Deep Learning Papers Reading Roadmap

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!

The roadmap is constructed in accordance with the following four guidelines:

From outline to detail
From old to state-of-the-art
from generic to specific areas
focus on state-of-the-art

You will find many papers that are quite new but really worth reading.

I would continue adding papers to this roadmap.

1 Deep Learning History and Basics

1.0 Book

[0] Bengio, Yoshua, Ian J. Goodfellow, and Aaron Courville. "Deep learning." An MIT Press book. (2015). [html] (Deep Learning Bible, you can read this book while reading following papers.) ⭐⭐⭐⭐⭐

1.1 Survey

[1] LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." Nature 521.7553 (2015): 436-444. [pdf] (Three Giants' Survey) ⭐⭐⭐⭐⭐

1.2 Deep Belief Network(DBN)(Milestone of Deep Learning Eve)

[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) ⭐⭐⭐

1.3 ImageNet Evolution（Deep Learning broke out from here）

[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.