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Found 3 papers, 1 papers with code
Nonparametric Topic Modeling with Neural Inference
Moreover, we also propose HiTM-VAE, where the document-specific topic distributions are generated in a hierarchical manner.
A Bayesian Nonparametric Topic Model with Variational Auto-Encoders
On the other hand, different with the other BNP topic models, the inference of iTM-VAE is modeled by neural networks, which has rich representation capacity and can be computed in a simple feed-forward manner.
Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering
In this paper, we propose Variational Deep Embedding (VaDE), a novel unsupervised generative clustering approach within the framework of Variational Auto-Encoder (VAE).
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