Topic: uncertainty-quantification Goto Github

👇 Here are 386 public repositories matching this topic...

aangelopoulos / conformal-prediction
uncertainty-quantification,Lightweight, useful implementation of conformal prediction on real data.
User: aangelopoulos
Home Page: http://people.eecs.berkeley.edu/~angelopoulos/blog/posts/gentle-intro/
computer-vision conformal conformal-prediction distribution-shift natural-language-processing time-series time-series-prediction uncertainty uncertainty-estimation uncertainty-quantification

aangelopoulos / conformal-time-series
uncertainty-quantification,Conformal prediction for time-series applications.
User: aangelopoulos
conformal conformal-prediction control control-systems control-theory controls time-series time-series-analysis uncertainty uncertainty-quantification

aangelopoulos / conformal_classification
uncertainty-quantification,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).
User: aangelopoulos
prediction-sets conformal-prediction conformal machine-learning distribution-free nonparametric nonparametric-statistics classification imagenet artificial-intelligence

alaalab / deep-learning-uncertainty
uncertainty-quantification,Literature survey, paper reviews, experimental setups and a collection of implementations for baselines methods for predictive uncertainty estimation in deep learning models.
Organization: alaalab
uncertainty-quantification deep-learning deep-neural-networks prediction-uncertainty uncertainty-estimation

awslabs / fortuna
uncertainty-quantification,A Library for Uncertainty Quantification.
Organization: awslabs
Home Page: https://aws-fortuna.readthedocs.io/en/latest/
deep-learning neural-networks uncertainty uncertainty-estimation uncertainty-quantification bayesian-inference calibration conformal-prediction model-calibration uncertainty-calibration

bayeswatch / deep-kernel-transfer
uncertainty-quantification,Official pytorch implementation of the paper "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels" (NeurIPS 2020)
Organization: bayeswatch
Home Page: https://arxiv.org/abs/1910.05199
gaussian-processes few-shot-learning bayesian-methods deep-learning deep-kernel-learning one-shot-learning kernel-methods classification regression gpytorch

cambridge-mlg / dun
uncertainty-quantification,Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)
Organization: cambridge-mlg
bayesian-inference variational-inference bayesian-neural-networks bayesian-deep-learning reproducible-research reproducible-paper network-depths uncertainty robustness neural-architecture-search

cics-nd / cnn-surrogate
uncertainty-quantification,Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification
Organization: cics-nd
Home Page: https://doi.org/10.1016/j.jcp.2018.04.018
encoder-decoder bayesian-surrogate uncertainty-quantification image-regression stein-variational-gradient-decent

cogito2012 / dear
uncertainty-quantification,[ICCV 2021 Oral] Deep Evidential Action Recognition
User: cogito2012
action-recognition openset-recognition video-understanding uncertainty-quantification evidential-deep-learning debiasing model-calibration ood-detection

deel-ai / puncc
uncertainty-quantification,👋 Puncc is a python library for predictive uncertainty quantification using conformal prediction.
Organization: deel-ai
Home Page: https://deel-ai.github.io/puncc/
conformal-prediction uncertainty-estimation uncertainty-quantification conformal-inference conformal-regressors

deephyper / deephyper
uncertainty-quantification,DeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks
Organization: deephyper
Home Page: https://deephyper.readthedocs.io
hyperparameter-search automl scalability neural-architecture-search deep-neural-networks hyperparameter-optimization autodl python ml tensorflow

dkogan / mrcal
uncertainty-quantification,Next-generation camera-modeling toolkit
User: dkogan
Home Page: http://mrcal.secretsauce.net
camera-calibration lens-modeling structure-from-motion uncertainty-quantification

dobriban / topics-in-modern-statistical-learning
uncertainty-quantification,Materials for STAT 991: Topics In Modern Statistical Learning (UPenn, 2022 Spring) - uncertainty quantification, conformal prediction, calibration, etc
User: dobriban
machine-learning uncertainty-quantification deep-learning conformal-prediction prediction tolerance-intervals calibration

