Graph neural collaborative topic model for citation recommendation
Due to the overload of published scientific articles, citation recommendation has long been a
critical research problem for automatically recommending the most relevant citations of …
critical research problem for automatically recommending the most relevant citations of …
Learning deep sigmoid belief networks with data augmentation
Deep directed generative models are developed. The multi-layered model is designed by
stacking sigmoid belief networks, with sparsity-encouraging priors placed on the model …
stacking sigmoid belief networks, with sparsity-encouraging priors placed on the model …
Relational deep learning: A deep latent variable model for link prediction
Link prediction is a fundamental task in such areas as social network analysis, information
retrieval, and bioinformatics. Usually link prediction methods use the link structures or node …
retrieval, and bioinformatics. Usually link prediction methods use the link structures or node …
Warplda: a cache efficient o (1) algorithm for latent dirichlet allocation
Developing efficient and scalable algorithms for Latent Dirichlet Allocation (LDA) is of wide
interest for many applications. Previous work has developed an $ O (1) $ Metropolis …
interest for many applications. Previous work has developed an $ O (1) $ Metropolis …
Neural relational topic models for scientific article analysis
Topic modelling and citation recommendation of scientific articles are important yet
challenging research problems in scientific article analysis. In particular, the inference on …
challenging research problems in scientific article analysis. In particular, the inference on …
Max-margin nonparametric latent feature models for link prediction
Link prediction is a fundamental task in statistical network analysis. Recent advances have
been made on learning flexible nonparametric Bayesian latent feature models for link …
been made on learning flexible nonparametric Bayesian latent feature models for link …
Generative text convolutional neural network for hierarchical document representation learning
For document analysis, existing methods often resort to the document representation that
either discards the word order information or projects each word into a low-dimensional …
either discards the word order information or projects each word into a low-dimensional …
Deep relational topic modeling via graph poisson gamma belief network
To analyze a collection of interconnected documents, relational topic models (RTMs) have
been developed to describe both the link structure and document content, exploring their …
been developed to describe both the link structure and document content, exploring their …
Dirichlet process mixture of generalized inverted dirichlet distributions for positive vector data with extended variational inference
A Bayesian nonparametric approach for estimation of a Dirichlet process (DP) mixture of
generalized inverted Dirichlet distributions [ie, an infinite generalized inverted Dirichlet …
generalized inverted Dirichlet distributions [ie, an infinite generalized inverted Dirichlet …