Experiments used in "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning"
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Updated
Aug 9, 2018 - Python
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.
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.
Official implementation of "Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision", CVPR Workshops 2020.
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.
Uncertainty Estimation Using Deep Neural Network and Gradient Boosting Methods
Tracking an embodied AI agent to estimate movement from observations
Official repository for the paper "Masksembles for Uncertainty Estimation" (CVPR2021).
Anomaly detection in maritime navigation with evidential deep learning based on graph-based clustering
Code for evaluating uncertainty estimation methods for Transformer-based architectures in natural language understanding tasks.
Code for "Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors." (ICLR 2021)
PyTorch implementation for "Temperature as Uncertainty in Contrastive Learning" (https://arxiv.org/abs/2110.04403).
This repository provides the code used to create the results presented in "Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles".
Repository accompanying the paper "Uncertainty estimation for deep learning-based automated analysis of 12-lead electrocardiograms", accepted in European Heart Journal Digital Health.
Official Repo for the paper "Reconstructing Sinus Anatomy from Endoscopic Video -- Towards a Radiation-free Approach for Quantitative Longitudinal Assessment" (MICCAI 2020)
Repository for "Improving evidential deep learning via multi-task learning," published in AAAI2022
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