The explosive growth in data volume and the availability of cheap computing resources
have sparked increasing interest in Big learning, an emerging subfield that studies scalable …

In functional data analysis, curves or surfaces are observed, up to measurement error, at a
finite set of locations, for, say, a sample of n individuals. Often, the curves are homogeneous …

We introduce two Bayesian models for unsupervised semantic role labeling (SRL) task. The
models treat SRL as clustering of syntactic signatures of arguments with clusters …

随着大数据的快速发展, 以概率统计为基础的机器学习在近年来受到工业界和学术界的极大关注
, 并在视觉, 语音, 自然语言, 生物等领域获得很多重要的成功应用, 其中贝叶斯方法在过去20 …

The distance dependent Chinese restaurant process (ddCRP) was recently introduced to
accommodate random partitions of non-exchangeable data. The ddCRP clusters data in a …

We present the discrete infinite logistic normal distribution (DILN,“Dylan”), a Bayesian
nonparametric prior for mixed membership models. DILN is a generalization of the …

The application of machine learning (ML) algorithms for processing remote sensing data is
momentous, particularly for mapping hydrothermal alteration zones associated with …

A logistic stick-breaking process (LSBP) is proposed for non-parametric clustering of general
spatially-or temporally-dependent data, imposing the belief that proximate data are more …

The foundation of the subject of nonparametric Bayesian inference was laid in two technical
reports: a 1969 UCLA report by Thomas S. Ferguson (later published in 1973 as a paper in …

This paper describes research that seeks to supersede human inductive learning and
reasoning in high-level scene understanding and content extraction. Searching for relevant …