1361841518307734
• Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly (Kandasamy et al. 2019)
  https://arxiv.org/abs/1903.06694
• AttoNets: Compact and Efficient Deep Neural Networks for the Edge via Human-Machine Collaborative Design (Wong et al. 2019)
  https://arxiv.org/abs/1903.07209
• Improving Neural Architecture Search Image Classifiers via Ensemble Learning (Macko et al. 2019)
  https://arxiv.org/abs/1903.06236
• Software-Defined Design Space Exploration for an Efficient AI Accelerator Architecture (Yu et al. 2019)
  https://arxiv.org/abs/1903.07676
• MFAS: Multimodal Fusion Architecture Search (Pérez-Rúa et al. 2019; accepted at CVPR’19)
  https://hal.archives-ouvertes.fr/hal-02068293/document
• A Hybrid GA-PSO Method for Evolving Architecture and Short Connections of Deep Convolutional Neural Networks (Wang et al. 2019; accepted at PRICAI’19)
  https://arxiv.org/abs/1903.03893
• Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search (Li et al. 2019)
  https://arxiv.org/abs/1903.03777
• Inductive Transfer for Neural Architecture Optimization (Wistuba and Pedapati 2019)
  https://arxiv.org/abs/1903.03536
• Evolutionary Cell Aided Design for Neural Network (Colangelo et al. 2019)
  https://arxiv.org/abs/1903.02130
• Automated Architecture-Modeling for Convolutional Neural Networks (Duong 2019)
  https://btw.informatik.uni-rostock.de/download/workshopband/D1-1.pdf
• Learning Implicitly Recurrent CNNs Through Parameter Sharing (Savarese and Maire, accepted at ICLR’19)
  https://arxiv.org/abs/1902.09701
• Evaluating the Search Phase of Neural Architecture Searc (Sciuto et al. 2019)
  https://arxiv.org/abs/1902.08142
• Random Search and Reproducibility for Neural Architecture Search (Li and Talwalkar 2019)
  https://arxiv.org/abs/1902.07638
• Evolutionary Neural AutoML for Deep Learning (Liang et al. 2019)
  https://arxiv.org/abs/1902.06827
• Fast Task-Aware Architecture Inference (Kokiopoulou et al. 2019)
  https://arxiv.org/abs/1902.05781
• Probabilistic Neural Architecture Search (Casale et al. 2019)
  https://arxiv.org/abs/1902.05116
• Investigating Recurrent Neural Network Memory Structures using Neuro-Evolution (Ororbia et al. 2019)
  https://arxiv.org/abs/1902.02390
• Accuracy vs. Efficiency: Achieving Both through FPGA-Implementation Aware Neural Architecture Search (Jiang et al. 2019; accepted at DAC’19)
  https://arxiv.org/abs/1901.11211
• The Evolved Transformer (So et al. 2019)
  https://arxiv.org/abs/1901.11117
• Designing neural networks through neuroevolution (Stanley et al. 2019; accepted at Nature Machine Intelligence)
  https://www.nature.com/articles/s42256-018-0006-z
• NeuNetS: An Automated Synthesis Engine for Neural Network Design (Sood et al. 2019)
  https://arxiv.org/abs/1901.06261
• Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search (Chu et al. 2019; accepted at ICPR’20)
  https://arxiv.org/abs/1901.07261
• EAT-NAS: Elastic Architecture Transfer for Accelerating Large-scale Neural Architecture Search (Fang et al. 2019)
  https://arxiv.org/abs/1901.05884
• Bayesian Learning of Neural Network Architectures (Dikov et al. 2019)
  https://arxiv.org/abs/1901.04436
• Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation (Liu et al. 2019; accepted at CVPR’19)
  https://arxiv.org/abs/1901.02985
• The Art of Getting Deep Neural Networks in Shape (Mammadli et al. 2019; accepted at TACO Journal)
  https://dl.acm.org/citation.cfm?id=3291053
• Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search (Chu et al. 2019)
  https://arxiv.org/abs/1901.01074
• A particle swarm optimization-based flexible convolutional auto-encoder for image classification (Sun et al. 2018, published by IEEE Transactions on Neural Networks and Learning Systems)
  https://arxiv.org/abs/1712.05042
• SNAS: Stochastic Neural Architecture Search (Xie et al. 2018; accepted at ICLR’19)
  https://arxiv.org/abs/1812.09926
