#
uncertainty
Here are 167 public repositories matching this topic...
A python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization
visualization
metrics
scoring-rules
toolbox
uncertainty
calibration
visualizations
uncertainty-quantification
uncertainty-estimation
bayesian-neural-networks
bayesian-deep-learning
sharpness
predictive-uncertainty
recalibration
uncertainty-calibration
uncertainty-metrics
uncertainty-toolbox
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Updated
Jan 19, 2021 - Python
Sensitivity Analysis Library in Python (Numpy). Contains Sobol, Morris, Fractional Factorial and FAST methods.
python
numpy
uncertainty
uncertainty-quantification
sensitivity-analysis
morris
sensitivity-analysis-library
sobol
global-sensitivity-analysis
salib
joss
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Updated
Jan 30, 2021 - Python
(ICCV 2019) Uncertainty-aware Face Representation and Recognition
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Updated
Aug 8, 2019 - Python
[ICCV 2019] Official implementation of "MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation" in PyTorch + Social Distancing
machine-learning
computer-vision
deep-learning
pytorch
uncertainty
object-detection
human-pose-estimation
pose-estimation
3d-vision
kitti
3d-deep-learning
3d-detection
3d-object-detection
nuscenes
iccv2019
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Updated
Feb 3, 2021 - Python
Self-Supervised Learning for OOD Detection (NeurIPS 2019)
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Updated
Apr 22, 2020 - Python
Visualizations of distributions and uncertainty
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Jan 27, 2021 - R
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
python
data-science
demo
machine-learning
tutorial
statistics
ai
scikit-learn
ml
artificial-intelligence
uncertainty
supervised-learning
interpretability
rule-learning
bayesian-rule-lists
optimal-classification-tree
rulefit
imodels
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Jan 28, 2021 - Jupyter Notebook
"What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?", NIPS 2017 (unofficial code).
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Updated
Dec 19, 2019 - Python
Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning, ICLR 2020
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Updated
Sep 17, 2020 - Jupyter Notebook
My implementation of the paper "Simple and Scalable Predictive Uncertainty estimation using Deep Ensembles"
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Updated
Jan 15, 2018 - Python
Implementation and evaluation of different approaches to get uncertainty in neural networks
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Feb 16, 2018 - Jupyter Notebook
Official pytorch implementation of the paper "Deep Kernel Transfer in Gaussian Processes for Few-shot Learning"
kernel
deep-learning
paper
regression
uncertainty
bayesian-methods
classification
kernel-methods
one-shot-learning
uncertainty-quantification
gaussian-processes
kernels
meta-learning
shot-learning
deep-kernel-learning
few-shot-learning
gpytorch
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Updated
Nov 30, 2020 - Python
Bayesian dessert for Lasagne
lasagne
theano
deep-learning
neural-network
uncertainty
bayesian-inference
variational-inference
gelato
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Updated
Jun 25, 2017 - Python
Learn fast, scalable, and calibrated measures of uncertainty using neural networks!
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Updated
Dec 25, 2020 - Python
PyTorch Implementation of QuickNAT and Bayesian QuickNAT, a fast brain MRI segmentation framework with segmentation Quality control using structure-wise uncertainty
machine-learning
quality-control
ai
computer-vision
deep-learning
pytorch
uncertainty
medical-imaging
segmentation
bayesian
convolutional-neural-networks
mri-images
brain-imaging
neuroanatomy
biomarkers
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Updated
Jan 7, 2021 - Python
Density estimation using Gaussian mixtures in the presence of noisy, heterogeneous and incomplete data
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Updated
Jan 28, 2021 - Python
Official Pytorch Implementation of 'Weakly-supervised Temporal Action Localization by Uncertainty Modeling' (AAAI-21)
deep-learning
pytorch
uncertainty
background-modeling
weakly-supervised-learning
temporal-action-localization
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Updated
Jan 20, 2021 - Python
Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)
uncertainty
calibration
ai-safety
robustness
adversarial-examples
pretrained
data-corruption
out-of-distribution-detection
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Updated
May 15, 2019 - Python
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
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Updated
Sep 1, 2020 - Python
Some notebooks
machine-learning
reinforcement-learning
deep-learning
genetic-algorithm
q-learning
artificial-intelligence
uncertainty
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Updated
Apr 26, 2020 - Jupyter Notebook
A Ranked Programming Extension for Racket
racket
uncertainty
probabilistic-programming
racket-lang
commonsense-reasoning
defeasible-logic
belief-revision
ranking-theory
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Updated
Jul 10, 2020 - HTML
[ICCV'19] Calibration Wizard: A Guidance System for Camera Calibration Based on Modelling Geometric and Corner Uncertainty
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Updated
Apr 1, 2020 - C++
Multi Task Learning Implementation with Homoscedastic Uncertainty in Tensorflow
deep-learning
neural-network
tensorflow
uncertainty
estimator
uncertainties
multi-task-learning
homoscedastic
homoskedastic
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Updated
Sep 1, 2018 - Jupyter Notebook
Uncertainty Propagation for R Vectors
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Updated
Nov 10, 2020 - R
Airport Surface Simulator and Evaluation Tool 2
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Updated
Dec 1, 2018 - Python
implementing "recurrent attentive neural processes" to forecast power usage (w. LSTM baseline, MCDropout)
prediction
pytorch
uncertainty
rnn
attention
anp
neural-processes
attentive-neural-processes
anp-rnn
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Updated
Nov 1, 2020 - Jupyter Notebook
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
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Updated
Jun 20, 2020 - Jupyter Notebook
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Hi @JavierAntoran @stratisMarkou,
First of all, thanks for making all of this code available - it's been great to look through!
Im currently spending some time trying to work through the Weight Uncertainty in Neural Networks in order to implement Bayes-by-Backprop. I was struggling to understand the difference between your implementation of `Bayes-by-Bac