A Library for Uncertainty Quantification.
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Updated
Nov 23, 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
Quantum Finance Library
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"
Delta hedging under SABR model
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.
Parameter estimation and model calibration using Genetic Algorithm optimization in Python.
[MICCAI2022] Estimating Model Performance under Domain Shifts with Class-Specific Confidence Scores.
Simulating and Optimising Dynamical Models in Python 3
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".
System Dynamics Review (2021)
ARBO is a Matlab/C++ package for simulation and analysis of arbovirus nonlinear dynamics.
An efficient Java™ solver implementation for SBML
In this project, I analyze, plot and clean Tanzania's Water Pump Dataset, which is provided by DrivenData.org for a competition.
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