eliahuhorwitz / conffusion
uncertainty-quantification,Official Implementation for the "Conffusion: Confidence Intervals for Diffusion Models" paper.
User: eliahuhorwitz
Home Page: https://www.vision.huji.ac.il/conffusion/
computer-vision conformal-prediction image-generation image-to-image inpainting quantile-regression super-resolution diffusion-models generative-model deep-learning

emukit / emukit
uncertainty-quantification,A Python-based toolbox of various methods in decision making, uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.
Organization: emukit
Home Page: https://emukit.github.io/emukit/
machine-learning bayesian-optimization uncertainty-quantification multi-fidelity experimental-design bayesian-quadrature sensitivity-analysis decision-making emulation python

ensta-u2is-ai / awesome-uncertainty-deeplearning
uncertainty-quantification,This repository contains a collection of surveys, datasets, papers, and codes, for predictive uncertainty estimation in deep learning models.
Organization: ensta-u2is-ai
deep-learning uncertainty-quantification uncertainty-estimation uncertainty-neural-networks uncertainty-analysis deep-neural-networks deep-learning-tutorials machine-learning awesome awesome-resources

ensta-u2is-ai / torch-uncertainty
uncertainty-quantification,Open-source framework for uncertainty and deep learning models in PyTorch :seedling:
Organization: ensta-u2is-ai
Home Page: https://torch-uncertainty.github.io
ensembles uncertainty-quantification predictive-uncertainty uncertainty reliable-ai pytorch bayesian-network neural-networks trustworthy-machine-learning mixup

filippomb / ensemble-conformalized-quantile-regression
uncertainty-quantification,Valid and adaptive prediction intervals for probabilistic time series forecasting
User: filippomb
Home Page: https://arxiv.org/abs/2202.08756
probabilistic-forecasting time-series-forecasting time-series-prediction uncertainty-quantification frequentist-statistics frequentistic-confidence-intervals recurrent-neural-networks quantile-regression conformal-prediction random-forest

glaciohack / xdem
uncertainty-quantification,Analysis of digital elevation models (DEMs)
Organization: glaciohack
Home Page: https://xdem.readthedocs.io/
dem variogram coregistration geostatistics digital-elevation-model error-analysis surface-elevation-data terrain-analysis uncertainty-quantification python gis

henrikbostrom / crepes
uncertainty-quantification,Conformal classifiers, regressors and predictive systems
User: henrikbostrom
machine-learning conformal-prediction conformal-regressors conformal-predictive-systems python jupyter-notebook confidence-intervals cumulative-distribution-function regression sklearn

ibm / uq360
uncertainty-quantification,Uncertainty Quantification 360 (UQ360) is an extensible open-source toolkit that can help you estimate, communicate and use uncertainty in machine learning model predictions.
Organization: ibm
Home Page: https://uq360.res.ibm.com/
uncertainty-quantification metrics uncertainty-estimates machine-learning artificial-intelligence trusted-ai uncertainty bayesianneuralnetwork calibration

idaholab / raven
uncertainty-quantification,RAVEN is a flexible and multi-purpose probabilistic risk analysis, validation and uncertainty quantification, parameter optimization, model reduction and data knowledge-discovering framework.
Organization: idaholab
Home Page: https://raven.inl.gov/
risk-analysis uncertainty-quantification probabilistic-analysis data-mining optimization-algorithms parametric-analysis validation model-reduction model-calibration

intellabs / bayesian-torch
uncertainty-quantification,A library for Bayesian neural network layers and uncertainty estimation in Deep Learning extending the core of PyTorch
Organization: intellabs
bayesian-deep-learning bayesian-neural-networks stochastic-variational-inference uncertainty-estimation pytorch bayesian-layers deep-learning bayesian-inference uncertainty-quantification uncertainty-neural-networks

jonathf / chaospy
uncertainty-quantification,Chaospy - Toolbox for performing uncertainty quantification.
User: jonathf
Home Page: https://chaospy.readthedocs.io/
uncertainty-quantification variance-reduction sensitivity-analysis gaussian-quadrature sparse-grids

