3 citations · 7 across the 5 of their papers we have counts for
5 papers
Gaze-Informed Vision Transformers: Predicting Driving Decisions Under Uncertainty
Sharath Koorathota, Nikolas Papadopoulos, Jia Li Ma +5
Vision Transformers (ViT) have advanced computer vision, yet their efficacy in complex tasks like driving remains less explored. This study enhances ViT by integrating human eye ga…
Bayesian Beta-Bernoulli Process Sparse Coding with Deep Neural Networks
Arunesh Mittal, Kai Yang, Paul Sajda +1
Several approximate inference methods have been proposed for deep discrete latent variable models. However, non-parametric methods which have previously been successfully employed…
Improving Prediction of Cognitive Performance using Deep Neural Networks in Sparse Data
Sharath Koorathota, Arunesh Mittal, Richard P. Sloan +1
Cognition in midlife is an important predictor of age-related mental decline and statistical models that predict cognitive performance can be useful for predicting decline. However…
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…