• Graph Hypernetworks for Neural Architecture Search (Zhang et al. 2018; Accepted at ICLR’19)
  https://arxiv.org/abs/1810.05749
• Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution (Elsken et al. 2018; accepted at ICLR’19)
  https://arxiv.org/abs/1804.09081
• Macro Neural Architecture Search Revisited (Hu et al. 2018; accepted at Meta-Learn NeurIPS workshop’18)
  http://metalearning.ml/2018/papers/metalearn2018_paper16.pdf
• AMLA: an AutoML frAmework for Neural Network Design (Kamath et al. 2018; at ICML AutoML workshop)
  http://pkamath.com/publications/papers/amla_automl18.pdf
• ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation (Dai et al. 2018)
  https://arxiv.org/abs/1812.08934
• Neural Architecture Search Over a Graph Search Space (de Laroussilhe et al. 2018)
  https://arxiv.org/abs/1812.10666
• A Review of Meta-Reinforcement Learning for Deep Neural Networks Architecture Search (Jaafra et al. 2018)
  https://arxiv.org/abs/1812.07995
• Evolutionary Neural Architecture Search for Image Restoration (van Wyk and Bosman 2018)
  https://arxiv.org/abs/1812.05866
• IRLAS: Inverse Reinforcement Learning for Architecture Search (Guo et al. 2018; accepted at CVPR’19)
  https://arxiv.org/abs/1812.05285
• FBNet: Hardware-Aware Efficient ConvNet Designvia Differentiable Neural Architecture Search (Wu et al. 2018; accepted at CVPR’19)
  https://arxiv.org/abs/1812.03443
• ShuffleNASNets: Efficient CNN models throughmodified Efficient Neural Architecture Search (Laube et al. 2018)
  https://arxiv.org/abs/1812.02975
• ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware (Cai et al. 2018; accepted at ICLR’19)
  https://arxiv.org/abs/1812.00332
• Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search (Wu et al. 2018)
  https://arxiv.org/abs/1812.00090
• Evolving Deep Convolutional Neural Networks by Variable-length Particle Swarm Optimization for Image Classification (Wang et al. 2018; accepted at CEC’18)
  https://arxiv.org/abs/1803.06492
• A Hybrid Differential Evolution Approach to Designing Deep Convolutional Neural Networks for Image Classification (Wang et al. 2018; accepted AI’18)
  https://arxiv.org/abs/1808.06661
• TEA-DNN: the Quest for Time-Energy-Accuracy Co-optimized Deep Neural Networks (Cai et al. 2018)
  https://arxiv.org/abs/1811.12065
• Evolving Space-Time Neural Architectures for Videos (Piergiovanni et al. 2018; accepted at ICCV’19)
  https://arxiv.org/abs/1811.10636
• InstaNAS: Instance-aware Neural Architecture Search (Cheng et al. 2018)
  https://arxiv.org/abs/1811.10201
• Evolutionary-Neural Hybrid Agents for Architecture Search (Maziarz et al. 2018; accepted at  ICML’19 workshop on AutoML)
  https://arxiv.org/abs/1811.09828
• Joint Neural Architecture Search and Quantization (Chen et al. 2018)
  https://arxiv.org/abs/1811.09426
• Transfer Learning with Neural AutoML (Wong et al. 2018; accepted at NeurIPS’18)
  http://papers.nips.cc/paper/8056-transfer-learning-with-neural-automl.pdf
• Evolving Image Classification Architectures with Enhanced Particle Swarm Optimisation (Fielding and Zhang 2018)
  https://ieeexplore.ieee.org/document/8533601
• Deep Active Learning with a Neural Architecture Search (Geifman and El-Yaniv 2018; accepted at NeurIPS’19)
  https://arxiv.org/abs/1811.07579
• Stochastic Adaptive Neural Architecture Search for Keyword Spotting (Véniat et al. 2018)
  https://arxiv.org/abs/1811.06753
• NSGA-NET: A Multi-Objective Genetic Algorithm for Neural Architecture Search (Lu et al. 2018)
  https://arxiv.org/abs/1810.03522
• You only search once: Single Shot Neural Architecture Search via Direct Sparse Optimization (Zhang et al. 2018)
  https://arxiv.org/abs/1811.01567
• Automatically Evolving CNN Architectures Based on Blocks (Sun et al. 2018; accepted by IEEE Transactions on Neural Networks and Learning Systems)
  https://arxiv.org/abs/1810.11875
• The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints (Hundt et al. 2018; accepted at IROS’19)
