Saxena S., Suematsu Y. L., Tan J., Le Q., Kurakin A. (2017). Large-Scale Evolution of Image Classifiers // https://arxiv.org/abs/1703.01041

3265

Kaiser L., Gomez A. N., Shazeer N., Vaswani A., Parmar N., Jones L., Uszkoreit J. (2017). One Model To Learn Them All // https://arxiv.org/abs/1706.05137

3266

Zoph B., Vasudevan V., Shlens J., Le Q. V. (2017). Learning Transferable Architectures for Scalable Image Recognition // https://arxiv.org/abs/1707.07012

3267

Chen L.-C., Collins M. D., Zhu Y., Papandreou G., Zoph B., Schroff F., Adam H., Shlens J. (2018). Searching for Efficient Multi-Scale Architectures for Dense Image Prediction // https://arxiv.org/abs/1809.04184

3268

Liu H., Simonyan K., Yang Y. (2018). DARTS: Differentiable Architecture Search // https://arxiv.org/abs/1806.09055

3269

Howard A., Sandler M., Chu G., Chen L.-C., Chen B., Tan M., Wang W., Zhu Y., Pang R., Vasudevan V., Le Q. V., Adam H. (2019). Searching for MobileNetV3 // https://arxiv.org/abs/1905.02244v5

3270

Xiong Y., Liu H., Gupta S., Akin B., Bender G., Kindermans P.-J., Tan M., Singh V., Chen B. (2020). MobileDets: Searching for Object Detection Architectures for Mobile Accelerators // https://arxiv.org/abs/2004.14525v2

3271

Abdelfattah M. S., Mehrotra A., Dudziak Ł., Lane N. D. (2021). Zero-Cost Proxies for Lightweight NAS // https://arxiv.org/abs/2101.08134

3272

Dudziak Ł., Chau T., Abdelfattah M. S., Lee R., Kim H., Lane N. D. (2020). BRP-NAS: Prediction-based NAS using GCNs // https://arxiv.org/abs/2007.08668

3273

Zhang Y., Zhang Q., Yang Y. (2020). How Does Supernet Help in Neural Architecture Search? // https://arxiv.org/abs/2010.08219

3274

Dai X., Zhang P., Wu B., Yin H., Sun F., Wang Y., Dukhan M., Hu Y., Wu Y., Jia Y., Vajda P., Uyttendaele M., Jha N. K. (2018). ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation // https://arxiv.org/abs/1812.08934

3275

Wan A., Dai X., Zhang P., He Z., Tian Y., Xie S., Wu B., Yu M., Xu T., Chen K., Vajda P., Gonzalez J. E. (2020). FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions // https://arxiv.org/abs/2004.05565

3276

Awad N., Mallik N., Hutter F. (2020). Differential Evolution for Neural Architecture Search // https://arxiv.org/abs/2012.06400

3277

Jie R., Gao J. (2021). Differentiable Neural Architecture Search with Morphism-based Transformable Backbone Architectures // https://arxiv.org/abs/2106.07211

3278

Tian Y., Shen L., Shen L., Su G., Li Z., Liu W. (2020). AlphaGAN: Fully Differentiable Architecture Search for Generative Adversarial Networks // https://arxiv.org/abs/2006.09134

3279

Ding M., Lian X., Yang L., Wang P., Jin X., Lu Z., Luo P. (2021). HR-NAS: Searching Efficient High-Resolution Neural Architectures with Lightweight Transformers // https://arxiv.org/abs/2106.06560

3280

Yang Y., You S., Li H., Wang F., Qian C., Lin Z. (2021). Towards Improving the Consistency, Efficiency, and Flexibility of Differentiable Neural Architecture Search // https://arxiv.org/abs/2101.11342

3281

Jin H., Song Q., Hu X. (2018). Auto-Keras: An Efficient Neural Architecture Search System // https://arxiv.org/abs/1806.10282

3282

Ying C., Klein A., Real E., Christiansen E., Murphy K., Hutter F. (2019). NAS-Bench-101: Towards Reproducible Neural Architecture Search // https://arxiv.org/abs/1902.09635

3283

Zela A., Siems J., Hutter F. (2020). NAS-Bench-1Shot1: Benchmarking and Dissecting One-shot Neural Architecture Search // https://arxiv.org/abs/2001.10422

3284

Dong X., Yang Y. (2020). NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search // https://arxiv.org/abs/2001.00326

3285

Tu R., Khodak M., Roberts N., Talwalkar A. (2021). NAS-Bench-360: Benchmarking Diverse Tasks for Neural Architecture Search // https://arxiv.org/abs/2110.05668

3286

Yan S., White C., Savani Y., Hutter F. (2021). NAS-Bench-x11 and the Power of Learning Curves // https://arxiv.org/abs/2111.03602

3287

Li C., Yu Z., Fu Y., Zhang Y., Zhao Y., You H., Yu Q., Wang Y., Lin Y. (2021). HW-NAS-Bench: Hardware-Aware Neural Architecture Search Benchmark // https://arxiv.org/abs/2103.10584

3288

Mehrotra A., Ramos A. G. C. P., Bhattacharya S., Dudziak Ł., Vipperla R., Chau T., Abdelfattah M. S., Ishtiaq S., Lane N. D. (2020). NAS-Bench-ASR: Reproducible Neural Architecture Search for Speech Recognition // https://openreview.net/forum?id=CU0APx9LMaL

3289

Dong X., Liu L., Musial K., Gabrys B. (2020). NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size // https://arxiv.org/abs/2009.00437

3290

Klein A., Hutter F. (2019). Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization // https://arxiv.org/abs/1905.04970

3291

Hirose Y., Yoshinari N., Shirakawa S. (2021). NAS-HPO-Bench-II: A Benchmark Dataset on Joint Optimization of Convolutional Neural Network Architecture and Training Hyperparameters // https://arxiv.org/abs/2110.10165

3292

Tan M., Le Q. V. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks // https://arxiv.org/abs/1905.11946

3293

Arora A. (2020). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks // https://amaarora.github.io/2020/08/13/efficientnet.html

3294

Tan M., Le Q. V. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks // https://arxiv.org/abs/1905.11946

3295

Huang Y., Cheng Y., Bapna A., Firat O., Chen M. X., Chen D., Lee H. J., Ngiam J., Le Q. V., Wu Y., Chen Z. (2018). GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism // https://arxiv.org/abs/1811.06965

3296

Pham H., Dai Z., Xie Q., Luong M.-T., Le Q. V. (2020). Meta Pseudo Labels // https://arxiv.org/abs/2003.10580

3297

Wang Z., Yang E., Shen L., Huang H. (2023). A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning // https://arxiv.org/abs/2307.09218

3298

Kirkpatrick J., Pascanu R., Rabinowitz N.,