jxzhangjhu / awesome-llm-uncertainty-reliability-robustness
uncertainty-quantification,Awesome-LLM-Robustness: a curated list of Uncertainty, Reliability and Robustness in Large Language Models
User: jxzhangjhu
awesome-list calibration gpt-3 gpt-4 llms reliability robustness safety uncertainty-estimation uncertainty-quantification chatgpt prompt-engineering prompting chain-of-thought in-context-learning large-language-models hallucination

lightning-uq-box / lightning-uq-box
uncertainty-quantification,Lightning-UQ-Box: Uncertainty Quantification for Neural Networks with PyTorch and Lightning
Organization: lightning-uq-box
Home Page: https://lightning-uq-box.readthedocs.io/en/latest/
bayesian-neural-networks conformal-prediction deep-learning earth-observation predictive-uncertainty pytorch pytorch-lightning uncertainty-quantification

lingkai-kong / sde-net
uncertainty-quantification,Code for paper: SDE-Net: Equipping Deep Neural network with Uncertainty Estimates
User: lingkai-kong
deep-learning uncertainty-quantification robustness open-world-classification

madsjulia / mads.jl
uncertainty-quantification,MADS: Model Analysis & Decision Support
Organization: madsjulia
Home Page: http://mads.gitlab.io
decision-support model-analysis calibration sensitivity-analysis uncertainty-quantification decision-making model-reduction machine-learning blind-source-separation inversion

mbhenaff / een
uncertainty-quantification,EEN: Error Encoding Network
User: mbhenaff
deep-learning video-prediction-models deep-neural-networks convolutional-neural-networks uncertainty-quantification

nikitadurasov / masksembles
uncertainty-quantification,Official repository for the paper "Masksembles for Uncertainty Estimation" (CVPR2021).
User: nikitadurasov
Home Page: https://nikitadurasov.github.io/projects/masksembles/
uncertainty-estimation deep-learning computer-vision tensorflow torch uncertainty-quantification uncertainty-neural-networks paper out-of-distribution-detection

openturns / openturns
uncertainty-quantification,Uncertainty treatment library
Organization: openturns
Home Page: http://openturns.github.io/openturns/latest/index.html
python metamodel probability-distribution stochastic-process optimization data-analysis reliability sensitivity uncertainty-quantification

ornl / tasmanian
uncertainty-quantification,The Toolkit for Adaptive Stochastic Modeling and Non-Intrusive ApproximatioN
Organization: ornl
Home Page: https://ornl.github.io/TASMANIAN/
uncertainty-quantification inverse-problems high-order-approximation-models reduced-order-models

pygpc-polynomial-chaos / pygpc
uncertainty-quantification,A Sensitivity and uncertainty analysis toolbox for Python based on the generalized polynomial chaos method
Organization: pygpc-polynomial-chaos
uncertainty-quantification dynamical-systems polynomial-chaos-expansion uncertainty-propagation uncertainty-estimation gpc

salib / salib
uncertainty-quantification,Sensitivity Analysis Library in Python. Contains Sobol, Morris, FAST, and other methods.
Organization: salib
Home Page: http://SALib.github.io/SALib/
sensitivity-analysis-library sensitivity-analysis sobol morris python global-sensitivity-analysis numpy salib joss uncertainty

sandialabs / uqtk
uncertainty-quantification,Sandia Uncertainty Quantification Toolkit
Organization: sandialabs
snl-data-analysis bayesian-inference snl-science-libs uq uncertainty-quantification uncertainty-estimation uncertainty-propagation uncertainty mcmc mcmc-sampler

sciml / polychaos.jl
uncertainty-quantification,A Julia package to construct orthogonal polynomials, their quadrature rules, and use it with polynomial chaos expansions.
Organization: sciml
Home Page: https://docs.sciml.ai/PolyChaos/stable/
polynomial-chaos-expansions orthogonal-polynomials quadrature-rules quadrature uncertainty-quantification uncertainty-propagation julia-language quadrature-integration polynomials differential-equations

sciml / scimlexpectations.jl
uncertainty-quantification,Fast uncertainty quantification for scientific machine learning (SciML) and differential equations
Organization: sciml
Home Page: https://docs.sciml.ai/SciMLExpectations/stable/
differentialequations uncertainty-quantification uq differential-equations sciml scientific-machine-learning integration julia ode

