딥 러닝

introduction

Deep learning algorithms have been shown to perform extremely well on many classical machine learning problems.

Jakub Sygnowski, Henryk Michalewski, Learning from the memory of Atari 2600, arXiv:1605.01335 [cs.LG], May 04 2016, http://arxiv.org/abs/1605.01335
Kenny Young, Ryan Hayward, Gautham Vasan, Neurohex: A Deep Q-learning Hex Agent, arXiv:1604.07097 [cs.AI], April 24 2016, http://arxiv.org/abs/1604.07097
Xiaoxiao Guo, Satinder Singh, Richard Lewis, Honglak Lee, Deep Learning for Reward Design to Improve Monte Carlo Tree Search in ATARI Games, arXiv:1604.07095 [cs.AI], April 24 2016, http://arxiv.org/abs/1604.07095
Luke de Oliveira, Michael Kagan, Lester Mackey, Benjamin Nachman, Ariel Schwartzman, Jet-Images -- Deep Learning Edition, arXiv:1511.05190[hep-ph], November 16 2015, http://arxiv.org/abs/1511.05190v2
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, Ananthram Swami, Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks, http://arxiv.org/abs/1511.04508v2
https://github.com/kuz/DeepMind-Atari-Deep-Q-Learner
https://brunch.co.kr/@justinleeanac/2
http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html
Silver, David, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, et al. “Mastering the Game of Go with Deep Neural Networks and Tree Search.” Nature 529, no. 7587 (January 28, 2016): 484–89. doi:10.1038/nature16961.

Kristinn R. Thórisson, Jordi Bieger, Thröstur Thorarensen, Jóna S. Sigurðardóttir, Bas R. Steunebrink, Why Artificial Intelligence Needs a Task Theory --- And What It Might Look Like, arXiv:1604.04660 [cs.AI], April 15 2016, http://arxiv.org/abs/1604.04660