NIPS 2017 — notes and thoughts 个人笔记
只写一下自己觉得很有趣的部分
keytrends 今年流行啥？
- 深度学习还是很火。视觉图像等有了很多处理应用。多数是CNN的变种，比较牛逼的有Capsule Networks 和 WaveNet。
- 强化学习
- Meta-Learning and One-Shot learning
- GANS
- Bayesian NNs are area of active research
- Fairness in ML
- Explainable ML
- 加速SGD的tricks
- 图学习
关于机器学习的可解释性的思考？试图了解ML
有趣的项目/文章
- 强化学习买机票
- ML in credit, education, employment, housing, marketing verticals 机器学习用于信用，教育，就业，住宅，市场领域
- Powering the next 100 years/John Platt 展望未来100年
- Reprogramming Human Genome 对人类基因重新编程/编辑
- The Trouble with Bias 关于Biases的一些思考和讨论
- A Unified Approach To Interpreting Model Predictions 解释模型预测机制的一种统一方法，用MINIST，有git源码，非常直观
- Towards Accurate Binary Convolutional Neural Network (ABC-Net)
Authors claim that this is the first time a binary neural network achieves prediction accuracy
comparable to its full-precision counterpart on ImageNet. - The Unreasonable Effectiveness of Structure 结构的不可思议的牛逼之处
- Engineering and Reverse-Engineering Intelligence Using Probabilistic Programs, Program Induction, and Deep Learning
- Attention is all you need 关于机器翻译
- A simple neural network module for relational reasoning 问答
- Train longer, generalize better: closing the generalization gap in large batch training of neural networks 加速SGD
- Deep Learning for Robotics 深度学习用在机器人上
- Learning State Representations
“Shallow learning, deep representations” – sophisticated learning of problem representation makes learning task simple
浅层学习，但是深度表示。问题表示很复杂，学起来很容易？ - AlphaZero – mastering games without human knowledge
压轴戏，AlphaZero不需要人类知识就掌握了游戏？这篇文章详细地介绍了Zero是怎么做到的。
原文在这里：
https://olgalitech.wordpress.com/2017/12/12/nips-2017-notes-and-thoughs/
