My implementation of the paper "Simple and Scalable Predictive Uncertainty estimation using Deep Ensembles"

Guided Perturbations: Self-Corrective Behavior in Convolutional Neural Networks

An implementation of natural parameter networks and its extension to GRUs in PyTorch

A validation study for the application of quantile regression neural networks to Bayesian remote sensing retrievals

A pytorch implementation of MCDO(Monte-Carlo Dropout methods)

Attempt to reproduce the toy experiment of http://bit.ly/2C9Z8St with an ensemble of nets and with dropout.

A CNN based Depth, Optical Flow, Flow Uncertainty and Camera Pose Prediction pipeline

Experiments from Efficient Training of Interval Neural Networks for Imprecise Training Data

This repository is for implementation of the paper Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles. This algorithm quantifies predictive predictive uncertainty in non-Bayesian NN with Deep Ensemble Model. Contribution of this paper is that it describes simple and scalable method for estimating predictive uncertainty es…

Probabilistic framework for solving Visual Dialog

Observations and notes to understand the workings of neural network models and other thought experiments using Tensorflow

A neural-network based image classifier that quantifies its uncertainty using Bayesian methods, as described in Kendall and Gal (2017)

Implementation of the MNIST experiment for Monte Carlo Dropout from http://mlg.eng.cam.ac.uk/yarin/PDFs/NIPS_2015_bayesian_convnets.pdf

A list of papers on Active Learning and Uncertainty Estimation for Neural Networks.

A repository about Robust Deep Neural Networks with Uncertainty, Local Competition and Error-Correcting-Output-Codes in TensorFlow.

The second-moment loss (SML) is a novel training objective for dropout-based regression networks that yields improved uncertainty estimates.

Code for "Deal: Deep Evidential Active Learning for Image Classification" (ICMLA 2020)

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 Deep Learning" (Loquercio, Segù, Scaramuzza. RA-L 2020).