scottshambaugh / monaco
uncertainty-quantification,Quantify uncertainty and sensitivities in your computer models with an industry-grade Monte Carlo library.
User: scottshambaugh
monte-carlo simulation statistics uncertainty-quantification uncertainty-analysis sensitivity-analysis python monaco scientific-computing data-science

servicenow / azimuth
uncertainty-quantification,Helping AI practitioners better understand their datasets and models in text classification. From ServiceNow.
Organization: servicenow
Home Page: https://servicenow.github.io/azimuth/
data-analysis error-analysis explainability machine-learning nlp text-classification saliency-map similarity-analysis uncertainty-quantification explainable-ai

simetenn / uncertainpy
uncertainty-quantification,Uncertainpy: a Python toolbox for uncertainty quantification and sensitivity analysis, tailored towards computational neuroscience.
User: simetenn
Home Page: http://uncertainpy.readthedocs.io
uncertainty-quantification sensitivity-analysis neuroscience python computational-modeling

snap-stanford / conformalized-gnn
uncertainty-quantification,Uncertainty Quantification over Graph with Conformalized Graph Neural Networks (NeurIPS 2023)
Organization: snap-stanford
calibration conformal-prediction gnn graph graph-neural-networks uncertainty-quantification

stefanradev93 / bayesflow
uncertainty-quantification,A Python library for amortized Bayesian workflows using generative neural networks.
User: stefanradev93
Home Page: https://bayesflow.org/
bayesian-inference deep-learning model-comparison amortized-inference simulation-based invertible-networks parameter-estimation likelihood-free uncertainty-quantification

surgroup / uqpy
uncertainty-quantification,UQpy (Uncertainty Quantification with python) is a general purpose Python toolbox for modeling uncertainty in physical and mathematical systems.
Organization: surgroup
uncertainty-quantification uncertainty-propagation uncertainty-sampling probability probabilistic stochastic stochastic-process monte-carlo monte-carlo-simulation latin-hypercube

torchuq / torchuq
uncertainty-quantification,A library for uncertainty quantification based on PyTorch
Organization: torchuq
pytorch deep-learning uncertainty uncertainty-quantification

ucl-ccs / easyvvuq
uncertainty-quantification,Python 3 framework to facilitate verification, validation and uncertainty quantification (VVUQ) for a wide variety of simulations.
Organization: ucl-ccs
Home Page: https://easyvvuq.readthedocs.io/
uncertainty-quantification simulation hpc-applications vvuq verification python

uncertainty-toolbox / uncertainty-toolbox
uncertainty-quantification,Uncertainty Toolbox: a Python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization
Organization: uncertainty-toolbox
Home Page: https://uncertainty-toolbox.github.io
uncertainty uncertainty-quantification uncertainty-estimation uncertainty-metrics calibration sharpness uncertainty-toolbox metrics visualizations visualization

usgs / pestpp
uncertainty-quantification,tools for scalable and non-intrusive parameter estimation, uncertainty analysis and sensitivity analysis
Organization: usgs
uncertainty-quantification parameter-estimation ensembles optimization-tools optimization sensitivity-analysis global-sensitivity-analysis parallel-computing non-intrusive ensemble-methods

valeman / awesome-conformal-prediction
uncertainty-quantification,A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD and MSc theses, articles and open-source libraries.
User: valeman
conformal-prediction python r machinelearning datascience deeplearning awesome awesome-list probability uncertainty-quantification

xxxnell / spatial-smoothing
uncertainty-quantification,(ICML 2022) Official PyTorch implementation of “Blurs Behave Like Ensembles: Spatial Smoothings to Improve Accuracy, Uncertainty, and Robustness”.
User: xxxnell
Home Page: https://arxiv.org/abs/2105.12639
bayesian-deep-learning bayesian-neural-networks neural-network pytorch robustness uncertainty uncertainty-estimation uncertainty-quantification

ykwon0407 / uq_bnn
uncertainty-quantification,Uncertainty quantification using Bayesian neural networks in classification (MIDL 2018, CSDA)
User: ykwon0407
uncertainty-quantification bayesian-neural-networks