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Consistency Regularization for Variational Auto-Encoders


May 31, 2021
Samarth Sinha, Adji B. Dieng


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Deep Probabilistic Graphical Modeling


Apr 25, 2021
Adji B. Dieng

* This thesis was defended in April 2020 and accepted without revision. The author received her PhD in Statistics from Columbia University on May 20, 2020 

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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 $Ο‡$-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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