This repo contains the code for the paper "Efficient hierarchical Bayesian inference for spatio-temporal regression models in neuroimaging" that has been accepted to NeurIPS 2021.

Last update: Jul 12, 2022

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Overview

Dugh-NeurIPS-2021

This repo contains the code for the paper "Efficient hierarchical Bayesian inference for spatio-temporal regression models in neuroimaging" that has been accepted to NeurIPS 2021.

The auditory and visual evoked field (AEF and VEF) data as well as the data for the simulation pipline can be downloaded using the following link: https://www.dropbox.com/sh/lm3m1tp475kxwjm/AABuKoqQeMMy11QfhOglHVP0a?dl=0

Owner

Ali Hashemi

I am a Data Scientist with over six years of experience and domain expertise in machine learning, statistics, optimization, and signal processing.

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This repo contains the code for the paper "Efficient hierarchical Bayesian inference for spatio-temporal regression models in neuroimaging" that has been accepted to NeurIPS 2021.

Related tags

Overview

Dugh-NeurIPS-2021

Owner

Ali Hashemi

Non-Official Pytorch implementation of "Face Identity Disentanglement via Latent Space Mapping" https://arxiv.org/abs/2005.07728 Using StyleGAN2 instead of StyleGAN

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Emotion Recognition from Facial Images

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Data Consistency for Magnetic Resonance Imaging

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