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![PDF] Sliced-Wasserstein Autoencoder: An Embarrassingly Simple Generative Model | Semantic Scholar](https://d3i71xaburhd42.cloudfront.net/8ea9093542075bd8cc4928a4c671a95f363c61ef/5-Figure1-1.png "PDF] Sliced-Wasserstein Autoencoder: An Embarrassingly Simple Generative Model | Semantic Scholar")
PDF] Sliced-Wasserstein Autoencoder: An Embarrassingly Simple Generative Model | Semantic Scholar
Generative Autoencoders Beyond VAEs: (Sliced) Wasserstein Autoencoders · Notes on Artificial Intelligence
Sliced-Wasserstein Autoencoder: An Embarrassingly Simple Generative Model
Wasserstein variational autoencoders - Batı Şengül
Introduction to Deep Generative Models
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WASSERSTEIN AUTO-ENCODERS
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Tom Sercu on Twitter: "We want to generate new peptides (short proteins) which are active anti-microbials and not toxic. To start, we trained a Wasserstein Auto-Encoder on a large diverse set of
JACIII Vol.25 p.195 (2021) | Fuji Technology Press: academic journal publisher
The theory behind Latent Variable Models: formulating a Variational Autoencoder | AI Summer
Probabilistic Natural Language Generation with Wasserstein Autoencoders | DeepAI
Molecular Generative Model Based on an Adversarially Regularized Autoencoder | Journal of Chemical Information and Modeling
![PDF] Sliced-Wasserstein Autoencoder: An Embarrassingly Simple Generative Model | Semantic Scholar](https://d3i71xaburhd42.cloudfront.net/8ea9093542075bd8cc4928a4c671a95f363c61ef/12-Figure3-1.png "PDF] Sliced-Wasserstein Autoencoder: An Embarrassingly Simple Generative Model | Semantic Scholar")
PDF] Sliced-Wasserstein Autoencoder: An Embarrassingly Simple Generative Model | Semantic Scholar
Sliced-Wasserstein Autoencoder: An Embarrassingly Simple Generative Model | DeepAI
Generative Autoencoders Beyond VAEs: (Sliced) Wasserstein Autoencoders · Notes on Artificial Intelligence
GANs vs. Autoencoders: Comparison of Deep Generative Models | by Matthew Stewart | Towards Data Science
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