Deep Learning > Papers

and

. Also, after this list comes out, another awesome list for deep learning beginners, called

, has been created and loved by many deep learning researchers. Although the Roadmap List includes lots of important deep learning papers, it feels overwhelming for me to read them all. As I mentioned in the introduction, I believe that seminal works can give us lessons regardless of their application domain. Thus, I would like to introduce top 100 deep learning papers here as a good starting point of overviewing deep learning researches. To get the news for newly released papers everyday, follow my twitter or facebook page!

(2015), S. Ren et al.

(2015), T. Lillicrap et al. [[pdf]](https://arxiv.org/pdf/1509.02971)

(2013), V. Mnih et al. [[pdf]](http://arxiv.org/pdf/1312.5602.pdf))

(2017), K. He et al. [[pdf]](https://128.84.21.199/pdf/1703.06870)

A Generative Model for Raw Audio

visual manipulation on the natural image manifold

image colorization

Feed-forward synthesis of textures and stylized images

Single shot multibox detector

AlexNet-level accuracy with 50x fewer parameters and< 1MB model size

Efficient inference engine on compressed deep neural network

Training deep neural networks with weights and activations constrained to+ 1 or-1

memory networks for visual and textual question answering

attention networks for image question answering

computing using a neural network with dynamic external memory

neural machine translation system: Bridging the gap between human and machine translation