uncertainty-neural-networks

There are 9 repositories under uncertainty-neural-networks 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 bayesian-inference pytorch reproducible-research bayesian-neural-networks bayes-by-backprop mc-dropout mcmc variational-inference kronecker-factored-approximation langevin-dynamics approximate-inference local-reparametrization-trick uncertainty uncertainty-neural-networks out-of-distribution-detection classification regression sgld hmc
Language:Jupyter Notebook 1741
IntelLabs / bayesian-torch
A library for Bayesian neural network layers and uncertainty estimation in Deep Learning extending the core of PyTorch
bayesian-deep-learning bayesian-neural-networks stochastic-variational-inference uncertainty-estimation pytorch bayesian-layers deep-learning bayesian-inference uncertainty-quantification uncertainty-neural-networks deep-neural-networks
Language:Python 447
ENSTA-U2IS-AI / awesome-uncertainty-deeplearning
This repository contains a collection of surveys, datasets, papers, and codes, for predictive uncertainty estimation in deep learning models.
awesome awesome-resources deep-learning deep-learning-tutorials deep-neural-networks machine-learning uncertainty-analysis uncertainty-estimation uncertainty-neural-networks uncertainty-quantification
382
dougbrion / pytorch-classification-uncertainty
This repo contains a PyTorch implementation of the paper: "Evidential Deep Learning to Quantify Classification Uncertainty"
paper pytorch evidential-deep-learning uncertainty-neural-networks classification mnist mnist-classification dirichlet-distributions uncertainty torchvision
Language:Python 378
mattiasegu / uncertainty_estimation_deep_learning
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
Language:Python 217
neuralnetworks.thought-experiments
shekkizh / neuralnetworks.thought-experiments
Observations and notes to understand the workings of neural network models and other thought experiments using Tensorflow
neural-network pruning optimal-brain-damage generative-adversarial-network generative-model uncertainty-neural-networks
Language:Jupyter Notebook 201
google / TrustScore
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
Language:Jupyter Notebook 173
vvanirudh / deep-ensembles-uncertainty
My implementation of the paper "Simple and Scalable Predictive Uncertainty estimation using Deep Ensembles"
tensorflow deep-learning uncertainty uncertainty-neural-networks
Language:Python 133
nikitadurasov / masksembles
Official repository for the paper "Masksembles for Uncertainty Estimation" (CVPR2021).
uncertainty-estimation deep-learning computer-vision tensorflow torch uncertainty-quantification uncertainty-neural-networks paper out-of-distribution-detection
Language:Python 94
cambridge-mlg / DUN
Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)
bayesian-inference variational-inference bayesian-neural-networks bayesian-deep-learning reproducible-research reproducible-paper network-depths uncertainty robustness neural-architecture-search machine-learning deep-learning neural-networks uncertainty-neural-networks uncertainty-quantification expectation-maximization probabilistic-inference probabilistic-neural-network learnt-depth pytorch
Language:Jupyter Notebook 71
amrit110 / Active-Learning-Papers
A list of papers on Active Learning and Uncertainty Estimation for Neural Networks.
active-learning uncertainty-neural-networks
63
sungyubkim / MCDO
A pytorch implementation of MCDO(Monte-Carlo Dropout methods)
bayesian-neural-networks bayesian-deep-learning variational-inference dropout variational-dropout uncertainty-neural-networks pytorch monte-carlo-dropout
Language:Jupyter Notebook 52
IntelLabs / AVUC
Code to accompany the paper 'Improving model calibration with accuracy versus uncertainty optimization'.
uncertainty-calibration bayesian-deep-learning uncertainty-estimation model-calibration deep-learning uncertainty-quantification variational-inference uncertainty-neural-networks probabilistic-neural-network probabilistic-deep-learning
Language:Python 48
Kaleidophon / nlp-uncertainty-zoo
Model zoo for different kinds of uncertainty quantification methods used in Natural Language Processing, implemented in PyTorch.
deep-learning lstm nlp nlp-machine-learning package python pytorch rnn transformers uncertainty-estimation uncertainty-neural-networks uncertainty-quantification
Language:Python 44
testingautomated-usi / uncertainty-wizard
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-tensorflow keras uncertainty-quantification uncertainty-neural-networks uncertainty
Language:Python 42
gengshan-y / monodepth-uncertainty
Inferring distributions over depth from a single image, IROS 2019
tensorflow monocular-depth deep-learning uncertainty-estimation uncertainty-neural-networks
Language:Python 39
konstantinos-p / Bayesian-Neural-Networks-Reading-List
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"
approximate-inference bayes-by-backprop bayesian-inference bayesian-neural-networks deep-learning hmc kronecker-factored-approximation langevin-dynamics local-reparametrization-trick mcmc sgld uncertainty uncertainty-neural-networks variational-inference
