UQ360Uncertainty Quantification 360 (UQ360) is an extensible open-source toolkit that can help you estimate, communicate and use uncertainty in machine learning model predictions.
Stars: ✭ 211 (+134.44%)
Mutual labels: uncertainty, calibration
anomaly-segThe Combined Anomalous Object Segmentation (CAOS) Benchmark
Stars: ✭ 115 (+27.78%)
Mutual labels: out-of-distribution-detection, ml-safety
spatial-smoothing(ICML 2022) Official PyTorch implementation of “Blurs Behave Like Ensembles: Spatial Smoothings to Improve Accuracy, Uncertainty, and Robustness”.
Stars: ✭ 68 (-24.44%)
Mutual labels: uncertainty, robustness
robust-local-lipschitzA Closer Look at Accuracy vs. Robustness
Stars: ✭ 75 (-16.67%)
Mutual labels: robustness, adversarial-examples
DUNCode for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)
Stars: ✭ 65 (-27.78%)
Mutual labels: uncertainty, robustness
uapcaUncertainty-aware principal component analysis.
Stars: ✭ 16 (-82.22%)
Mutual labels: uncertainty
MonoRUn[CVPR'21] MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation
Stars: ✭ 85 (-5.56%)
Mutual labels: uncertainty
wellengA collection of Wells/Drilling Engineering tools, focused on well trajectory planning for the time being.
Stars: ✭ 79 (-12.22%)
Mutual labels: uncertainty
chempropFast and scalable uncertainty quantification for neural molecular property prediction, accelerated optimization, and guided virtual screening.
Stars: ✭ 75 (-16.67%)
Mutual labels: uncertainty
out-of-distribution-detectionThe Ultimate Reference for Out of Distribution Detection with Deep Neural Networks
Stars: ✭ 117 (+30%)
Mutual labels: out-of-distribution-detection
torchuqA library for uncertainty quantification based on PyTorch
Stars: ✭ 88 (-2.22%)
Mutual labels: uncertainty
survHESurvival analysis in health economic evaluation Contains a suite of functions to systematise the workflow involving survival analysis in health economic evaluation. survHE can fit a large range of survival models using both a frequentist approach (by calling the R package flexsurv) and a Bayesian perspective.
Stars: ✭ 32 (-64.44%)
Mutual labels: uncertainty
ProSelfLC-2021noisy labels; missing labels; semi-supervised learning; entropy; uncertainty; robustness and generalisation.
Stars: ✭ 45 (-50%)
Mutual labels: uncertainty
awesome-conformal-predictionA professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD and MSc theses, articles and open-source libraries.
Stars: ✭ 998 (+1008.89%)
Mutual labels: uncertainty
image-segmentationMask R-CNN, FPN, LinkNet, PSPNet and UNet with multiple backbone architectures support readily available
Stars: ✭ 62 (-31.11%)
Mutual labels: pretrained
pytorch-ensemblesPitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning, ICLR 2020
Stars: ✭ 196 (+117.78%)
Mutual labels: uncertainty
ACSCAutomatic Calibration for Non-repetitive Scanning Solid-State LiDAR and Camera Systems
Stars: ✭ 210 (+133.33%)
Mutual labels: calibration
sandySampling nuclear data and uncertainty
Stars: ✭ 30 (-66.67%)
Mutual labels: uncertainty