  https://arxiv.org/abs/1810.11714
• Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells (Nekrasov et al. 2018; accepted at CVPR’19)
  https://arxiv.org/abs/1810.10804
• Automatic Configuration of Deep Neural Networks with Parallel Efficient Global Optimization (van Stein et al. 2018)
  https://arxiv.org/abs/1810.05526
• Gradient Based Evolution to Optimize the Structure of Convolutional Neural Networks  (Mitschke et al. 2018)
  https://ieeexplore.ieee.org/document/8451394
• Searching Toward Pareto-Optimal Device-Aware Neural Architectures (Cheng et al. 2018)
  https://arxiv.org/abs/1808.09830
• Neural Architecture Optimization (Luo et al. 2018; accepted at NeurIPS’18)
  https://arxiv.org/abs/1808.07233
• Exploring Shared Structures and Hierarchies for Multiple NLP Tasks (Chen et al. 2018)
  https://arxiv.org/abs/1808.07658
• Neural Architecture Search: A Survey (Elsken et al. 2018)
  https://arxiv.org/abs/1808.05377
• BlockQNN: Efficient Block-wise Neural Network Architecture Generation (Zhong et al. 2018)
  https://arxiv.org/abs/1808.05584
• Automatically Designing CNN Architectures Using Genetic Algorithm for Image Classification (Sunet al. 2018)
  https://arxiv.org/abs/1808.03818
• Reinforced Evolutionary Neural Architecture Search (Chen et al. 2018; accepted at CVPR’19)
  https://arxiv.org/abs/1808.00193
• Teacher Guided Architecture Search (Bashivan et al. 2018)
  https://arxiv.org/abs/1808.01405
• Efficient Progressive Neural Architecture Search (Perez-Rua et al. 2018)
  https://arxiv.org/abs/1808.00391
• MnasNet: Platform-Aware Neural Architecture Search for Mobile (Tan et al. 2018; accepted at CVPR’19)
  https://arxiv.org/abs/1807.11626
• Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search (Zela et al. 2018)
  https://arxiv.org/abs/1807.06906
• Automatically Designing CNN Architectures for Medical Image Segmentation (Mortazi and Bagci 2018)
  https://arxiv.org/abs/1807.07663
• MONAS: Multi-Objective Neural Architecture Search using Reinforcement Learning (Hsu et al. 2018)
  https://arxiv.org/abs/1806.10332
• Path-Level Network Transformation for Efficient Architecture Search (Cai et al. 2018; accepted at ICML’18)
  https://arxiv.org/abs/1806.02639
• Lamarckian Evolution of Convolutional Neural Networks (Prellberg and Kramer, 2018)
  https://arxiv.org/abs/1806.08099
• Deep Learning Architecture Search by Neuro-Cell-based Evolution with Function-Preserving Mutations (Wistuba, 2018)
  http://www.ecmlpkdd2018.org/wp-content/uploads/2018/09/108.pdf
• DARTS: Differentiable Architecture Search (Liu et al. 2018; accepted at ICLR’19)
  https://arxiv.org/abs/1806.09055
• Constructing Deep Neural Networks by Bayesian Network Structure Learning (Rohekar et al. 2018)
  https://arxiv.org/abs/1806.09141
• Resource-Efficient Neural Architect (Zhou et al. 2018)
  https://arxiv.org/abs/1806.07912
• Efficient Neural Architecture Search with Network Morphism (Jin et al. 2018)
  https://arxiv.org/abs/1806.10282
• TAPAS: Train-less Accuracy Predictor for Architecture Search (Istrate et al. 2018)
  https://arxiv.org/abs/1806.00250
• Neural Architecture Search using Deep Neural Networks and Monte Carlo Tree Search (Wang et al 2018; accepted at AAAI’20)
  https://arxiv.org/abs/1805.07440
  https://arxiv.org/abs/1903.11059
• Multi-objective Architecture Search for CNNs (Elsken et al. 2018)
  https://arxiv.org/abs/1804.09081
• GNAS: A Greedy Neural Architecture Search Method for Multi-Attribute Learning (Huang et al 2018)
  https://arxiv.org/abs/1804.06964
• Evolutionary Architecture Search For Deep Multitask Networks (Liang et al. 2018)
  https://arxiv.org/abs/1803.03745
• From Nodes to Networks: Evolving Recurrent Neural Networks (Rawal et al. 2018)
  https://arxiv.org/abs/1803.04439
• Neural Architecture Construction using EnvelopeNets (Kamath et al. 2018)
  https://arxiv.org/abs/1803.06744
