| [1] |
郁建兴, 刘宇轩, 吴超 . 人工智能大模型的变革与治理[J]. 中国行政管理, 2023,39(4): 6-13.
|
|
YU J X , LIU Y X , WU C . The social transformation and governance of large generative artificial intelligence models[J]. Chinese Public Administration, 2023,39(4): 6-13.
|
| [2] |
张凌寒, 于琳 . 从传统治理到敏捷治理:生成式人工智能的治理范式革新[J]. 电子政务,已录用, 2023.
|
|
ZHANG L H , YU L . From traditional governance to agile governance:innovation in the governance paradigm of generative artificial intelligence[J]. E-Government,accepted, 2023.
|
| [3] |
宋恺, 屈蕾蕾, 杨萌科 . 生成式人工智能的治理策略研究[J]. 信息通信技术与政策, 2023,49(7): 83-88.
|
|
SONG K , QU L L , YANG M K . Research on governance strategy of generative artificial intelligence[J]. Information and Communications Technology and Policy, 2023,49(7): 83-88.
|
| [4] |
于水, 范德志 . 新一代人工智能(ChatGPT)的主要特征、社会风险及其治理路径[J]. 大连理工大学学报(社会科学版),已录用, 2023.
|
|
YU S , FAN D Z . The main characteristics,social risks and governance paths of the new generation of artificial intelligence(ChatGPT)[J]. Journal of Dalian University of Technology(Social Sciences),accepted, 2023.
|
| [5] |
张欣 . 生成式人工智能的数据风险与治理路径[J]. 法律科学(西北政法大学学报), 2023(5): 1-13.
|
|
ZHANG X . The data risk and governance path of generative artificial intelligence[J]. Science of Law(Journal of Northwest University of Political Science and Law), 2023(5): 1-13.
|
| [6] |
WU J , GAN W , CHEN Z ,et al. AI-generated content (AIGC):a survey[EB]. arXiv preprint, 2023,arXiv:2304.06632.
|
| [7] |
YOUNG S , BLOOTHOOFT G . Corpus-based methods in language and speech processing[J]. Springer Science ＆ Business Media, 1997.
|
| [8] |
REYNOLDS D , LI S Z , JAIN A . Gaussian mixture models[M]// Encyclopedia of Biometrics. Boston.MA: Springer, 2009: 659-663.
|
| [9] |
BENGIO Y , DUCHARME R , VINCENT P . A neural probabilistic language model[J]. Advances in Neural Information Processing Systems, 2000.
|
| [10] |
MIKOLOV T , KARAFIAT M , BURGET L ,et al. Recurrent neural network based language model[J]. In Interspeech, 2010,2(3): 1045-1048.
|
| [11] |
GRAVES A . Long short-term memory[M]// Supervised Sequence Labelling with Recurrent Neural Networks. Berlin,Heidelberg: Springer, 2012: 37-45.
|
| [12] |
DEY R , SALEM F M . Gate-variants of gated recurrent unit (GRU) neural networks[C]// Proceedings of 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS). Piscataway:IEEE Press, 2017: 1597-1600.
|
| [13] |
EFROS A A , LEUNG T K . Texture synthesis by non-parametric sampling[C]// Proceedings of the Seventh IEEE International Conference on Computer Vision. Piscataway:IEEE Press, 2002: 1033-1038.
|
| [14] |
HECKBERT P S . Survey of texture mapping[J]. IEEE Computer Graphics and Applications, 1986,6(11): 56-67.
|
| [15] |
CRESWELL A , WHITE T , DUMOULIN V ,et al. Generative adversarial networks:an overview[J]. IEEE Signal Processing Magazine, 2018,35(1): 53-65.
|
| [16] |
SONG Y , ERMON S . Generative modeling by estimating gradients of the data distribution[J]. Advances in Neural Information Processing Systems, 2019,32.
|
| [17] |
VASWANI A , SHAZEER N , PARMAR N ,et al. Attention is all you need[J]. Advances in Neural Information Processing Systems, 2017,30.
|
| [18] |
DOSOVITSKIY A , BEYER L , KOLESNIKOV A ,et al. An image is worth 16x16 words:transformers for image recognition at scale[EB]. arXiv preprint, 2020,arXiv:2010.11929.
|
| [19] |
LIU Z , LIN Y T , CAO Y ,et al. Swin transformer:hierarchical vision transformer using shifted windows[C]// Proceedings of 2021 IEEE/CVF International Conference on Computer Vision (ICCV). Piscataway:IEEE Press, 2022: 9992-10002.
|
| [20] |
RADFORD A , KIM J W , HALLACY C ,et al. Learning transferable visual models from natural language supervision[C]// Proceedings of International Conference on Machine Learning.New York:PMLR. 2021: 8748-8763.
|
| [21] |
CAO Y , LI S , LIU Y ,et al. A comprehensive survey of AI-generated content (AIGC):a history of generative AI from GAN to ChatGPT[EB]. arXiv preprint, 2023,arXiv:2303.04226.
|
| [22] |
OUYANG L , WU J , JIANG X ,et al. Training language models to follow instructions with human feedback[EB]. arXiv preprint, 2022:arXiv:2203.02155.
|
| [23] |
TAN F X , YAN P F , GUAN X P . Deep reinforcement learning:from Qlearning to deep Q-learning[M]// Neural Information Processing. Cham: Springer International Publishing, 2017: 475-483.
|
| [24] |
ROMBACH R , BLATTMANN A , LORENZ D ,et al. High-resolution image synthesis with latent diffusion models[C]// Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway:IEEE Press, 2022: 10674-10685.
|
| [25] |
CARLINI N , IPPOLITO D , JAGIELSKI M ,et al. Quantifying memorization across neural language models[EB]. arXiv preprint, 2022,arXiv:2202.07646.
|
| [26] |
CARLINI N , HAYES J , NASR M ,et al. Extracting training data from diffusion models[EB]. arXiv preprint, 2023,arXiv:2301.13188.
|
| [27] |
BORJI A . A categorical archive of ChatGPT failures[EB]. arXiv preprint, 2023,arXiv:2302.03494.
|
| [28] |
SCHUHMANN C , VENCU R , BEAUMONT R ,et al. Laion-400m:open dataset of clip-filtered 400 million image-text pairs[EB]. arXiv preprint, 2021,arXiv:2111.02114.
|
| [29] |
周慧珺, 邹文博 . 数字化转型背景下数字鸿沟的现状、影响与应对策略[J]. 当代经济管理, 2023,45(3): 60-67.
|
|
ZHOU H J , ZOU W B . The present situation,influence and countermeasures of the digital divide under the background of digital transformation[J]. Contemporary Economic Management, 2023,45(3): 60-67.
|
| [30] |
李春芳, 黄涛 . 受众视角下人工智能生成物的版权保护探析[J]. 华南理工大学学报(社会科学版), 2022,24(1): 24-32.
|
|
LI C F , HUANG T . Copyright protection of artificial intelligence products from the perspective of audience[J]. Journal of South China University of Technology(Social Science Edition), 2022,24(1): 24-32.
|
