图 书 馆开课单位--华盛顿大学
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1STEM-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
Lysia Li; Rohan Aras(华盛顿大学) STEM-ming the Tide: Predicting STEM Attrition using Student Transcript Data
热度：22
2XGBoost: 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：54
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：54
3Principles 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：30
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：30
4Net2Net: 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
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
5Ten 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
Pedro Domingos(华盛顿大学) Ten problems for the next 10 years.
热度：28
6False 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：35
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：35
7Statistical 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：29
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：29
8STAN: 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：66
Yingtao Luo(华盛顿大学) STAN: Spatio-Temporal Attention Network for next Point-of-Interest Recommendation
热度：66
9Online 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
Chunjong Park(华盛顿大学) Online Mobile App Usage as an Indicator of Sleep Behavior and Job Performance
热度：14
10Commonsense 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：26
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：26