223 citations · 556 across the 23 of their papers we have counts for
8 papers · 1 filter
Quality-Weighted Vendi Scores And Their Application To Diverse Experimental Design
Quan Nguyen, Adji Bousso Dieng
Experimental design techniques such as active search and Bayesian optimization are widely used in the natural sciences for data collection and discovery. However, existing techniqu…
Alternators For Sequence Modeling
Mohammad Reza Rezaei, Adji Bousso Dieng
This paper introduces alternators, a novel family of non-Markovian dynamical models for sequences. An alternator features two neural networks: the observation trajectory network (O…
Deep Probabilistic Graphical Modeling
Adji B. Dieng
Probabilistic graphical modeling (PGM) provides a framework for formulating an interpretable generative process of data and expressing uncertainty about unknowns, but it lacks flex…
Prescribed Generative Adversarial Networks
Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei +1
Generative adversarial networks (GANs) are a powerful approach to unsupervised learning. They have achieved state-of-the-art performance in the image domain. However, GANs are limi…
Reweighted Expectation Maximization
Adji B. Dieng, John Paisley
Training deep generative models with maximum likelihood remains a challenge. The typical workaround is to use variational inference (VI) and maximize a lower bound to the log margi…
Avoiding Latent Variable Collapse With Generative Skip Models
Adji B. Dieng, Yoon Kim, Alexander M. Rush +1
Variational autoencoders learn distributions of high-dimensional data. They model data with a deep latent-variable model and then fit the model by maximizing a lower bound of the l…
Noisin: Unbiased Regularization for Recurrent Neural Networks
Adji B. Dieng, Rajesh Ranganath, Jaan Altosaar +1
Recurrent neural networks (RNNs) are powerful models of sequential data. They have been successfully used in domains such as text and speech. However, RNNs are susceptible to overf…
Augment and Reduce: Stochastic Inference for Large Categorical Distributions
Francisco J. R. Ruiz, Michalis K. Titsias, Adji B. Dieng +1
Categorical distributions are ubiquitous in machine learning, e.g., in classification, language models, and recommendation systems. However, when the number of possible outcomes is…