Recent work on variational autoencoders (VAEs) has enabled the development of
generative topic models using neural networks. Topic models based on latent Dirichlet …

Word meaning changes over time, depending on linguistic and extra-linguistic factors.
Associating a word's correct meaning in its historical context is a central challenge in …

In the probabilistic topic models, the quantity of interest—a lowrank matrix consisting of topic
vectors—is hidden in the text corpus matrix, masked by noise, and Singular Value …

Latent Dirichlet allocation model (LDA) has been widely used in topic modeling. Recent
works have shown the effectiveness of integrating neural network mechanisms with this …

Topic models and all their variants analyse text by learning meaningful representations
through word co-occurrences. As pointed out by previous work, such models implicitly …

Abstract The Sketched Wasserstein Distance (WS) is a new probability distance specifically
tailored to finite mixture distributions. Given any metric d defined on a set A of probability …

The classical low rank approximation problem is to find a rank $ k $ matrix $ UV $(where $ U
$ has $ k $ columns and $ V $ has $ k $ rows) that minimizes the Frobenius norm of $ A-UV …

Topic models provide a useful text-mining tool for learning, extracting, and discovering latent
structures in large text corpora. Although a plethora of methods have been proposed for …

Discovering intricate dependencies between topics in topic modeling is challenging due to
the noisy and incomplete nature of real-world data and the inherent complexity of topic …

Change and its precondition, variation, are inherent in languages. Over time, new words
enter the lexicon, others become obsolete, and existing words acquire new senses …