Uncertainty Toolbox: a Python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization
翻译 - 用于预测不确定性量化,校准,指标和可视化的python工具箱
#计算机科学#Natural Gradient Boosting for Probabilistic Prediction
#计算机科学#A Library for Uncertainty Quantification.
#自然语言处理#Lightweight, useful implementation of conformal prediction on real data.
#Awesome#Awesome-LLM-Robustness: a curated list of Uncertainty, Reliability and Robustness in Large Language Models
#自然语言处理#Curated list of open source tooling for data-centric AI on unstructured data.
#计算机科学#This repository contains a collection of surveys, datasets, papers, and codes, for predictive uncertainty estimation in deep learning models.
#计算机科学#Literature survey, paper reviews, experimental setups and a collection of implementations for baselines methods for predictive uncertainty estimation in deep learning models.
#Awesome#A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD and MSc theses, articles and open-source libraries.
#计算机科学#An extension of XGBoost to probabilistic modelling
#计算机科学#A library for Bayesian neural network layers and uncertainty estimation in Deep Learning extending the core of PyTorch
#计算机科学#A generic Mixture Density Networks (MDN) implementation for distribution and uncertainty estimation by using Keras (TensorFlow)
👋 Puncc is a python library for predictive uncertainty quantification using conformal prediction.
[TMECH'2024] Official codes of ”PALoc: Advancing SLAM Benchmarking with Prior-Assisted 6-DoF Trajectory Generation and Uncertainty Estimation“
#计算机科学#An extension of LightGBM to probabilistic modelling
CVPR 2020 - On the uncertainty of self-supervised monocular depth estimation
#计算机科学#[ICCV 2021 Oral] Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation
#计算机科学#Quantile Regression Forests compatible with scikit-learn.
#计算机科学#[CVPR 2022 Oral] Multi-View Depth Estimation by Fusing Single-View Depth Probability with Multi-View Geometry
#计算机科学#Code for the Neural Processes website and replication of 4 papers on NPs. Pytorch implementation.