Topic: uncertainty-quantification Goto Github
Some thing interesting about uncertainty-quantification
Some thing interesting about uncertainty-quantification
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/
uncertainty-quantification,Conformal prediction for time-series applications.
User: aangelopoulos
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
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,A Library for Uncertainty Quantification.
Organization: awslabs
Home Page: https://aws-fortuna.readthedocs.io/en/latest/
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
uncertainty-quantification,Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)
Organization: cambridge-mlg
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
uncertainty-quantification,[ICCV 2021 Oral] Deep Evidential Action Recognition
User: cogito2012
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/
uncertainty-quantification,DeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks
Organization: deephyper
Home Page: https://deephyper.readthedocs.io
uncertainty-quantification,Next-generation camera-modeling toolkit
User: dkogan
Home Page: http://mrcal.secretsauce.net
uncertainty-quantification,Materials for STAT 991: Topics In Modern Statistical Learning (UPenn, 2022 Spring) - uncertainty quantification, conformal prediction, calibration, etc
User: dobriban
uncertainty-quantification,Official Implementation for the "Conffusion: Confidence Intervals for Diffusion Models" paper.
User: eliahuhorwitz
Home Page: https://www.vision.huji.ac.il/conffusion/
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/
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
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
uncertainty-quantification,Valid and adaptive prediction intervals for probabilistic time series forecasting
User: filippomb
Home Page: https://arxiv.org/abs/2202.08756
uncertainty-quantification,Analysis of digital elevation models (DEMs)
Organization: glaciohack
Home Page: https://xdem.readthedocs.io/
uncertainty-quantification,Python package for conformal prediction
User: henrikbostrom
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,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/
uncertainty-quantification,A library for Bayesian neural network layers and uncertainty estimation in Deep Learning extending the core of PyTorch
Organization: intellabs
uncertainty-quantification,Chaospy - Toolbox for performing uncertainty quantification.
User: jonathf
Home Page: https://chaospy.readthedocs.io/
uncertainty-quantification,Awesome-LLM-Robustness: a curated list of Uncertainty, Reliability and Robustness in Large Language Models
User: jxzhangjhu
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/
uncertainty-quantification,Code for paper: SDE-Net: Equipping Deep Neural network with Uncertainty Estimates
User: lingkai-kong
uncertainty-quantification,MADS: Model Analysis & Decision Support
Organization: madsjulia
Home Page: http://mads.gitlab.io
uncertainty-quantification,EEN: Error Encoding Network
User: mbhenaff
uncertainty-quantification,Official repository for the paper "Masksembles for Uncertainty Estimation" (CVPR 2021).
User: nikitadurasov
Home Page: https://www.norange.io/projects/masksembles/
uncertainty-quantification,Uncertainty treatment library
Organization: openturns
Home Page: http://openturns.github.io/openturns/latest/index.html
uncertainty-quantification,The Toolkit for Adaptive Stochastic Modeling and Non-Intrusive ApproximatioN
Organization: ornl
Home Page: https://ornl.github.io/TASMANIAN/
uncertainty-quantification,A Sensitivity and uncertainty analysis toolbox for Python based on the generalized polynomial chaos method
Organization: pygpc-polynomial-chaos
uncertainty-quantification,Sensitivity Analysis Library in Python. Contains Sobol, Morris, FAST, and other methods.
Organization: salib
Home Page: http://SALib.github.io/SALib/
uncertainty-quantification,Sandia Uncertainty Quantification Toolkit
Organization: sandialabs
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/
uncertainty-quantification,Fast uncertainty quantification for scientific machine learning (SciML) and differential equations
Organization: sciml
Home Page: https://docs.sciml.ai/SciMLExpectations/stable/
uncertainty-quantification,Quantify uncertainty and sensitivities in your computer models with an industry-grade Monte Carlo library.
User: scottshambaugh
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,Uncertainty Quantification over Graph with Conformalized Graph Neural Networks (NeurIPS 2023)
Organization: snap-stanford
uncertainty-quantification,A Python library for amortized Bayesian workflows using generative neural networks.
User: stefanradev93
Home Page: https://bayesflow.org/
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,A library for uncertainty quantification based on PyTorch
Organization: torchuq
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,Uncertainty Toolbox: a Python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization
Organization: uncertainty-toolbox
Home Page: https://uncertainty-toolbox.github.io
uncertainty-quantification,tools for scalable and non-intrusive parameter estimation, uncertainty analysis and sensitivity analysis
Organization: usgs
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
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
uncertainty-quantification,Uncertainty quantification using Bayesian neural networks in classification (MIDL 2018, CSDA)
User: ykwon0407
uncertainty-quantification,
User: zlin7
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