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?

  1. 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。
  2. 强化学习
  3. Meta-Learning and One-Shot learning
  4. GANS
  5. Bayesian NNs are area of active research
  6. Fairness in ML
  7. Explainable ML
  8. 加速SGD的tricks
  9. 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/

转载于:https://www.cnblogs.com/theodoric008/p/8046202.html

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