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
A drop-in replacement for the standard Categorical Cross-Entropy (CCE) loss that significantly improves OOD and Calibration performance without reducing ID performance.
A phenology modelling framework in R
Delta hedging under SABR model
[CVPR 2023] Bridging Precision and Confidence: A Train-Time Loss for Calibrating Object Detection
Official code for "On Calibrating Diffusion Probabilistic Models"
Official code for "Exploring and exploiting model uncertainty for robust visual question answering"
A toolbox for the calibration and evaluation of simulation 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.
HypeLoRA: Hypernetwork-Generated LoRA Adapters for Calibrated Language Model Fine-Tuning
[MICCAI2022] Estimating Model Performance under Domain Shifts with Class-Specific Confidence Scores.
Parameter estimation and model calibration using Genetic Algorithm optimization in Python.
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)
[ICLR'26] Official code for "A-TPT: Angular Diversity Calibration Properties for Test-Time Prompt Tuning of Vision-Language Models"
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