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05/31/2021
Consistency Regularization for Variational Auto-Encoders
Variational auto-encoders (VAEs) are a powerful approach to unsupervised...
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04/25/2021
Deep Probabilistic Graphical Modeling
Probabilistic graphical modeling (PGM) provides a framework for formulat...
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10/09/2019
Prescribed Generative Adversarial Networks
Generative adversarial networks (GANs) are a powerful approach to unsupe...
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07/12/2019
The Dynamic Embedded Topic Model
Topic modeling analyzes documents to learn meaningful patterns of words....
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07/08/2019
Topic Modeling in Embedding Spaces
Topic modeling analyzes documents to learn meaningful patterns of words....
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06/13/2019
Reweighted Expectation Maximization
Training deep generative models with maximum likelihood remains a challe...
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07/12/2018
Avoiding Latent Variable Collapse With Generative Skip Models
Variational autoencoders (VAEs) learn distributions of high-dimensional ...
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05/03/2018
Noisin: Unbiased Regularization for Recurrent Neural Networks
Recurrent neural networks (RNNs) are powerful models of sequential data....
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02/12/2018
Augment and Reduce: Stochastic Inference for Large Categorical Distributions
Categorical distributions are ubiquitous in machine learning, e.g., in c...
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11/05/2016
TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency
In this paper, we propose TopicRNN, a recurrent neural network (RNN)-bas...
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11/01/2016
Variational Inference via χ-Upper Bound Minimization
Variational inference (VI) is widely used as an efficient alternative to...
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10/31/2016
