Natural Gradient Boosting for Probabilistic Prediction
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Updated
Feb 21, 2024 - Python
Natural Gradient Boosting for Probabilistic Prediction
Uncertainty Toolbox: a Python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization
A generic Mixture Density Networks (MDN) implementation for distribution and uncertainty estimation by using Keras (TensorFlow)
Lightweight, useful implementation of conformal prediction on real data.
Literature survey, paper reviews, experimental setups and a collection of implementations for baselines methods for predictive uncertainty estimation in deep learning models.
A library for Bayesian neural network layers and uncertainty estimation in Deep Learning extending the core of PyTorch
An extension of XGBoost to probabilistic modelling
A Library for Uncertainty Quantification.
This repository contains a collection of surveys, datasets, papers, and codes, for predictive uncertainty estimation in deep learning models.
To Trust Or Not To Trust A Classifier. A measure of uncertainty for any trained (possibly black-box) classifier which is more effective than the classifier's own implied confidence (e.g. softmax probability for a neural network).
Code for the Neural Processes website and replication of 4 papers on NPs. Pytorch implementation.
Awesome-LLM-Robustness: a curated list of Uncertainty, Reliability and Robustness in Large Language Models
Curated list of open source tooling for data-centric AI on unstructured data.
Official implementation of "Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision", CVPR Workshops 2020.
This repository provides the code used to create the results presented in "Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles".
Sandia Uncertainty Quantification Toolkit
CVPR 2020 - On the uncertainty of self-supervised monocular depth estimation
Various Conformal Prediction methods implemented from scratch in pure NumPy for an educational purpose.
An extension of LightGBM to probabilistic modelling
[ICCV 2021 Oral] Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation
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