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Found 3 papers, 1 papers with code
Learning Manifold Dimensions with Conditional Variational Autoencoders
Although the variational autoencoder (VAE) and its conditional extension (CVAE) are capable of state-of-the-art results across multiple domains, their precise behavior is still not fully understood, particularly in the context of data (like images) that lie on or near a low-dimensional manifold.
Training Deep Generative Models via Auxiliary Supervised Learning
Deep generative modeling has long been viewed as a challenging unsupervised learning problem, partly due to the lack of labels and the high dimension of the data.
Graph Polish: A Novel Graph Generation Paradigm for Molecular Optimization
In this study, we present a novel molecular optimization paradigm, Graph Polish, which changes molecular optimization from the traditional "two-language translating" task into a "single-language polishing" task.
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