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https://www.automl.org/automl/literature-on-neural-architecture-search/

• 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)
  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 (Singh et al. 2020)
  https://arxiv.org/pdf/2002.06963.pdf

• 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

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