35
alexrakowski / mc-dropout-mnist
Implementation of the MNIST experiment for Monte Carlo Dropout from http://mlg.eng.cam.ac.uk/yarin/PDFs/NIPS_2015_bayesian_convnets.pdf
mnist mc-dropout deep-learning uncertainty-neural-networks bayesian-convnets keras
Language:Python 30
cantarinigiorgio / HHP-Net
[WACV'22] Official implementation of "HHP-Net: A light Heteroscedastic neural network for Head Pose estimation with uncertainty"
deep-learning neural-network head pose-estimation uncertainty-neural-networks
Language:Python 22
ptrckhmmr / DEAL
Code for "Deal: Deep Evidential Active Learning for Image Classification" (ICMLA 2020)
active-learning cnn image-classification uncertainty-neural-networks
Language:Python 18
microsoft / protein-uq
Benchmarking uncertainty quantification methods on proteins.
active-learning protein protein-design protein-fitness-prediction protein-function-prediction protein-sequences uncertainty-estimation uncertainty-neural-networks uncertainty-quantification
Language:Shell 17
AI4PFAS / AI4PFAS
Dataset and code for "Uncertainty-Informed Deep Transfer Learning of PFAS Toxicity"
pfas-toxicity uncertainty-neural-networks selectivenet cheminformatics artificial-intelligence deep-ensemble machine-learning ld50 gpflow pytorch tensorflow2 mordred rdkit gnn
Language:Jupyter Notebook 16
dlmacedo / robust-deep-learning
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.
classification deep-learning deep-neural-networks machine-learning ood-detection open-set open-set-recognition out-of-distribution-detection pytorch uncertainty-calibration uncertainty-estimation uncertainty-neural-networks robust-deep-learning robust-machine-learning trustworthy-ai trustworthy-machine-learning anomaly-detection novelty-detection out-of-distribution
Language:Python 16
dthanuja / ENG
A CNN based Depth, Optical Flow, Flow Uncertainty and Camera Pose Prediction pipeline
convolutional-neural-networks depth-prediction camera-pose-estimation optical-flow uncertainty-neural-networks
Language:Python 13
antonbaumann / MIMO-Unet
PyTorch implementation of Probabilistic MIMO U-Net
deep-ensemble u-net uncertainty-neural-networks uncertainty-analysis uncertainty-estimation uncertainty-quantification
Language:Python 12
DLR-RM / moegplib
Official Code: Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes
uncertainty-neural-networks uncertainty-quantification corl gaussian-processes neural-tangent-kernel
Language:Python 9
marketdesignresearch / NOMU
NOMU: Neural Optimization-based Model Uncertainty
bayesian-optimization deep-learning epistemic-uncertainty machine-learning neural-network uncertainty uncertainty-estimation uncertainty-neural-networks uncertainty-quantification
Language:Python 9
TeaPearce / Censored_Quantile_Regression_NN
NeurIPS paper 'Censored Quantile Regression Neural Networks for Distribution-Free Survival Analysis'
deep-learning deep-neural-networks quantile-regression survival-analysis uncertainty-neural-networks uncertainty-quantification
Language:Jupyter Notebook 9
cognizant-ai-labs / rio-paper
Code and supporting materials for the ICLR 2020 RIO paper
academic-paper academic-public-license iclr2020 neural-networks uncertainty-quantification uncertainty-neural-networks
Language:Python 8
jerinphilip / dirichlet-prior-networks
uncertainty-estimation uncertainty-neural-networks neural-networks
Language:Jupyter Notebook 8
JCSadeghi / Efficient-Training-of-Interval-Neural-Networks-for-Imprecise-Training-Data
Experiments from Efficient Training of Interval Neural Networks for Imprecise Training Data
machine-learning neural-networks tensorflow uncertainty-quantification uncertainty-neural-networks interval-arithmetic
Language:Python 7
GaMPEN
aritraghsh09 / GaMPEN
ML framework to estimate Bayesian posteriors of galaxy morphological parameters
astrophysics deep-neural-networks galaxy-morphology pytorch spatial-transformer-networks uncertainty-neural-networks
Language:Jupyter Notebook 6
firasl / CWCC
This repository provides the official implementation of "Robust channel-wise illumination estimation." accepted in BMVC (2021).
colorconstancy illumination-estimation uncertainty-neural-networks tensorflow2 keras bmvc2021
Language:Python 6
MicheleUIT / Probabilistic-load-forecasting-with-Reservoir-Computing
Probabilistic load forecasting with Reservoir Computing
probabilistic-forecasting probabilistic-programming reservoir-computing uncertainty-neural-networks uncertainty-quantification
Language:Jupyter Notebook 6
second-moment-loss
fraunhofer-iais / second-moment-loss
The second-moment loss (SML) is a novel training objective for dropout-based regression networks that yields improved uncertainty estimates.
python pytorch uncertainty-neural-networks regression-models dropout
Language:Jupyter Notebook 5
Vikr-182 / uap-bev
UAP-BEV: Uncertainty Aware Planning in Bird's Eye View Generated from Monocular Images (CASE 2023)
cem control-barrier-functions mpc non-parametric uncertainty-neural-networks uncertainty-quantification
4

