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文件名称: 机器学习论文合集(pdf格式).zip
  所属分类: 深度学习
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  文件大小: 74mb
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  上传时间: 2020-03-10
  提 供 者: LLL***
 详细说明:25篇机器学习经典论文合集,有需要欢迎积分自取 Efficient sparse coding algorithms论文附有代码 [1] Zheng S, Kwok J T. Follow the moving leader in deep learning[C]//Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 2017: 4110-4119. [2] Kalai A, Vempala S. Efficient algorithms for online decision problems[J]. Journal of Computer and System Sciences, 2005, 71(3): 291-307. [3] Kingma, D. and Ba, J. Adam: A method for stochastic optimization. In Proceedings of the International Conference for Learning Representations, 2015. [4] Lee H, Battle A, Raina R, et al. Efficient sparse coding algorithms[C]//Advances in neural information processing systems. 2007: 801-808. [5] Fan J, Ding L, Chen Y, et al. Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery[J]. 2019. [6] Z. Lai, Y. Chen, J. Wu, W. W. Keung, and F. Shen, “Jointly sparse hashing for image retrieval,” IEEE Transactions on Image Processing, vol. 27, no. 12, pp. 6147–6158, 2018. [7] Z. Zhang, Y. Chen, and V. Saligrama, “Efficient training of very deep neural networks for supervised hashing,” in Proc. IEEE Int. Conf. Computer Vision and Pattern Recognition, 2016, pp. 1487–1495. [8] Wei-Shi Zheng, Shaogang Gong, Tao Xiang. Person re-identification by probabilistic relative distance comparison[C]// CVPR 2011. IEEE, 2011. [9] Liao S, Hu Y, Zhu X, et al. Person re-identification by local maximal occurrence representation and metric learning[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 2197-2206. [10] Liu X, Li H, Shao J, et al. Show, tell and discriminate: Image captioning by self-retrieval with partially labeled data[C]//Proceedings of the European Conference on Computer Vision (ECCV). 2018: 338-354. [11] Yao T, Pan Y, Li Y, et al. Exploring visual relationship for image captioning[C]//Proceedings of the European conference on computer vision (ECCV). 2018: 684-699. [12] Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang., ”Image
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