• Transfer Automatic Machine Learning (Wong et al. 2018)
  https://arxiv.org/abs/1803.02780
• Neural Architecture Search with Bayesian Optimisation and Optimal Transport (Kandasamy et al. 2018)
  https://arxiv.org/abs/1802.07191
• Efficient Neural Architecture Search via Parameter Sharing (Pham et al. 2018; accepted at ICML’18)
  https://arxiv.org/abs/1802.03268
• Regularized Evolution for Image Classifier Architecture Search (Real et al. 2018)
  https://arxiv.org/abs/1802.01548
• Effective Building Block Design for Deep Convolutional Neural Networks using Search (Dutta et al. 2018)
  https://arxiv.org/abs/1801.08577
• Combination of Hyperband and Bayesian Optimization for Hyperparameter Optimization in Deep Learning (Wang et al. 2018)
  https://arxiv.org/abs/1801.01596
• Memetic Evolution of Deep Neural Networks (Lorenzo and Nalepa 2018)
  https://dl.acm.org/citation.cfm?id=3205631
• Understanding and Simplifying One-Shot Architecture Search (Bender et al. 2018; accepted at ICML’18)
  http://proceedings.mlr.press/v80/bender18a/bender18a.pdf
• Differentiable Neural Network Architecture Search (Shin et al. 2018; accepted at ICLR’18 workshop)
  https://openreview.net/pdf?id=BJ-MRKkwG
• PPP-Net: Platform-aware progressive search for pareto-optimal neural architectures (Dong et al. 2018; accepted at ICLR’18 workshop)
  https://openreview.net/pdf?id=B1NT3TAIM
• Speeding up the Hyperparameter Optimization of Deep Convolutional Neural Networks (Hinz et al. 2018)
  https://www.worldscientific.com/doi/abs/10.1142/S1469026818500086
• Gitgraph – From Computational Subgraphs to Smaller Architecture search spaces (Bennani-Smires et al. 2018)
  https://openreview.net/pdf?id=rkiO1_1Pz
• N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning (Ashok et al. 2017; accepted at ICLR’18)
  https://arxiv.org/abs/1709.06030
• Genetic CNN (Xie and Yuille, 2017; accepted at ICCV’17)
  https://arxiv.org/abs/1703.01513
• MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks (Gordon et al. 2017)
  https://arxiv.org/abs/1711.06798
• MaskConnect: Connectivity Learning by Gradient Descent (Ahmed and Torresani. 2017; accepted at ECCV’18)
  https://arxiv.org/abs/1709.09582
• A Flexible Approach to Automated RNN Architecture Generation (Schrimpf et al. 2017)
  https://arxiv.org/abs/1712.07316
• DeepArchitect: Automatically Designing and Training Deep Architectures (Negrinho and Gordon 2017)
  https://arxiv.org/abs/1704.08792
• A Genetic Programming Approach to Designing Convolutional Neural Network Architectures (Suganuma et al. 2017; accepted at GECCO’17)
  https://arxiv.org/abs/1704.00764
• Practical Block-wise Neural Network Architecture Generation (Zhong et al. 2017; accepted at CVPR’18)
  https://arxiv.org/abs/1708.05552
• Accelerating Neural Architecture Search using Performance Prediction (Baker et al. 2017; accepted at NeurIPS worshop on Meta-Learning 2017)
  https://arxiv.org/abs/1705.10823
• Large-Scale Evolution of Image Classifiers (Real et al. 2017; accepted at ICML’17)
  https://arxiv.org/abs/1703.01041
• Hierarchical Representations for Efficient Architecture Search (Liu et al. 2017; accepted at ICLR’18)
  https://arxiv.org/abs/1711.00436
• Neural Optimizer Search with Reinforcement Learning (Bello et al. 2017)
  https://arxiv.org/abs/1709.07417
• Progressive Neural Architecture Search (Liu et al. 2017; accepted at ECCV’18)
  https://arxiv.org/abs/1712.00559
• Learning Transferable Architectures for Scalable Image Recognition (Zoph et al. 2017; CVPR’18)
  https://arxiv.org/abs/1707.07012
• Simple And Efficient Architecture Search for Convolutional Neural Networks (Elsken et al. 2017; accepted at NeurIPS workshop on Meta-Learning’17)
  https://arxiv.org/abs/1711.04528
• Bayesian Optimization Combined with Incremental Evaluation for Neural Network Architecture Optimization (Wistuba, 2017)
  https://www.semanticscholar.org/paper/Bayesian-Optimization-Combined-with-Successive-for-Wistuba/ddb182533c91f0941f088e1e298c52a111253554
