34 citations · 80 across the 18 of their papers we have counts for
7 papers · 1 filter
Bayesian recurrent state space model for rs-fMRI
Arunesh Mittal, Scott Linderman, John Paisley +1
We propose a hierarchical Bayesian recurrent state space model for modeling switching network connectivity in resting state fMRI data. Our model allows us to uncover shared network…
Deep Bayesian Nonparametric Factor Analysis
Arunesh Mittal, Paul Sajda, John Paisley
We propose a deep generative factor analysis model with beta process prior that can approximate complex non-factorial distributions over the latent codes. We outline a stochastic E…
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…
Location Dependent Dirichlet Processes
Shiliang Sun, John Paisley, Qiuyang Liu
Dirichlet processes (DP) are widely applied in Bayesian nonparametric modeling. However, in their basic form they do not directly integrate dependency information among data arisin…
Bayesian Poisson Tensor Factorization for Inferring Multilateral Relations from Sparse Dyadic Event Counts
Aaron Schein, John Paisley, David M. Blei +1
We present a Bayesian tensor factorization model for inferring latent group structures from dynamic pairwise interaction patterns. For decades, political scientists have collected…
Stochastic Annealing for Variational Inference
San Gultekin, Aonan Zhang, John Paisley
We empirically evaluate a stochastic annealing strategy for Bayesian posterior optimization with variational inference. Variational inference is a deterministic approach to approxi…