图 书 馆开课单位--微软
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11Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization[强凸随机优化的梯度下降最优化]
Ohad Samir(微软) Stochastic gradient descent (SGD) is a simple and popular method to solve stochastic optimization problems which arise in machine learning. For strong...
热度：20
Ohad Samir(微软) Stochastic gradient descent (SGD) is a simple and popular method to solve stochastic optimization problems which arise in machine learning. For strong...
热度：20
12Large-Scale Behavioral Targeting[大规模行为目标]
Ye Chen(微软) Best Application Paper Award Winner Behavioral targeting (BT) leverages historical user behavior to select the ads most relevant to users to display. ...
热度：24
Ye Chen(微软) Best Application Paper Award Winner Behavioral targeting (BT) leverages historical user behavior to select the ads most relevant to users to display. ...
热度：24
13Software Breakthroughs: Solving the Toughest Problems in Computer Science[软件突破：解决计算机科学中最棘手的问题]
William H. Gates(微软) Bill Gates’ talk at MIT provided an optimistic view of the next generation of computer science, now that the “rough draft” is done. ...
热度：26
William H. Gates(微软) Bill Gates’ talk at MIT provided an optimistic view of the next generation of computer science, now that the “rough draft” is done. ...
热度：26
14Data Mining in the Online Services Industry[在线服务行业中的数据挖掘]
Qi Lu(微软) The online services industry is a rapidly growing industry with a worldwide online ad market projected to grow from $48 billion in 2011 to $67 billion...
热度：25
Qi Lu(微软) The online services industry is a rapidly growing industry with a worldwide online ad market projected to grow from $48 billion in 2011 to $67 billion...
热度：25
15From Code to Data: AI at Scale for Developer Productivity[从代码到数据：开发人员生产力的大规模人工智能]
Neel Sundaresan(微软) The last decade has seen three great phenomena in computing – the rebirth of AI algorithms and AI hardware; the evolution of cloud computing and...
热度：28
Neel Sundaresan(微软) The last decade has seen three great phenomena in computing – the rebirth of AI algorithms and AI hardware; the evolution of cloud computing and...
热度：28
16Pythia: AI-assisted Code Completion System[Pythia：人工智能辅助代码完成系统]
Alexey Svyatkovskiy(微软) In this paper, we propose a novel end-to-end approach for AI-assisted code completion called Pythia. It generates ranked lists of method and API recom...
热度：31
Alexey Svyatkovskiy(微软) In this paper, we propose a novel end-to-end approach for AI-assisted code completion called Pythia. It generates ranked lists of method and API recom...
热度：31
17Ivy Barley, Co-Founder, Developers in Vogue[常春藤·巴利，《时尚》开发商联合创始人]
Ivy Barley(微软) Ivy Barley, Co-Founder, Developers in Vogue
热度：22
Ivy Barley(微软) Ivy Barley, Co-Founder, Developers in Vogue
热度：22
18Windows Azure: Microsoft’s cloud approach[Microsoft的云方法]
Gerd Olsowsky-Klein(微软) Windows Azure: Microsoft’s cloud approach
热度：28
Gerd Olsowsky-Klein(微软) Windows Azure: Microsoft’s cloud approach
热度：28
19Match Plan Generation in Web Search with Parameterized Action Reinforcement Learning[基于参数化动作强化学习的Web搜索匹配计划生成]
Ziyan Luo(微软) Match Plan Generation in Web Search with Parameterized Action Reinforcement Learning
热度：27
Ziyan Luo(微软) Match Plan Generation in Web Search with Parameterized Action Reinforcement Learning
热度：27
20Knowledge-Aware Procedural Text Understanding with Multi-Stage Training[基于知识的多阶段训练程序文本理解]
Zhihan Zhang(微软) Knowledge-Aware Procedural Text Understanding with Multi-Stage Training
热度：33
Zhihan Zhang(微软) Knowledge-Aware Procedural Text Understanding with Multi-Stage Training
热度：33