Search Results for author: Adji B. Dieng

Found 14 papers, 7 papers with code

Deep Probabilistic Graphical Modeling

no code implementations25 Apr 2021 Adji B. Dieng

We develop reweighted expectation maximization, an algorithm that unifies several existing maximum likelihood-based algorithms for learning models parameterized by neural networks.

Topic Models

Topic Modeling in Embedding Spaces

11 code implementations TACL 2020 Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei

To this end, we develop the Embedded Topic Model (ETM), a generative model of documents that marries traditional topic models with word embeddings.

Topic Models Variational Inference +1

Reweighted Expectation Maximization

1 code implementation13 Jun 2019 Adji B. Dieng, John Paisley

The typical workaround is to use variational inference (VI) and maximize a lower bound to the log marginal likelihood of the data.

Bayesian Inference Density Estimation +1

Learning with Reflective Likelihoods

no code implementations27 Sep 2018 Adji B. Dieng, Kyunghyun Cho, David M. Blei, Yann Lecun

Furthermore, the reflective likelihood objective prevents posterior collapse when used to train stochastic auto-encoders with amortized inference.

Attribute

Avoiding Latent Variable Collapse With Generative Skip Models

no code implementations12 Jul 2018 Adji B. Dieng, Yoon Kim, Alexander M. Rush, David M. Blei

VAEs can capture complex distributions, but they can also suffer from an issue known as "latent variable collapse," especially if the likelihood model is powerful.

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

1 code implementation5 Nov 2016 Adji B. Dieng, Chong Wang, Jianfeng Gao, John Paisley

The proposed TopicRNN model integrates the merits of RNNs and latent topic models: it captures local (syntactic) dependencies using an RNN and global (semantic) dependencies using latent topics.

Language Modelling Sentiment Analysis +1

Variational Inference via $χ$-Upper Bound Minimization

no code implementations1 Nov 2016 Adji B. Dieng, Dustin Tran, Rajesh Ranganath, John Paisley, David M. Blei

In this paper we propose CHIVI, a black-box variational inference algorithm that minimizes $D_{\chi}(p || q)$, the $\chi$-divergence from $p$ to $q$.

Variational Inference

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