most citedMHSAN: Multi-Head Self-Attention Network for Visual Semantic Embedding

3 citations · 7 across the 4 of their papers we have counts for

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cs.LG2020★ 1 cited

Variational Mutual Information Maximization Framework for VAE Latent Codes with Continuous and Discrete Priors

Andriy Serdega, Dae-Shik Kim

Learning interpretable and disentangled representations of data is a key topic in machine learning research. Variational Autoencoder (VAE) is a scalable method for learning directe…

cs.LG2020★ 1 cited

VMI-VAE: Variational Mutual Information Maximization Framework for VAE With Discrete and Continuous Priors

Andriy Serdega, Dae-Shik Kim

Variational Autoencoder is a scalable method for learning latent variable models of complex data. It employs a clear objective that can be easily optimized. However, it does not ex…