Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
-
Updated
Oct 20, 2023 - Jupyter Notebook
Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
A library for Bayesian neural network layers and uncertainty estimation in Deep Learning extending the core of PyTorch
This repo contains a PyTorch implementation of the paper: "Evidential Deep Learning to Quantify Classification Uncertainty"
To Trust Or Not To Trust A Classifier. A measure of uncertainty for any trained (possibly black-box) classifier which is more effective than the classifier's own implied confidence (e.g. softmax probability for a neural network).
This repository contains a collection of surveys, datasets, papers, and codes, for predictive uncertainty estimation in deep learning models.
Observations and notes to understand the workings of neural network models and other thought experiments using Tensorflow
This repository provides the code used to implement the framework to provide deep learning models with total uncertainty estimates as described in "A General Framework for Uncertainty Estimation in Deep Learning" (Loquercio, Segù, Scaramuzza. RA-L 2020).
My implementation of the paper "Simple and Scalable Predictive Uncertainty estimation using Deep Ensembles"
Code to accompany the paper 'Improving model calibration with accuracy versus uncertainty optimization'.
Official repository for the paper "Masksembles for Uncertainty Estimation" (CVPR 2021).
Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)
A list of papers on Active Learning and Uncertainty Estimation for Neural Networks.
A pytorch implementation of MCDO(Monte-Carlo Dropout methods)
Example Git repository that you can run on the signaloid.io uncertainty-tracking computation platform.
Uncertainty-Wizard is a plugin on top of tensorflow.keras, allowing to easily and efficiently create uncertainty-aware deep neural networks. Also useful if you want to train multiple small models in parallel.
[WACV'22] Official implementation of "HHP-Net: A light Heteroscedastic neural network for Head Pose estimation with uncertainty"
NOMU: Neural Optimization-based Model Uncertainty
Model zoo for different kinds of uncertainty quantification methods used in Natural Language Processing, implemented in PyTorch.
A project to train your model from scratch or fine-tune a pretrained model using the losses provided in this library to improve out-of-distribution detection and uncertainty estimation performances. Calibrate your model to produce enhanced uncertainty estimations. Detect out-of-distribution data using the defined score type and threshold.
A CNN based Depth, Optical Flow, Flow Uncertainty and Camera Pose Prediction pipeline
Add a description, image, and links to the uncertainty-neural-networks topic page so that developers can more easily learn about it.
To associate your repository with the uncertainty-neural-networks topic, visit your repo's landing page and select "manage topics."