5.7k citations · 15.5k across the 75 of their papers we have counts for
8 papers · 2 filters
Variational Boosting: Iteratively Refining Posterior Approximations
Andrew C. Miller, Nicholas Foti, Ryan P. Adams
We propose a black-box variational inference method to approximate intractable distributions with an increasingly rich approximating class. Our method, termed variational boosting,…
Recurrent switching linear dynamical systems
Scott W. Linderman, Andrew C. Miller, Ryan P. Adams +3
Many natural systems, such as neurons firing in the brain or basketball teams traversing a court, give rise to time series data with complex, nonlinear dynamics. We can gain insigh…
Bayesian latent structure discovery from multi-neuron recordings
Scott W. Linderman, Ryan P. Adams, Jonathan W. Pillow
Neural circuits contain heterogeneous groups of neurons that differ in type, location, connectivity, and basic response properties. However, traditional methods for dimensionality…
Clustering with a Reject Option: Interactive Clustering as Bayesian Prior Elicitation
Akash Srivastava, James Zou, Ryan P. Adams +1
A good clustering can help a data analyst to explore and understand a data set, but what constitutes a good clustering may depend on domain-specific and application-specific criter…
Patterns of Scalable Bayesian Inference
Elaine Angelino, Matthew James Johnson, Ryan P. Adams
Datasets are growing not just in size but in complexity, creating a demand for rich models and quantification of uncertainty. Bayesian methods are an excellent fit for this demand,…
Composing graphical models with neural networks for structured representations and fast inference
Matthew J. Johnson, David Duvenaud, Alexander B. Wiltschko +2
We propose a general modeling and inference framework that composes probabilistic graphical models with deep learning methods and combines their respective strengths. Our model fam…