图 书 馆开课单位--康奈尔大学
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41Influence and Passivity in Social Media[社会化媒介中的影响与被动]
Romero Daniel M(康奈尔大学) The ever-increasing amount of information owing through Social Media forces the members of these networks to compete for attention and influence by re...
热度：83
Romero Daniel M(康奈尔大学) The ever-increasing amount of information owing through Social Media forces the members of these networks to compete for attention and influence by re...
热度：83
4243Scientific Data Mining: Distilling Free-Form Natural Laws from Experimental Data[科学数据挖掘:从实验数据中提炼出自由形式的自然规律]
Hod Lipson(康奈尔大学) For centuries, scientists have attempted to identify and document analytical laws that underlie physical phenomena in nature. Despite the prevalence o...
热度：50
Hod Lipson(康奈尔大学) For centuries, scientists have attempted to identify and document analytical laws that underlie physical phenomena in nature. Despite the prevalence o...
热度：50
44Two-on-two robot soccer[二对二机器人足球]
Raffaello D'Andrea, Oliver Purwin, Keyong Li(康奈尔大学) A short demo of two-on-two robotic soccer, featuring the Cornell team's legacy and current players/robots.
热度：43
Raffaello D'Andrea, Oliver Purwin, Keyong Li(康奈尔大学) A short demo of two-on-two robotic soccer, featuring the Cornell team's legacy and current players/robots.
热度：43
45A New Algorithm for Compressed Counting with Applications in Shannon Entropy Estimation in Dynamic Data [一种新的压缩计数算法及其在动态数据Shannon熵估计中的应用]
Ping Li(康奈尔大学) In this paper, we propose a new accurate algorithm for Compressed Counting, whose sample complexity is only O (1/v2) for v-additive Shannon entropy es...
热度：28
Ping Li(康奈尔大学) In this paper, we propose a new accurate algorithm for Compressed Counting, whose sample complexity is only O (1/v2) for v-additive Shannon entropy es...
热度：28
46Mining experimental data for dynamical invariants - from cognitive robotics to computational biology[为动态不变量挖掘实验数据——从认知机器人到计算生物学]
Hod Lipson(康奈尔大学 ) For centuries, scientists have attempted to identify and document analytical laws that underlie physical phenomena in nature. Despite the prevalence o...
热度：50
Hod Lipson(康奈尔大学 ) For centuries, scientists have attempted to identify and document analytical laws that underlie physical phenomena in nature. Despite the prevalence o...
热度：50
47Online Learning with Implicit User Preferences[具有内隐用户偏好的在线学习 ]
Thorsten Joachims(康奈尔大学) Many systems, ranging from search engines to smart homes, aim to continually improve the utility they are providing to their users. While clearly a ma...
热度：29
Thorsten Joachims(康奈尔大学) Many systems, ranging from search engines to smart homes, aim to continually improve the utility they are providing to their users. While clearly a ma...
热度：29
48Accelerated Adaptive Markov Chain for Partition Function Computation[加速自适应马尔可夫链配分函数计算]
Stefano Ermon(康奈尔大学) We propose a novel Adaptive Markov Chain Monte Carlo algorithm to compute the partition function. In particular, we show how to accelerate a flat hist...
热度：34
Stefano Ermon(康奈尔大学) We propose a novel Adaptive Markov Chain Monte Carlo algorithm to compute the partition function. In particular, we show how to accelerate a flat hist...
热度：34
49Relax and Randomize: From Value to Algorithms[松弛和随机化：从值到算法]
Karthik Sridharan(康奈尔大学) We show a principled way of deriving online learning algorithms from a minimax analysis. Various upper bounds on the minimax value, previously thought...
热度：88
Karthik Sridharan(康奈尔大学) We show a principled way of deriving online learning algorithms from a minimax analysis. Various upper bounds on the minimax value, previously thought...
热度：88
50Copula Processes[Copula过程]
Andrew Gordon Wilson(康奈尔大学) We define a copula process which describes the dependencies between arbitrarily many random variables independently of their marginal distributions. A...
热度：82
Andrew Gordon Wilson(康奈尔大学) We define a copula process which describes the dependencies between arbitrarily many random variables independently of their marginal distributions. A...
热度：82