216 citations · 510 across the 17 of their papers we have counts for
4 papers · 1 filter
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…
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…
The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM
Ardavan Saeedi, Matthew Hoffman, Matthew Johnson +1
We propose the segmented iHMM (siHMM), a hierarchical infinite hidden Markov model (iHMM) that supports a simple, efficient inference scheme. The siHMM is well suited to segmentati…