58 citations · 119 across the 7 of their papers we have counts for
8 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…
Point process models for sequence detection in high-dimensional neural spike trains
Alex H. Williams, Anthony Degleris, Yixin Wang +1
Sparse sequences of neural spikes are posited to underlie aspects of working memory, motor production, and learning. Discovering these sequences in an unsupervised manner is a long…
Poisson-Randomized Gamma Dynamical Systems
Aaron Schein, Scott W. Linderman, Mingyuan Zhou +2
This paper presents the Poisson-randomized gamma dynamical system (PRGDS), a model for sequentially observed count tensors that encodes a strong inductive bias toward sparsity and…
Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling
Josue Nassar, Scott W. Linderman, Monica Bugallo +1
Many real-world systems studied are governed by complex, nonlinear dynamics. By modeling these dynamics, we can gain insight into how these systems work, make predictions about how…
Learning Latent Permutations with Gumbel-Sinkhorn Networks
Gonzalo Mena, David Belanger, Scott Linderman +1
Permutations and matchings are core building blocks in a variety of latent variable models, as they allow us to align, canonicalize, and sort data. Learning in such models is diffi…
Reparameterizing the Birkhoff Polytope for Variational Permutation Inference
Scott W. Linderman, Gonzalo E. Mena, Hal Cooper +2
Many matching, tracking, sorting, and ranking problems require probabilistic reasoning about possible permutations, a set that grows factorially with dimension. Combinatorial optim…