16 datasets found

Hierarchical Dirichlet Process (HDP) mixture model for modeling the hierarchy of groups of data.

The NOISE dataset is a semi-synthetic dataset constructed from the matrix A∗, where the data is generated from y = A∗x + ζ, where ζ is the noise.

The NEG dataset is a semi-synthetic dataset constructed from the matrix A∗, where the entries of A∗ are i.i.d. samples from the uniform distribution on [−0.5, 0.5).

The CTM dataset is a semi-synthetic dataset constructed from the matrix X, whose columns are drawn from the logistic normal prior in the correlated topic model.

The DIR dataset is a semi-synthetic dataset constructed from the matrix X, whose columns are from a Dirichlet distribution with parameters (0.05, 0.05,..., 0.05).

Enrico: A dataset for topic modeling of mobile UI designs

The paper discusses the use of multi-objective Bayesian optimization for hyperparameter transfer in topic models.

The paper discusses the use of multi-objective Bayesian optimization for hyperparameter transfer in topic models.

The paper discusses the use of multi-objective Bayesian optimization for hyperparameter transfer in topic models.

The Graphbtm dataset is a biterm topic model.

The RCV1 dataset is a corpus of Reuters news articles.

The dataset used in this paper is a collection of news articles from an international news website, covering a time span from September 2012 to April 2014.

The paper discusses the use of multi-objective Bayesian optimization for hyperparameter transfer in topic models.

Multilingual dataset for topic modeling based on aligned Wikipedia articles extracted from Wikipedia Comparable Corpora

The dataset used in this work is a custom synthetic dataset generated using the liquid-dsp library, containing 600000 examples of each of 13.8 million examples, with SNRs...

The Subjectivity dataset is a dataset provided by [Pang and Lee, 2004].