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Found 11 papers, 6 papers with code
The Cambridge Law Corpus: A Dataset for Legal AI Research
We introduce the Cambridge Law Corpus (CLC), a dataset for legal AI research.
Probabilistic Embeddings with Laplacian Graph Priors
The proposed model enables incorporating graph side-information into static word embeddings.
Robust, Accurate Stochastic Optimization for Variational Inference
We consider the problem of fitting variational posterior approximations using stochastic optimization methods.
Unbiased estimator for the variance of the leave-one-out cross-validation estimator for a Bayesian normal model with fixed variance
We show that it is possible to construct an unbiased estimator considering a specific predictive performance measure and model.
Methodology
Uncertainty in Bayesian Leave-One-Out Cross-Validation Based Model Comparison
We show that it is possible that the problematic skewness of the error distribution, which occurs when the models make similar predictions, does not fade away when the data size grows to infinity in certain situations.
Methodology
Interpretable Word Embeddings via Informative Priors
Word embeddings have demonstrated strong performance on NLP tasks.
Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models
To scale non-parametric extensions of probabilistic topic models such as Latent Dirichlet allocation to larger data sets, practitioners rely increasingly on parallel and distributed systems.
Bayesian leave-one-out cross-validation for large data
Model inference, such as model comparison, model checking, and model selection, is an important part of model development.
Pólya Urn Latent Dirichlet Allocation: a doubly sparse massively parallel sampler
We conclude by comparing the performance of our algorithm with that of other approaches on well-known corpora.
DOLDA - a regularized supervised topic model for high-dimensional multi-class regression
Generating user interpretable multi-class predictions in data rich environments with many classes and explanatory covariates is a daunting task.
Sparse Partially Collapsed MCMC for Parallel Inference in Topic Models
We propose a parallel sparse partially collapsed Gibbs sampler and compare its speed and efficiency to state-of-the-art samplers for topic models on five well-known text corpora of differing sizes and properties.
