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

100篇+深度学习论文合集；DBN；ImageNet；Speech Recognition Evolution；model；optimization、unsupervise learning;RNN；transfer learning；One Shot Deep Learning；Deep Reinforcement Learning；Neural Turing Machine等。

吴恩达在深度学习课程中所提及，建议大家阅读的经典论文。

天气预测论文整理79篇，基于深度学习和数据融合研究强对流天气，适合写论文综述，天气预测研究

FAST AND ACCURATE DEEP NETWORK LEARNING BY EXPONENTIAL LINEAR UNITS (ELUS)（2016，Djork-Arn´e Clevert, Thomas Unterthiner & Sepp Hochreiter）

论文翻译，人工翻译，排版，图像处理，前沿研究

唐宇迪博士深度学习课程系列中深度学习论文集，都是很经典的深度学习论文，高清版本，非常适合对该领域感兴趣的读者阅读研究，其中涉及了深度学习理论的方方面面。

那些值得读的深度学习论文集合

100篇+深度学习论文合集 1、Deep Belief Network(DBN)(Milestone of Deep Learning Eve) 2、ImageNet Evolution（Deep Learning broke out from here） 3、Speech Recognition Evolution 4、Model 5、Optimization 6、Unsupervised Learning Deep Generative Model 7、RNN Sequence-to-Sequence Model 8、Neural Turing Machine 9、Deep Reinforcement Learning 10、Deep Transfer Learning Lifelong Learning especially for RL 11、One Shot Deep Learning

这是我一开始入门深度学习时找到的最经典的几篇深度学习论文，是各个领域的大牛写的经典又通俗易懂的文章。看完这几篇，大概就能知道深度学习能做什么，以及大概发展过程了。英文原版论文看着地道，但是理解不方便，于是我费力把这几篇都翻译成了中文的。方便理解的多