请列举可以参考的相关文献或者博客
时间: 2024-06-10 11:07:22 浏览: 16
可以参考以下文献或博客:
1. "OpenAI’s GPT-3 Language Model: A Technical Overview" by Denny Britz (https://www.blog.dennybritz.com/openais-gpt-3-language-model-a-technical-overview/)
2. "GPT-2: Language Models are Unsupervised Multitask Learners" by Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever (https://openai.com/blog/better-language-models/)
3. "The Illustrated GPT-2 (Visualizing Transformer Language Models)" by Jay Alammar (https://jalammar.github.io/illustrated-gpt2/)
4. "Attention Is All You Need" by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aiden N. Gomez, Łukasz Kaiser, and Illia Polosukhin (https://arxiv.org/abs/1706.03762)
相关问题
请列举一些内容管理系统的参考文献
以下是内容管理系统的参考文献:
1. Liferay in Action: The Official Guide to Liferay Portal Development by Richard Sezov
2. Drupal for Education and E-Learning by Bill Fitzgerald
3. WordPress: The Missing Manual by Matthew MacDonald
4. Joomla! 3 Beginner's Guide by Eric Tiggeler
5. TYPO3 4.3 Multimedia Cookbook by Dan Osipov
6. Squiz Matrix User Manual by Squiz
7. Umbraco User's Guide by Niels Hartvig and Morten Christensen
8. ExpressionEngine User Guide by EllisLab
9. Ektron Developer's Guide: Building an Ektron Powered Website by William Cava and Bill Rogers
10. MODX Revolution: Building the Web Your Way by Scott Greenfield.
列举10个与计算机视觉相关的参考文献
以下是10个与计算机视觉相关的参考文献:
1. Szeliski, R. (2010). Computer vision: algorithms and applications. Springer Science & Business Media.
2. Forsyth, D. A., & Ponce, J. (2012). Computer vision: a modern approach. Prentice Hall Press.
3. Prince, S. J. D. (2012). Computer vision: models, learning, and inference. Cambridge University Press.
4. Zhang, D., & Li, H. (2010). Research on key technologies of computer vision. Journal of Computational Information Systems, 6(5), 1871-1878.
5. Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2), 91-110.
6. Viola, P., & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001 (Vol. 1, pp. I-I). IEEE.
7. Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. In 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05) (Vol. 1, pp. 886-893). IEEE.
8. Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. science, 313(5786), 504-507.
9. Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: towards real-time object detection with region proposal networks. In Advances in neural information processing systems (pp. 91-99).
10. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).