图 书 馆开课单位--微软公司
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101102Scalable Influence Maximization for Prevalent Viral Marketing in Large-Scale Social Networks[大规模社交网络中流行病毒营销的可扩展影响最大化]
Wei Chen(微软公司) Influence maximization, defined by Kempe, Kleinberg, and Tardos (2003), is the problem of finding a small set of seed nodes in a social network that m...
热度：69
Wei Chen(微软公司) Influence maximization, defined by Kempe, Kleinberg, and Tardos (2003), is the problem of finding a small set of seed nodes in a social network that m...
热度：69
103Empirical Comparisons of Learning Methods & Case Studies[学习方法与案例研究的实证比较]
Rich Caruana(微软公司) Decision trees may be intelligible, but can they cut the mustard? Have SVMs replaced neural nets, or are neural nets still best for regression, and SV...
热度：56
Rich Caruana(微软公司) Decision trees may be intelligible, but can they cut the mustard? Have SVMs replaced neural nets, or are neural nets still best for regression, and SV...
热度：56
104Scalable Tensor Decompositions for Learning Hidden Variable Models[用于学习隐藏变量模型的可扩展张量分解]
Sham M. Kakade(微软公司) In many applications, we face the challenge of modeling the interactions between multiple observations. A popular and successful approach in machine l...
热度：38
Sham M. Kakade(微软公司) In many applications, we face the challenge of modeling the interactions between multiple observations. A popular and successful approach in machine l...
热度：38
105Latent Variable Sparse Bayesian Models[潜在变量稀疏贝叶斯模型]
David P Wipf(微软公司) A variety of practical approaches have recently been introduced for performing estimation and inference using linear models with sparse priors on the ...
热度：85
David P Wipf(微软公司) A variety of practical approaches have recently been introduced for performing estimation and inference using linear models with sparse priors on the ...
热度：85
106One-Handed Touchscreen Input for Legacy Applications[用于传统应用的单手触摸屏输入]
Amy K. Karlson(微软公司) We present two controlled studies aimed at understanding therelative tradeoffs that five different input methods (includingdirect and indirect) offer ...
热度：59
Amy K. Karlson(微软公司) We present two controlled studies aimed at understanding therelative tradeoffs that five different input methods (includingdirect and indirect) offer ...
热度：59
107Contextual Bandits with Similarity Information[具有相似信息的语境错误]
Aleksandrs Slivkins(微软公司) In a multi-armed bandit (MAB) problem, an online algorithm makes a sequence of choices. In each round it chooses from a time-invariant set of alternat...
热度：41
Aleksandrs Slivkins(微软公司) In a multi-armed bandit (MAB) problem, an online algorithm makes a sequence of choices. In each round it chooses from a time-invariant set of alternat...
热度：41
108Less is More: Sampling the Neighborhood Graph Makes SALSA Better and Faster[少即是多：采样邻域图使SalSA更好更快]
Sreenivas Gollapudi;Rina Panigrahy; Marc Najork(微软公司) Less is More: Sampling the Neighborhood Graph Makes SALSA Better and Faster
热度：24
Sreenivas Gollapudi;Rina Panigrahy; Marc Najork(微软公司) Less is More: Sampling the Neighborhood Graph Makes SALSA Better and Faster
热度：24
109Graphical Models and Variational Methods[图形模型和变分方法]
Christopher Bishop(微软公司) In this course I will discuss how exponential families, a standard tool in statistics, can be used with great success in machine learning to unify man...
热度：56
Christopher Bishop(微软公司) In this course I will discuss how exponential families, a standard tool in statistics, can be used with great success in machine learning to unify man...
热度：56
110Probabilistic Inference and Differential Privacy[概率推理与差异隐私]
Frank McSherry(微软公司) We identify and investigate a strong connection between probabilistic inference and differential privacy, the latter being a recent privacy definition...
热度：61
Frank McSherry(微软公司) We identify and investigate a strong connection between probabilistic inference and differential privacy, the latter being a recent privacy definition...
热度：61