223 citations · 435 across the 41 of their papers we have counts for
41 papers
Scaling Up Bayesian Neural Networks with Neural Networks
Zahra Moslemi, Yang Meng, Shiwei Lan +1
Bayesian Neural Networks (BNNs) offer a principled and natural framework for proper uncertainty quantification in the context of deep learning. They address the typical challenges…
A Model-Agnostic Graph Neural Network for Integrating Local and Global Information
Wenzhuo Zhou, Annie Qu, Keiland W. Cooper +2
Graph Neural Networks (GNNs) have achieved promising performance in a variety of graph-focused tasks. Despite their success, however, existing GNNs suffer from two significant limi…
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes
Ba-Hien Tran, Babak Shahbaba, Stephan Mandt +1
Autoencoders and their variants are among the most widely used models in representation learning and generative modeling. However, autoencoder-based models usually assume that the…
Statistical implications of relaxing the homogeneous mixing assumption in time series Susceptible-Infectious-Removed models
Luis D. J. Martinez Lomeli, Michelle N. Ngo, Jon Wakefield +2
Infectious disease epidemiologists routinely fit stochastic epidemic models to time series data to elucidate infectious disease dynamics, evaluate interventions, and forecast epide…
A Horseshoe mixture model for Bayesian screening with an application to light sheet fluorescence microscopy in brain imaging
Francesco Denti, Ricardo Azevedo, Chelsie Lo +4
In this paper, we focus on identifying differentially activated brain regions using a light sheet fluorescence microscopy - a recently developed technique for whole-brain imaging.…
Brain Waves Analysis Via a Non-parametric Bayesian Mixture of Autoregressive Kernels
Guillermo Granados-Garcia, Mark Fiecas, Babak Shahbaba +2
The standard approach to analyzing brain electrical activity is to examine the spectral density function (SDF) and identify predefined frequency bands that have the most substantia…