If you are a newcomer to the Deep Learning area, the first question you may have is "Which paper should I start reading from?"
Here is a reading roadmap of Deep Learning papers!
I would continue adding papers to this roadmap.
[0] Bengio, Yoshua, Ian J. Goodfellow, and Aaron Courville. "Deep learning." An MIT Press book in preparation. Draft chapters available at http://www. iro. umontreal. ca/∼ bengioy/dlbook (2015).[pdf] (Deep Learning Bible)
[1] LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." Nature 521.7553 (2015): 436-444. [pdf] (Three Giants' Survey) ⭐⭐⭐⭐⭐
[2] Hinton, Geoffrey E., Simon Osindero, and Yee-Whye Teh. "A fast learning algorithm for deep belief nets." Neural computation 18.7 (2006): 1527-1554.[pdf](Deep Learning Eve) ⭐⭐⭐
[3] Hinton, Geoffrey E., and Ruslan R. Salakhutdinov. "Reducing the dimensionality of data with neural networks." Science 313.5786 (2006): 504-507. [pdf] (Milestone, Show the promise of deep learning) ⭐⭐⭐
[4] Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012. [pdf] (AlexNet, Deep Learning Breakthrough) ⭐⭐⭐⭐⭐
[5] Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014).[pdf] (VGGNet,Neural Networks become very deep!) ⭐⭐⭐
[6] Szegedy, Christian, et al. "Going deeper with convolutions." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.[pdf] (GoogLeNet) ⭐⭐⭐
[7] He, Kaiming, et al. "Deep residual learning for image recognition." arXiv preprint arXiv:1512.03385 (2015).[pdf] (ResNet,Very very deep networks) ⭐⭐⭐⭐⭐