This repository provides the code used to implement the framework to provide deep learning models with total uncertainty estimates as described in "A General Framework for Uncertainty Estimation in…

Paper-Reproduce: (Sensors-MDPI) Remaining Useful Life Prediction Method for Bearings Based on LSTM with Uncertainty Quantification

Examples of implementations of WTTE-RNN

WTTE-RNN a framework for churn and time to event prediction

A PyTorch implementation of Weibull Time to Event Recurrent Neural Networks for churn prediction tasks.

Weibull Time To Event prediction with PyTorch and deep learning

Time to failure (TTF) using Weibull distribution and recurrent neural networks in Keras.

Survival analsyis and time-to-failure predictive modeling using Weibull distributions and Recurrent Neural Networks in Keras

Official implementation of https://arxiv.org/abs/1911.06256. Bayesian and frequentist deep learning models for remaining useful life (RUL) estimation implemented in PyTorch.

PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

Bayesian LSTM Implementation in PyTorch

Master Thesis on Bayesian Convolutional Neural Network using Variational Inference

[2020-CVPR]Data Uncertainty Learning in Face Recognition

Official implementation of our NeurIPS2021 paper: Relative Uncertainty Learning for Facial Expression Recognition

Bayesian Neural Networks to predict RUL on N-CMAPSS

AM207 project: dissect aleatoric and epistemic uncertainty

PyTorch implementation of bayesian neural network [torchbnn]

This project aims to propose a TCN-Based Bayesian neural nework that is used for remaining useful life prediction.

A library for Bayesian neural network layers and uncertainty estimation in Deep Learning extending the core of PyTorch

Using knowledge-informed machine learning on the PRONOSTIA (FEMTO) and IMS bearing data sets. Predict remaining-useful-life (RUL).

A tutorial on "Bayesian Compression for Deep Learning" published at NIPS (2017).

a repo sharing Bayesian Neural Network recent papers

Methods to get the probability of a changepoint in a time series.

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more

Tensorflow code for the Bayesian GAN (https://arxiv.org/abs/1705.09558) (NIPS 2017)

Project on Bayesian Deep Learning

A simple and extensible library to create Bayesian Neural Network layers on PyTorch.

Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.