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Prescribed Generative Adversarial Networks

Oct 09, 2019
Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei, Michalis K. Titsias

* Code for this paper can be found at https://github.com/adjidieng/PresGANs 

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The Dynamic Embedded Topic Model

Jul 12, 2019
Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei


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Topic Modeling in Embedding Spaces

Jul 08, 2019
Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei

* Code can be found at https://github.com/adjidieng/ETM 

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Reweighted Expectation Maximization

Jun 13, 2019
Adji B. Dieng, John Paisley

* Code can be found at https://github.com/adjidieng/REM 

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Noisin: Unbiased Regularization for Recurrent Neural Networks

Jul 13, 2018
Adji B. Dieng, Rajesh Ranganath, Jaan Altosaar, David M. Blei

* In Proceedings of the International Conference on Machine Learning, 2018 

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Avoiding Latent Variable Collapse With Generative Skip Models

Jul 12, 2018
Adji B. Dieng, Yoon Kim, Alexander M. Rush, David M. Blei

* Presented at Workshop on Theoretical Foundations and Applications of Deep Generative Models, ICML, 2018 

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Augment and Reduce: Stochastic Inference for Large Categorical Distributions

Jun 07, 2018
Francisco J. R. Ruiz, Michalis K. Titsias, Adji B. Dieng, David M. Blei

* Francisco J. R. Ruiz, Michalis K. Titsias, Adji B. Dieng, and David M. Blei. Augment and Reduce: Stochastic Inference for Large Categorical Distributions. International Conference on Machine Learning. Stockholm (Sweden), July 2018 
* 11 pages, 2 figures 

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Variational Inference via $χ$-Upper Bound Minimization

Nov 12, 2017
Adji B. Dieng, Dustin Tran, Rajesh Ranganath, John Paisley, David M. Blei

* Neural Information Processing Systems, 2017 

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TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency

Feb 27, 2017
Adji B. Dieng, Chong Wang, Jianfeng Gao, John Paisley

* International Conference on Learning Representations 

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Edward: A library for probabilistic modeling, inference, and criticism

Feb 01, 2017
Dustin Tran, Alp Kucukelbir, Adji B. Dieng, Maja Rudolph, Dawen Liang, David M. Blei


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