uncertainty-quantification
There are 473 repositories under uncertainty-quantification topic.
uncertainty-toolbox/uncertainty-toolbox
Uncertainty Toolbox: a Python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization
SALib/SALib
Sensitivity Analysis Library in Python. Contains Sobol, Morris, FAST, and other methods.
awslabs/fortuna
A Library for Uncertainty Quantification.
aangelopoulos/conformal-prediction
Lightweight, useful implementation of conformal prediction on real data.
jxzhangjhu/Awesome-LLM-Uncertainty-Reliability-Robustness
Awesome-LLM-Robustness: a curated list of Uncertainty, Reliability and Robustness in Large Language Models
AlaaLab/deep-learning-uncertainty
Literature survey, paper reviews, experimental setups and a collection of implementations for baselines methods for predictive uncertainty estimation in deep learning models.
EmuKit/emukit
A Python-based toolbox of various methods in decision making, uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.
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.
IntelLabs/bayesian-torch
A library for Bayesian neural network layers and uncertainty estimation in Deep Learning extending the core of PyTorch
henrikbostrom/crepes
Python package for conformal prediction
valeman/awesome-conformal-prediction
A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD and MSc theses, articles and open-source libraries.
jonathf/chaospy
Chaospy - Toolbox for performing uncertainty quantification.
bayesflow-org/bayesflow
A Python library for amortized Bayesian workflows using generative neural networks.
ENSTA-U2IS-AI/torch-uncertainty
Open-source framework for uncertainty and deep learning models in PyTorch :seedling:
deel-ai/puncc
👋 Puncc is a python library for predictive uncertainty quantification using conformal prediction.
SURGroup/UQpy
UQpy (Uncertainty Quantification with python) is a general purpose Python toolbox for modeling uncertainty in physical and mathematical systems.
deephyper/deephyper
DeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks
IBM/UQ360
Uncertainty Quantification 360 (UQ360) is an extensible open-source toolkit that can help you estimate, communicate and use uncertainty in machine learning model predictions.
openturns/openturns
Uncertainty treatment library
aangelopoulos/conformal_classification
Wrapper for a PyTorch classifier which allows it to output prediction sets. The sets are theoretically guaranteed to contain the true class with high probability (via conformal prediction).
simetenn/uncertainpy
Uncertainpy: a Python toolbox for uncertainty quantification and sensitivity analysis, tailored towards computational neuroscience.
idaholab/raven
RAVEN is a flexible and multi-purpose probabilistic risk analysis, validation and uncertainty quantification, parameter optimization, model reduction and data knowledge-discovering framework.
dkogan/mrcal
Next-generation camera-modeling toolkit
BayesWatch/deep-kernel-transfer
Official pytorch implementation of the paper "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels" (NeurIPS 2020)
dobriban/Topics-In-Modern-Statistical-Learning
Materials for STAT 991: Topics In Modern Statistical Learning (UPenn, 2022 Spring) - uncertainty quantification, conformal prediction, calibration, etc
GlacioHack/xdem
Analysis of digital elevation models (DEMs)
lightning-uq-box/lightning-uq-box
Lightning-UQ-Box: Uncertainty Quantification for Neural Networks with PyTorch and Lightning
eliahuhorwitz/Conffusion
Official Implementation for the "Conffusion: Confidence Intervals for Diffusion Models" paper.
usgs/pestpp
tools for scalable and non-intrusive parameter estimation, uncertainty analysis and sensitivity analysis
ykwon0407/UQ_BNN
Uncertainty quantification using Bayesian neural networks in classification (MIDL 2018, CSDA)
Cogito2012/DEAR
[ICCV 2021 Oral] Deep Evidential Action Recognition
TorchUQ/torchuq
A library for uncertainty quantification based on PyTorch
SciML/PolyChaos.jl
A Julia package to construct orthogonal polynomials, their quadrature rules, and use it with polynomial chaos expansions.
Lingkai-Kong/SDE-Net
Code for paper: SDE-Net: Equipping Deep Neural network with Uncertainty Estimates
scottshambaugh/monaco
Quantify uncertainty and sensitivities in your computer models with an industry-grade Monte Carlo library.
aangelopoulos/conformal-time-series
Conformal prediction for time-series applications.