uncertainty-neural-networks

JavierAntoran / Bayesian-Neural-Networks

IntelLabs / bayesian-torch

ENSTA-U2IS-AI / awesome-uncertainty-deeplearning

dougbrion / pytorch-classification-uncertainty

mattiasegu / uncertainty_estimation_deep_learning

shekkizh / neuralnetworks.thought-experiments

google / TrustScore

vvanirudh / deep-ensembles-uncertainty

nikitadurasov / masksembles

cambridge-mlg / DUN

amrit110 / Active-Learning-Papers

sungyubkim / MCDO

IntelLabs / AVUC

Kaleidophon / nlp-uncertainty-zoo

testingautomated-usi / uncertainty-wizard

gengshan-y / monodepth-uncertainty

konstantinos-p / Bayesian-Neural-Networks-Reading-List

alexrakowski / mc-dropout-mnist

cantarinigiorgio / HHP-Net

ptrckhmmr / DEAL

microsoft / protein-uq

AI4PFAS / AI4PFAS

dlmacedo / robust-deep-learning

dthanuja / ENG

antonbaumann / MIMO-Unet

DLR-RM / moegplib

marketdesignresearch / NOMU

TeaPearce / Censored_Quantile_Regression_NN

cognizant-ai-labs / rio-paper

jerinphilip / dirichlet-prior-networks

JCSadeghi / Efficient-Training-of-Interval-Neural-Networks-for-Imprecise-Training-Data

aritraghsh09 / GaMPEN

firasl / CWCC

MicheleUIT / Probabilistic-load-forecasting-with-Reservoir-Computing

fraunhofer-iais / second-moment-loss

Vikr-182 / uap-bev