• Finding Competitive Network Architectures Within a Day Using UCT (Wistuba 2017)
  https://arxiv.org/abs/1712.07420
• Hyperparameter Optimization: A Spectral Approach (Hazan et al. 2017)
  https://arxiv.org/abs/1706.00764
• SMASH: One-Shot Model Architecture Search through HyperNetworks (Brock et al. 2017; accepted at NeurIPS workshop on Meta-Learning’17)
  https://arxiv.org/abs/1708.05344
• Efficient Architecture Search by Network Transformation (Cai et al. 2017; accepted at AAAI’18)
  https://arxiv.org/abs/1707.04873
• Modularized Morphing of Neural Networks (Wei et al. 2017)
  https://arxiv.org/abs/1701.03281
• Towards Automatically-Tuned Neural Networks (Mendoza et al. 2016; accepted at ICML AutoML workshop)
  http://proceedings.mlr.press/v64/mendoza_towards_2016.html
• Neural Networks Designing Neural Networks: Multi-Objective Hyper-Parameter Optimization (Smithson et al. 2016)
  https://arxiv.org/abs/1611.02120
• AdaNet: Adaptive Structural Learning of Artificial Neural Networks (Cortes et al. 2016)
  https://arxiv.org/abs/1607.01097
• Network Morphism (Wei et al. 2016)
  https://arxiv.org/abs/1603.01670
• Convolutional Neural Fabrics (Saxena and Verbeek 2016; accepted at NeurIPS’16)
  https://arxiv.org/abs/1606.02492
• CMA-ES for Hyperparameter Optimization of Deep Neural Networks (Loshchilov and Hutter 2016)
  https://arxiv.org/abs/1604.07269
• Designing Neural Network Architectures using Reinforcement Learning (Baker et al. 2016; accepted at ICLR’17)
  https://arxiv.org/abs/1611.02167
• Neural Architecture Search with Reinforcement Learning (Zoph and Le. 2016; accepted at ICLR’17)
  https://arxiv.org/abs/1611.01578
• Learning curve prediction with Bayesian Neural Networks (Klein et al. 2017: accepted at ICLR’17)
  http://ml.informatik.uni-freiburg.de/papers/17-ICLR-LCNet.pdf
• Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization (Li et al. 2016)
  https://arxiv.org/abs/1603.06560
• Net2Net: Accelerating Learning via Knowledge Transfer (Chen et al. 2015; accepted at ICLR’16)
  https://arxiv.org/abs/1511.05641
• Optimizing deep learning hyper-parameters through an evolutionary algorithm (Young et al. 2015)
  https://dl.acm.org/citation.cfm?id=2834896
• Practical Bayesian Optimization of Machine Learning Algorithms (Snoek et al. 2012; accepted at NeurIPS’12)
  https://papers.nips.cc/paper/4522-practical-bayesian-optimization-of-machine-learning-algorithms.pdf
• A Hypercube-based Encoding for Evolving large-scale Neural Networks (Stanley et al. 2009)
  https://ieeexplore.ieee.org/document/6792316/
• Neuroevolution: From Architectures to Learning (Floreano et al. 2008; accepted at Evolutionary Intelligence’08)
  https://link.springer.com/article/10.1007/s12065-007-0002-4
• Evolving Neural Networks through Augmenting Topologies (Stanley and Miikkulainen, 2002; accepted at Evolutionary Computation’02)
  http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf
• Evolving Artificial Neural Networks (Yao, 1999; accepted at IEEE)
  https://ieeexplore.ieee.org/document/784219/
• An Evolutionary Algorithm that Constructs Recurrent Neural Networks (Angeline et al. 1994)
  https://ieeexplore.ieee.org/document/265960/
• Designing Neural Networks Using Genetic Algorithms with Graph Generation System (Kitano, 1990)
  http://www.complex-systems.com/abstracts/v04_i04_a06/
• Designing Neural Networks using Genetic Algorithms (Miller et al. 1989; accepted at ICGA’89)
  https://dl.acm.org/citation.cfm?id=94034
• The Cascade-Correlation Learning Architecture (Fahlman and Leblere, 1989; accepted at NeurIPS’89)
  https://papers.nips.cc/paper/207-the-cascade-correlation-learning-architecture
• Self Organizing Neural Networks for the Identification Problem (Tenorio and Lee, 1988; accepted at NeurIPS’88)
  https://papers.nips.cc/paper/149-self-organizing-neural-networks-for-the-identification-problem