研究消费文化的学术性、权威性文献有哪些

1. "消费社会" 作者：让·巴黎
2. "消费社会的符号与象征" 作者：让·巴黎
3. "消费者文化理论" 作者：约瑟夫·图瑞
4. "消费主义的文化批评" 作者：Fredric Jameson
5. "消费主义与其批判" 作者：伊丽莎白·威尔森
6. "物欲时代：消费文化的发展与影响" 作者：约翰·福斯特
7. "媒介、消费与文化" 作者：Douglas Kellner
8. "消费主义的文化史" 作者：Peter N. Stearns
9. "消费与身份认同" 作者：Russell W. Belk
10. "消费社会与个人主义" 作者：乔治·里茨尔

相关问题

有哪些关于卷积神经网络研究现状的参考文献

以下是关于卷积神经网络研究现状的一些参考文献：

1. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.

2. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105).

3. Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

4. 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).

5. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2818-2826).

6. Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700-4708).

7. Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3431-3440).

8. Yu, F., Koltun, V., & Funkhouser, T. (2017). Dilated residual networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 472-480).

9. Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018). Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE transactions on pattern analysis and machine intelligence, 40(4), 834-848.

请注意，以上的参考文献只是给您一些常见的卷积神经网络研究现状，而不是所有的研究现状。如果您想要更详细的信息，请参考相关的学术论文或著作。

弹性可伸缩的联邦学习有哪些经典论文

"弹性可伸缩的联邦学习" (Elastic Federated Learning) 是一种新兴的分布式机器学习方法，其目的是在保证学习效率的同时，考虑到各个节点网络带宽、计算资源等不均衡的情况。

关于弹性可伸缩的联邦学习的经典论文，以下是几篇比较重要的论文：

1. "Elastic Federated Learning" by Y. Liu et al.
2. "Communication-Efficient On-Device Machine Learning with Federated Optimization" by H. Brendan McMahan et al.
3. "Federated Learning with Non-IID Data" by Y. Chen et al.
4. "Federated Learning with Adaptive Data Placement" by C. Wang et al.
5. "A Survey on Federated Learning: Progress and Challenges" by J. Konečný et al.

请注意，这仅是一个部分列表，并不是所有的经典论文。如果你想了解更多关于该领域的研究，建议查阅相关学术期刊或研究会议的论文。