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Found 5 papers, 1 papers with code
Unbiased Learning of Deep Generative Models with Structured Discrete Representations
By composing graphical models with deep learning architectures, we learn generative models with the strengths of both frameworks.
Learning Consistent Deep Generative Models from Sparsely Labeled Data
We consider training deep generative models toward two simultaneous goals: discriminative classification and generative modeling using an explicit likelihood.
Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints
We develop a new framework for learning variational autoencoders and other deep generative models that balances generative and discriminative goals.
Prediction-Constrained Topic Models for Antidepressant Recommendation
Supervisory signals can help topic models discover low-dimensional data representations that are more interpretable for clinical tasks.
Prediction-Constrained Training for Semi-Supervised Mixture and Topic Models
Supervisory signals have the potential to make low-dimensional data representations, like those learned by mixture and topic models, more interpretable and useful.
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