research
∙
04/14/2022
SemiMultiPose: A Semi-supervised Multi-animal Pose Estimation Framework
Multi-animal pose estimation is essential for studying animals' social b...
share
research
∙
10/29/2020
Attentive Clustering Processes
Amortized approaches to clustering have recently received renewed attent...
research
∙
06/05/2020
A zero-inflated gamma model for deconvolved calcium imaging traces
Calcium imaging is a critical tool for measuring the activity of large n...
research
∙
04/06/2020
Disentangled sticky hierarchical Dirichlet process hidden Markov model
The Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) has bee...
research
∙
03/11/2020
General linear-time inference for Gaussian Processes on one dimension
Gaussian Processes (GPs) provide a powerful probabilistic framework for ...
research
∙
12/28/2018
Discrete Neural Processes
Many data generating processes involve latent random variables over disc...
research
∙
11/24/2018
Amortized Bayesian inference for clustering models
We develop methods for efficient amortized approximate Bayesian inferenc...
research
∙
11/06/2018
A Novel Variational Family for Hidden Nonlinear Markov Models
Latent variable models have been widely applied for the analysis and vis...
research
∙
07/17/2018
Penalized matrix decomposition for denoising, compression, and improved demixing of functional imaging data
Calcium imaging has revolutionized systems neuroscience, providing the a...
research
∙
10/26/2017
Reparameterizing the Birkhoff Polytope for Variational Permutation Inference
Many matching, tracking, sorting, and ranking problems require probabili...
research
∙
10/26/2016
Recurrent switching linear dynamical systems
Many natural systems, such as neurons firing in the brain or basketball ...
research
∙
09/03/2016
Stochastic Bouncy Particle Sampler
We introduce a novel stochastic version of the non-reversible, rejection...
research
∙
06/24/2016
Robust and scalable Bayesian analysis of spatial neural tuning function data
A common analytical problem in neuroscience is the interpretation of neu...
research
∙
05/26/2016
Linear dynamical neural population models through nonlinear embeddings
A body of recent work in modeling neural activity focuses on recovering ...
research
∙
03/07/2016
Partition Functions from Rao-Blackwellized Tempered Sampling
Partition functions of probability distributions are important quantitie...
research
∙
11/23/2015
Black box variational inference for state space models
Latent variable time-series models are among the most heavily used tools...
research
∙
11/13/2015