开课单位--华盛顿大学

1
STEM-ming the Tide: Predicting STEM Attrition using Student Transcript Data[STEM 潮流:使用学生成绩单数据预测 STEM 流失]
   Lysia Li; Rohan Aras(华盛顿大学) STEM-ming the Tide: Predicting STEM Attrition using Student Transcript Data
热度:22

2
XGBoost: A Scalable Tree Boosting System[XGBoost:一个可扩展的树提升系统]
  Tianqi Chen(华盛顿大学) Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system cal...
热度:50

3
Principles of Very Large Scale Modeling[超大尺度建模原理]
  Pedro Domingos(华盛顿大学) ACM SIGKDD is pleased to announce that Pedro Domingos is the winner of its 2014 Innovation Award. He is recognized for his foundational research in da...
热度:27

4
Net2Net: Accelerating Learning via Knowledge Transfer[Net2Net:通过知识转移加速学习]
  Tianqi Chen(华盛顿大学) We introduce techniques for rapidly transferring the information stored in one neural net into another neural net. The main purpose is to accelerate t...
热度:30

5
Ten problems for the next 10 years[未来十年的十个问题]
  Pedro Domingos(华盛顿大学) Ten problems for the next 10 years.
热度:28

6
False Discovery Rate Controlled Heterogeneous Treatment Effect Detection for Online Controlled Experiments[在线控制实验中的错误发现率控制的异质处理效果检测]
  Yuxiang Xie(华盛顿大学) Online controlled experiments (a.k.a. A/B testing) have been used as the mantra for data-driven decision making on feature changing and product shippi...
热度:34

7
Statistical Predicate Invention[统计谓词的发明]
  Stanley Kok(华盛顿大学) We propose statistical predicate invention as a key problem for statistical relational learning. SPI is the problem of discovering new concepts, prope...
热度:28

8
STAN: Spatio-Temporal Attention Network for next Point-of-Interest Recommendation[STAN:用于下一个兴趣点推荐的时空注意网络]
  Yingtao Luo(华盛顿大学) STAN: Spatio-Temporal Attention Network for next Point-of-Interest Recommendation
热度:65

9
Online Mobile App Usage as an Indicator of Sleep Behavior and Job Performance[作为睡眠行为和工作表现指标的在线移动应用使用情况]
  Chunjong Park(华盛顿大学) Online Mobile App Usage as an Indicator of Sleep Behavior and Job Performance
热度:14

10
Commonsense Intelligence: Cracking the Longstanding Challenge in AI[常识智能:破解人工智能的长期挑战]
  Yejin Choi(华盛顿大学) Despite considerable advances in deep learning, AI remains to be narrow and brittle. One fundamental limitation is its lack of common sense: intuitive...
热度:25