Uncertainty Toolbox: a Python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization
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Updated
Jul 9, 2024 - Python
Uncertainty Toolbox: a Python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization
Natural Gradient Boosting for Probabilistic Prediction
A Library for Uncertainty Quantification.
An extension of XGBoost to probabilistic modelling
A library for Bayesian neural network layers and uncertainty estimation in Deep Learning extending the core of PyTorch
👋 Puncc is a python library for predictive uncertainty quantification using conformal prediction.
An extension of LightGBM to probabilistic modelling
CVPR 2020 - On the uncertainty of self-supervised monocular depth estimation
[ICCV 2021 Oral] Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation
[CVPR 2022 Oral] Multi-View Depth Estimation by Fusing Single-View Depth Probability with Multi-View Geometry
Quantile Regression Forests compatible with scikit-learn.
GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty Estimation [IJCV2023]
[CVPR 2024 Oral, Best Paper Award Candidate] Official repository of "PaSCo: Urban 3D Panoptic Scene Completion with Uncertainty Awareness"
Official Implementation for the "Conffusion: Confidence Intervals for Diffusion Models" paper.
Official implementation of "Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision", CVPR Workshops 2020.
[ICCV'23] Sparse Sampling Transformer with Uncertainty-Driven Ranking for Unified Removal of Raindrops and Rain Streaks
[CVPR 2024 Award Candidate] Producing and Leveraging Online Map Uncertainty in Trajectory Prediction
Official repository for the paper "Masksembles for Uncertainty Estimation" (CVPR 2021).
Official Implementation of "Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions" (ICLR, 2022)
(ICML 2022) Official PyTorch implementation of “Blurs Behave Like Ensembles: Spatial Smoothings to Improve Accuracy, Uncertainty, and Robustness”.
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