Python Uncertainty-calibration Resources

Bayesian-Torch is a library of neural network layers and utilities extending the core of PyTorch to enable the user to perform stochastic variational inference in Bayesian deep neural networks. Bayesian-Torch is designed to be flexible and seamless in extending a deterministic deep neural network architecture to corresponding Bayesian form by simply replacing the deterministic layers with Bayesian layers.

Representative Batch Normalization (RBN) with Feature Calibration The official implementation of the CVPR2021 oral paper: Representative Batch Normali

Sensitivity Analysis Library (SALib) Python implementations of commonly used sensitivity analysis methods. Useful in systems modeling to calculate the

Our article on Towards Data Science introduces the package and provides background information. Pytorch Forecasting aims to ease state-of-the-art time

Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking Ning Wang, Wengang Zhou, Jie Wang, and Houqiang Li Accepted by CVPR

Gelato Bayesian dessert for Lasagne Recent results in Bayesian statistics for constructing robust neural networks have proved that it is one of the be

mrcal is a generic toolkit to solve calibration and SFM-like problems originating at NASA/JPL. Functionality related to these problems is exposed as a set of C and Python libraries and some commandline tools.

Welcome to the comma.ai Calibration Challenge! Your goal is to predict the direction of travel (in camera frame) from provided dashcam video. This rep