Hattrick-Simpers J., Mehta A. (2018). Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments / Science Advances, Vol. 4, No. 4 // https://doi.org/10.1126/sciadv.aaq1566

3016

Hornby G. S., Globus A., Linden D. S., Lohn J. D. (2006). Automated antenna design with evolutionary algorithms // https://doi.org/10.2514/6.2006-7242

3017

Robinson A. (2019). Sketch2code: Generating a website from a paper mockup // https://arxiv.org/abs/1905.13750

3018

Dosovitskiy A., Springenberg J. T., Tatarchenko M., Brox T. (2014). Learning to Generate Chairs, Tables and Cars with Convolutional Networks // https://arxiv.org/abs/1411.5928

3019

Spilka D. (2018). What Does AI mean for Interior Design? / MIPIM World Blog, May 3, 2018 // https://blog.mipimworld.com/innovation/ai-artificial-intelligence-mean-interior-design/

3020

Злобин А. (2020). Студия Лебедева больше года выдавала искусственный интеллект за реального дизайнера / Forbes, 26.06.2020 // https://www.forbes.ru/newsroom/tehnologii/403795-studiya-lebedeva-bolshe-goda-vydavala-iskusstvennyy-intellekt-za-realnogo

3021

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

3022

Zoph B., Le Q. V. (2016). Neural Architecture Search with Reinforcement Learning // https://arxiv.org/abs/1611.01578

3023

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

3024

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

3025

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

3026

Ahmad W. U., Chakraborty S., Ray B., Chang K.-W. (2021). Unified Pre-training for Program Understanding and Generation // https://arxiv.org/abs/2103.06333

3027

Lewis M., Liu Y., Goyal N., Ghazvininejad M., Mohamed A., Levy O., Stoyanov V., Zettlemoyer L. (2019). BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension // https://arxiv.org/abs/1910.13461

3028

Feng Z., Guo D., Tang D., Duan N., Feng X., Gong M., Shou L., Qin B., Liu T., Jiang D., Zhou M. (2020). CodeBERT: A Pre-Trained Model for Programming and Natural Languages // https://arxiv.org/abs/2002.08155

3029

Svyatkovskiy A., Deng S. K., Fu S., Sundaresan N. (2020). IntelliCode Compose: Code Generation Using Transformer // https://arxiv.org/abs/2005.08025

3030

Alon U., Zilberstein M., Levy O., Yahav E. (2018). code2vec: Learning Distributed Representations of Code // https://arxiv.org/abs/1803.09473

3031

kite. Code Faster. Stay in Flow // https://www.kite.com/

3032

TabNine. Code faster with AI completions // https://www.tabnine.com/

3033

Copilot. Your AI pair programmer // https://copilot.github.com/

3034

Li Y., Choi D., Chung J., Kushman N., Schrittwieser J., Leblond R., Eccles T., Keeling J., Gimeno F., Lago A. D., Hubert T., Choy P., de Masson d’Autume C., Babuschkin I., Chen X., Huang P.-S., Welbl J., Gowal S., Cherepanov A., Molloy J., Mankowitz D. J., Robson E. S., Kohli P., de Freitas N., Kavukcuoglu K., Vinyals O. (2022). Competition-Level Code Generation with AlphaCode // https://storage.googleapis.com/deepmind-media/AlphaCode/competition_level_code_generation_with_alphacode.pdf

3035

Perez L., Ottens L., Viswanathan S. (2021). Automatic Code Generation using Pre-Trained Language Models // https://arxiv.org/abs/2102.10535

3036

Langston J. (2021). From conversation to code: Microsoft introduces its first product features powered by GPT-3 / Microsoft/The AI Blog, May 25, 2021 // https://blogs.microsoft.com/ai/from-conversation-to-code-microsoft-introduces-its-first-product-features-powered-by-gpt-3/

3037

Hasan M., Mehrab K. S., Ahmad W. U., Shahriyar R. (2021). Text2App: A Framework for Creating Android Apps from Text Descriptions // https://arxiv.org/abs/2104.08301

3038

Lin G., Wen S., Han Q.-L., Zhang J., Xiang Y. (2020). Software Vulnerability Detection Using Deep Neural Networks: A Survey / Proceedings of the IEEE, Vol. 108, Iss. 10, pp. 1825—1848 // https://doi.org/10.1109/JPROC.2020.2993293

3039

Wu J. (2021). Literature review on vulnerability detection using NLP technology // https://arxiv.org/abs/2104.11230

3040

Ziems N., Wu S. (2021). Security Vulnerability Detection Using Deep Learning Natural Language Processing // https://arxiv.org/abs/2105.02388

3041

Fried D., Aghajanyan A., Lin J., Wang S., Wallace E., Shi F., Zhong R., Yih W.-T., Zettlemoyer L., Lewis M. (2022). InCoder: A Generative Model for Code Infilling and Synthesis // https://arxiv.org/abs/2204.05999

3042

Nijkamp E., Pang B., Hayashi H., Tu L., Wang H., Zhou Y., Savarese S., Xiong C. (2022). CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis // https://arxiv.org/abs/2203.13474

3043

Allal L. B., Li R., Kocetkov D., Mou C., Akiki C., Ferrandis C. M., Muennighoff N., Mishra M., Gu A., Dey M., Umapathi L. K., Anderson C. J., Zi Y., Poirier J. L., Schoelkopf H., Troshin S., Abulkhanov D., Romero M., Lappert M., Toni F. D., de Río B. G., Liu Q., Bose S., Bhattacharyya U., Zhuo T. Y., Yu I., Villegas P., Zocca M., Mangrulkar S., Lansky D., Nguyen H., Contractor D., Villa L., Li J., Bahdanau D., Jernite Y., Hughes S., Fried D., Guha A., de Vries H., von Werra L. (2023). SantaCoder: don't reach for the stars! // https://arxiv.org/abs/2301.03988

3044

Li R., Allal L. B., Zi Y., Muennighoff N., Kocetkov D., Mou C., Marone M., Akiki C., Li J., Chim J., Liu Q., Zheltonozhskii E., Zhuo T. Y., Wang T., Dehaene O., Davaadorj M., Lamy-Poirier J., Monteiro J., Shliazhko O., Gontier N., Meade N., Zebaze A., Yee M., Umapathi L. K., Zhu J., Lipkin B., Oblokulov M., Wang Z., Murthy R., Stillerman J., Patel S.