Охота на электроовец. Большая книга искусственного интеллекта - Сергей Сергеевич Марков

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

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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

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

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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

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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/

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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

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

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

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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

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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

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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

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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

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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

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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

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

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

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

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

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

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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

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

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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/

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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

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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

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

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

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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

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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

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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

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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.