Official repository for the paper "Masksembles for Uncertainty Estimation" (CVPR2021).
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Updated
Jun 22, 2021 - Python
Official repository for the paper "Masksembles for Uncertainty Estimation" (CVPR2021).
Simple and efficient way of performing deep ensembling to improve robustness as well as estimate uncertainty
Wasserstein dropout (W-dropout) is a novel technique to quantify uncertainty in regression networks. It is fully non-parametric and yields accurate uncertainty estimates - even under data shifts.
Uncertainty quantification fo ML - collection of scripts, tutorials and templates
A repo for toy examples to test uncertainties estimation of neural networks
Probabilistic framework for solving Visual Dialog
Behaviour Cloning of Cartpole Swing-up Policy with Model-Predictive Uncertainty Regularization (UW CSE571 Guided Project)
This repository contains code and resources for my thesis project on uncertainty estimation in computed tomography (CT) scan modeling. Explore Bayesian and deterministic neural network architectures for CT analysis and compare their effectiveness in quantifying uncertainty.
A neural-network based image classifier that quantifies its uncertainty using Bayesian methods, as described in Kendall and Gal (2017)
RBF SVM based wrong prediction estimator in deep learning models employed for CPS data
A validation study for the application of quantile regression neural networks to Bayesian remote sensing retrievals
An implementation of natural parameter networks and its extension to GRUs in PyTorch
Uncertainty aware brain age prediction
Example Git repository that you can run on the signaloid.io uncertainty-tracking computation platform.
This repository is for implementation of the paper Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles. This algorithm quantifies predictive predictive uncertainty in non-Bayesian NN with Deep Ensemble Model. Contribution of this paper is that it describes simple and scalable method for estimating predictive uncertainty es…
Guided Perturbations: Self-Corrective Behavior in Convolutional Neural Networks
Attempt to reproduce the toy experiment of http://bit.ly/2C9Z8St with an ensemble of nets and with dropout.
A repository about Robust Deep Neural Networks with Uncertainty, Local Competition and Error-Correcting-Output-Codes in TensorFlow.
UAP-BEV: Uncertainty Aware Planning in Bird's Eye View Generated from Monocular Images (CASE 2023)
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