1 code implementation • NeurIPS 2021 • Samarth Sinha, Adji B. Dieng
In this paper, we propose a regularization method to enforce consistency in VAEs.
Ranked #1 on Image Generation on Binarized MNIST
no code implementations • 25 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.
2 code implementations • 9 Oct 2019 • Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei, Michalis K. Titsias
Generative adversarial networks (GANs) are a powerful approach to unsupervised learning.
Ranked #2 on Image Generation on Stacked MNIST
1 code implementation • 12 Jul 2019 • Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei
Topic modeling analyzes documents to learn meaningful patterns of words.
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.
Ranked #4 on Topic Models on AG News
1 code implementation • 13 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.
no code implementations • 27 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.
no code implementations • 12 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.
no code implementations • ICML 2018 • Adji B. Dieng, Rajesh Ranganath, Jaan Altosaar, David M. Blei
On the Penn Treebank, the method with Noisin more quickly reaches state-of-the-art performance.
1 code implementation • ICML 2018 • Francisco J. R. Ruiz, Michalis K. Titsias, Adji B. Dieng, David M. Blei
It maximizes a lower bound on the marginal likelihood of the data.
no code implementations • ICLR 2018 • Adji B. Dieng, Jaan Altosaar, Rajesh Ranganath, David M. Blei
We develop a noise-based regularization method for RNNs.
1 code implementation • 5 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.
no code implementations • 1 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 $q$.
no code implementations • 31 Oct 2016 • Dustin Tran, Alp Kucukelbir, Adji B. Dieng, Maja Rudolph, Dawen Liang, David M. Blei
Probabilistic modeling is a powerful approach for analyzing empirical information.