A Top-Down Binary Hierarchical Topic Model for Biomedical Literature

A Hierarchical Bayesian Model for Topic Segmentation

Many streams of real-world data, such as conversations or body movements, consist of relatively coherent segments, each characterized by particular topics or controllers. Making sense of these data requires simultaneously segmenting the sequences and inferring the structure of the segments. We present a hierarchical Bayesian model that can be used to break a sequence of utterances or movements ...

A Top-Down Model for Neuronal Synchronization

Hierarchical whitespace allocation in top-down placement

Increased transistor density in modern commercial ICs typically originates in new manufacturing and defect prevention technologies [15], [16]. Additionally, better utilization of such low-level transistor density may result from improved software that makes fewer assumptions about physical layout in order to reliably automate the design process. In particular, recent layouts tend to have large ...

Hierarchical Whitespace Allocation in Top - Down Placement

Increased transistor density in modern commercial ICs typically originates in new manufacturing and defect prevention technologies [15], [16]. Additionally, better utilization of such low-level transistor density may result from improved software that makes fewer assumptions about physical layout in order to reliably automate the design process. In particular, recent layouts tend to have large ...

Another Hierarchical Topic Model

We describe a hierarchical topic model. We assume that there are various levels of specificity in a document collection. For example, a collection of mailing list posts might be organized according to sentence, paragraph, post and thread. We describe a model that captures the structure at each level of the hierarchy. We use a trace norm penalty on a matrix composed of natural parameters for the...