
Consistency Regularization for Variational AutoEncoders
Variational autoencoders (VAEs) are a powerful approach to unsupervised...
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Deep Probabilistic Graphical Modeling
Probabilistic graphical modeling (PGM) provides a framework for formulat...
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Prescribed Generative Adversarial Networks
Generative adversarial networks (GANs) are a powerful approach to unsupe...
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The Dynamic Embedded Topic Model
Topic modeling analyzes documents to learn meaningful patterns of words....
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Topic Modeling in Embedding Spaces
Topic modeling analyzes documents to learn meaningful patterns of words....
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Reweighted Expectation Maximization
Training deep generative models with maximum likelihood remains a challe...
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Avoiding Latent Variable Collapse With Generative Skip Models
Variational autoencoders (VAEs) learn distributions of highdimensional ...
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Noisin: Unbiased Regularization for Recurrent Neural Networks
Recurrent neural networks (RNNs) are powerful models of sequential data....
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Augment and Reduce: Stochastic Inference for Large Categorical Distributions
Categorical distributions are ubiquitous in machine learning, e.g., in c...
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TopicRNN: A Recurrent Neural Network with LongRange Semantic Dependency
In this paper, we propose TopicRNN, a recurrent neural network (RNN)bas...
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Variational Inference via χUpper Bound Minimization
Variational inference (VI) is widely used as an efficient alternative to...
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Edward: A library for probabilistic modeling, inference, and criticism
Probabilistic modeling is a powerful approach for analyzing empirical in...
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Adji B. Dieng
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