A Library for Uncertainty Quantification.
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Updated
Jun 6, 2024 - Python
A Library for 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.
[ICCV 2021 Oral] Deep Evidential Action Recognition
a modeling environment tailored to parameter estimation in dynamical systems
Code to accompany the paper 'Improving model calibration with accuracy versus uncertainty optimization'.
A phenology modelling framework in R
[CVPR 2023] Bridging Precision and Confidence: A Train-Time Loss for Calibrating Object Detection
Official code for "On Calibrating Diffusion Probabilistic Models"
pycalibrate is a Python library to visually analyze model calibration in Jupyter Notebooks
Codebase for "A Consistent and Differentiable Lp Canonical Calibration Error Estimator", published at NeurIPS 2022.
[MICCAI2022] Estimating Model Performance under Domain Shifts with Class-Specific Confidence Scores.
This is the official PyTorch codebase for the ACL 2023 paper: "What are the Desired Characteristics of Calibration Sets? Identifying Correlates on Long Form Scientific Summarization".
Simulating and Optimising Dynamical Models in Python 3
Parameter estimation and model calibration using Genetic Algorithm optimization in Python.
System Dynamics Review (2021)
Optimal delta hedging with SABR model
ARBO is a package for simulation and analysis of arbovirus nonlinear dynamics.
An efficient Java™ solver implementation for SBML
A collection of time-efficient state estimation algorithms for the medium-fidelity WindFarmSimulator (WFSim) control model
Calibration of the monodomain model coupled with the Rogers-McCulloch model for the ionic current: design of a protocol for impulse delivery from an ATP device.
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