58 citations · 61 across the 3 of their papers we have counts for
4 papers · 1 filter
Estimating the Spectral Density of Large Implicit Matrices
Ryan P. Adams, Jeffrey Pennington, Matthew J. Johnson +4
Many important problems are characterized by the eigenvalues of a large matrix. For example, the difficulty of many optimization problems, such as those arising from the fitting of…
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,…
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…
Dependent Multinomial Models Made Easy: Stick Breaking with the Pólya-Gamma Augmentation
Scott W. Linderman, Matthew J. Johnson, Ryan P. Adams
Many practical modeling problems involve discrete data that are best represented as draws from multinomial or categorical distributions. For example, nucleotides in a DNA sequence,…