图 书 馆开课单位--剑桥大学
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71Accelerated Gibbs Sampling for the Indian Buffet Process [为印度自助餐流程加速吉布斯抽样]
Finale Doshi(剑桥大学) We often seek to identify co-occurring hidden features in a set of observations. The Indian Buffet Process (IBP) provides a nonparametric prior on the...
热度：142
Finale Doshi(剑桥大学) We often seek to identify co-occurring hidden features in a set of observations. The Indian Buffet Process (IBP) provides a nonparametric prior on the...
热度：142
72Poster: Dirichlet Process Mixture Models for Verb Clustering [海报：用于动词聚类的Dirichlet过程混合模型]
Andreas Vlachos(剑桥大学) In this work we apply Dirichlet Process Mixture Models to a learning task in natural language processing (NLP): lexical-semantic verb clustering. We a...
热度：29
Andreas Vlachos(剑桥大学) In this work we apply Dirichlet Process Mixture Models to a learning task in natural language processing (NLP): lexical-semantic verb clustering. We a...
热度：29
73Beam Sampling for the Infinite Hidden Markov Model[无限隐马尔可夫模型的光束采样]
Jurgen Van Gael(剑桥大学) The infinite hidden Markov model is a nonparametric extension of the widely used hidden Markov model. Our paper introduces a new inference algorithm f...
热度：62
Jurgen Van Gael(剑桥大学) The infinite hidden Markov model is a nonparametric extension of the widely used hidden Markov model. Our paper introduces a new inference algorithm f...
热度：62
74Gaussian Process Basics[高斯过程基础]
David MacKay(剑桥大学) How on earth can a plain old Gaussian distribution be useful for sophisticated regression and machine learning tasks?
热度：135
David MacKay(剑桥大学) How on earth can a plain old Gaussian distribution be useful for sophisticated regression and machine learning tasks?
热度：135