A generic Mixture Density Networks (MDN) implementation for distribution and uncertainty estimation by using Keras (TensorFlow)

Measuring uncertainty in Deep Learning for Medical Imaging using Monte Carlo Dropout

Experiments used in "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning"

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

Rapidly exploring Random Belief Tree

Modelling heterogeneous distributions with an Uncountable Mixture of Asymmetric Laplacians

Natural Gradient, Variational Inference

Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors

Estimating uncertainty of neural networks for automated screening of Diabetic Retinopathy using the PyTorch framework. Generated visual explanation of the deep learning system to convey the pixels in the image that influences its decision Integrated Gradient method.

Uncertainty-Aware RGB-D Visual Odometry

Official implementation of "Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision", CVPR Workshops 2020.

Built a regression model that predicts the expected days of hospitalization time and an uncertainty range estimation.

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

Code for Paper: Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data

Different implementations of Bayesian neural networks for uncertainty estimation. The uncertainty estimation is utilized for efficient exploration in reinforcement learning.

Tutorial materials for Social Simulation Fest 2021