uncertainty-neural-networks

Here are 59 public repositories matching this topic...

JavierAntoran / Bayesian-Neural-Networks

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more

deep-learning reproducible-research regression pytorch uncertainty classification uncertainty-neural-networks bayesian-inference mcmc variational-inference hmc bayesian-neural-networks langevin-dynamics approximate-inference local-reparametrization-trick kronecker-factored-approximation mc-dropout bayes-by-backprop out-of-distribution-detection sgld

Updated Oct 20, 2023
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ENSTA-U2IS-AI / awesome-uncertainty-deeplearning

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This repository contains a collection of surveys, datasets, papers, and codes, for predictive uncertainty estimation in deep learning models.

machine-learning awesome deep-neural-networks deep-learning uncertainty-neural-networks uncertainty-quantification uncertainty-estimation uncertainty-analysis awesome-resources deep-learning-tutorials

Updated Oct 3, 2024

IntelLabs / bayesian-torch

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A library for Bayesian neural network layers and uncertainty estimation in Deep Learning extending the core of PyTorch

deep-neural-networks deep-learning pytorch uncertainty-neural-networks bayesian-inference uncertainty-quantification uncertainty-estimation bayesian-neural-networks bayesian-deep-learning stochastic-variational-inference bayesian-layers

Updated Feb 10, 2024
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dougbrion / pytorch-classification-uncertainty

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This repo contains a PyTorch implementation of the paper: "Evidential Deep Learning to Quantify Classification Uncertainty"

paper pytorch uncertainty mnist classification mnist-classification uncertainty-neural-networks torchvision evidential-deep-learning dirichlet-distributions

Updated Jan 2, 2024
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mattiasegu / uncertainty_estimation_deep_learning

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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).

deep-learning uncertainty-neural-networks

Updated Jul 25, 2024
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shekkizh / neuralnetworks.thought-experiments

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Observations and notes to understand the workings of neural network models and other thought experiments using Tensorflow

neural-network generative-adversarial-network generative-model pruning optimal-brain-damage uncertainty-neural-networks

Updated Oct 27, 2019
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google / TrustScore

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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).

uncertainty-neural-networks uncertainty-estimation

Updated Mar 23, 2023
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vvanirudh / deep-ensembles-uncertainty

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My implementation of the paper "Simple and Scalable Predictive Uncertainty estimation using Deep Ensembles"

deep-learning tensorflow uncertainty uncertainty-neural-networks

Updated Jan 15, 2018
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nikitadurasov / masksembles

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Official repository for the paper "Masksembles for Uncertainty Estimation" (CVPR 2021).

computer-vision deep-learning tensorflow paper torch uncertainty-neural-networks uncertainty-quantification uncertainty-estimation out-of-distribution-detection

Updated Feb 8, 2024
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cambridge-mlg / DUN

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Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)

machine-learning deep-learning reproducible-research pytorch uncertainty neural-networks expectation-maximization uncertainty-neural-networks bayesian-inference uncertainty-quantification variational-inference bayesian-neural-networks robustness neural-architecture-search bayesian-deep-learning probabilistic-inference reproducible-paper probabilistic-neural-network learnt-depth network-depths

Updated Oct 3, 2023
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amrit110 / Active-Learning-Papers

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A list of papers on Active Learning and Uncertainty Estimation for Neural Networks.

uncertainty-neural-networks active-learning

Updated Jun 26, 2020

sungyubkim / MCDO

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A pytorch implementation of MCDO(Monte-Carlo Dropout methods)

pytorch dropout uncertainty-neural-networks variational-inference bayesian-neural-networks bayesian-deep-learning variational-dropout monte-carlo-dropout

Updated Jan 1, 2019
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IntelLabs / AVUC

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Code to accompany the paper 'Improving model calibration with accuracy versus uncertainty optimization'.

deep-learning uncertainty-neural-networks model-calibration uncertainty-quantification uncertainty-estimation variational-inference bayesian-deep-learning probabilistic-neural-network probabilistic-deep-learning uncertainty-calibration

Updated Dec 16, 2022
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tum-pbs / Diffusion-based-Flow-Prediction

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Official implementation of the AIAA Journal paper "Uncertainty-aware Surrogate Models for Airfoil Flow Simulations with Denoising Diffusion Probabilistic Models"

deep-learning uncertainty-neural-networks physics-simulation uncertainty-quantification fluid-dynamics fluid-simulation baysian-network diffusion-models flow-matching

Updated Nov 4, 2024
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konstantinos-p / Bayesian-Neural-Networks-Reading-List

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A primer on Bayesian Neural Networks. The aim of this reading list is to facilitate the entry of new researchers into the field of Bayesian Deep Learning, by providing an overview of key papers. More details: "A Primer on Bayesian Neural Networks: Review and Debates"

deep-learning uncertainty uncertainty-neural-networks bayesian-inference mcmc variational-inference hmc bayesian-neural-networks langevin-dynamics approximate-inference local-reparametrization-trick kronecker-factored-approximation bayes-by-backprop sgld

Updated Nov 1, 2023

Kaleidophon / nlp-uncertainty-zoo

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Model zoo for different kinds of uncertainty quantification methods used in Natural Language Processing, implemented in PyTorch.

python nlp package deep-learning transformers pytorch lstm rnn uncertainty-neural-networks uncertainty-quantification uncertainty-estimation nlp-machine-learning

Updated May 5, 2023
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testingautomated-usi / uncertainty-wizard

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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.

testing keras uncertainty uncertainty-neural-networks uncertainty-quantification keras-tensorflow