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Szegedy, C., et al., Going deeper with convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.SzegedyC.2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)2015Search in Google Scholar
He, K., et al., Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.HeK.2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)2016Search in Google Scholar
Devlin, J., et al., BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv, 2018.DevlinJ.arXiv,2018Search in Google Scholar
Silver, D., et al., Mastering the game of Go without human knowledge. Nature, 2017. 550(7676): p. 354–359.SilverD.Mastering the game of Go without human knowledge2017550767635435910.1038/nature2427029052630Search in Google Scholar
Ardito, L., A. Messeni Petruzzelli and V. Albino, Investigating the antecedents of general purpose technologies: A patent perspective in the green energy field. Journal of Engineering and Technology Management, 2016. 39: p. 81–100.ArditoL.PetruzzelliA. MesseniAlbinoV.Investigating the antecedents of general purpose technologies: A patent perspective in the green energy field2016398110010.1016/j.jengtecman.2016.02.002Search in Google Scholar
Ardito, L., D. D’Adda and A. Messeni Petruzzelli, Mapping innovation dynamics in the Internet of Things domain: Evidence from patent analysis. Technological Forecasting and Social Change, 2018. 136: p. 317–330.ArditoL.D’AddaD.PetruzzelliA. MesseniMapping innovation dynamics in the Internet of Things domain: Evidence from patent analysis201813631733010.1016/j.techfore.2017.04.022Search in Google Scholar
Silver, D., et al., Mastering the game of Go with deep neural networks and tree search. Nature, 2016. 529(7587): p. 484–489.SilverD.Mastering the game of Go with deep neural networks and tree search2016529758748448910.1038/nature1696126819042Search in Google Scholar
Huang, J. and R. Chen, Exploring the intellectual structure of cloud patents using non-exhaustive overlaps. Scientometrics, 2019. 121(2): p. 739–769.HuangJ.ChenR.Exploring the intellectual structure of cloud patents using non-exhaustive overlaps2019121273976910.1007/s11192-019-03219-4Search in Google Scholar
Huang, J. and K. Tan, An Extension-Based Classification System of Cloud Computing Patents. International Journal of Information Technology & Decision Making, 2020. 19(04): p. 1149–1172.HuangJ.TanK.An Extension-Based Classification System of Cloud Computing Patents202019041149117210.1142/S0219622020500248Search in Google Scholar
Deng, J., et al., ImageNet: a Large-Scale Hierarchical Image Database. 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009.DengJ.2009 IEEE Conference on Computer Vision and Pattern Recognition2009Search in Google Scholar
Krizhevsky, A., I. Sutskever and G.E. Hinton, ImageNet Classification with Deep Convolutional Neural Networks. Communications of the ACM, 2017. 6(60): p. 84–90.KrizhevskyA.SutskeverI.HintonG.E.ImageNet Classification with Deep Convolutional Neural Networks2017660849010.1145/3065386Search in Google Scholar
Leu, H., C. Wu and C. Lin, Technology exploration and forecasting of biofuels and biohydrogen energy from patent analysis. International Journal of Hydrogen Energy, 2012. 37(20): p. 15719–15725.LeuH.WuC.LinC.Technology exploration and forecasting of biofuels and biohydrogen energy from patent analysis20123720157191572510.1016/j.ijhydene.2012.04.143Search in Google Scholar
Vaccario G, Tomasello M V, Tessone C J, et al. Quantifying knowledge exchange in R&D networks: a data-driven modelVaccarioGTomaselloM VTessoneC JSearch in Google Scholar
Liu, S., et al., Development of a Patent Retrieval and Analysis Platform – A hybrid approach. Expert Systems with Applications, 2011. 38(6): p. 7864–7868.LiuS.Development of a Patent Retrieval and Analysis Platform – A hybrid approach20113867864786810.1016/j.eswa.2010.12.114Search in Google Scholar
Ardito, L., D. D’Adda and A. Messeni Petruzzelli, Mapping innovation dynamics in the Internet of Things domain: Evidence from patent analysis. Technological Forecasting and Social Change, 2018. 136: p. 317–330.ArditoL.D’AddaD.PetruzzelliA. MesseniMapping innovation dynamics in the Internet of Things domain: Evidence from patent analysis201813631733010.1016/j.techfore.2017.04.022Search in Google Scholar
van den Oord, A., et al., WaveNet: A Generative Model for Raw Audio. 9th ISCA Speech Synthesis Workshop, 2016: p. 13–15.van den OordA.WaveNet: A Generative Model for Raw Audio20161315Search in Google Scholar
Vargas, M.R., et al., Deep Learning for Stock Market Prediction Using Technical Indicators and Financial News Articles. 2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), 2017: p. 60–65.VargasM.R.2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)20176065Search in Google Scholar
Yoon, J. and K. Kim, An analysis of property–function based patent networks for strategic R&D planning in fast-moving industries: The case of silicon-based thin film solar cells. Expert Systems with Applications, 2012. 39(9): p. 7709–7717.YoonJ.KimK.An analysis of property–function based patent networks for strategic R&D planning in fast-moving industries: The case of silicon-based thin film solar cells20123997709771710.1016/j.eswa.2012.01.035Search in Google Scholar
Jun Liu, Huihong Chang, Jeffrey Yi-Lin Forrest, et al. Influence of artificial intelligence on technological innovation: Evidence from the panel data of china's manufacturing sectors. 2020, 158(C).LiuJunChangHuihongForrestJeffrey Yi-Lin2020158(C).10.1016/j.techfore.2020.120142Search in Google Scholar