Stars
An inexpensive time-series analysis of ECG for early MI detection.
Developed a method for estimating in-control distribution parameters on a dataset with 209 attributes to carry out the Phase-II analysis and to detect out of control data for future observations .L…
Code for the ICCV 2019 paper "Sampling-free Epistemic Uncertainty Estimation Using Approximated Variance Propagation"
Full Bayesian inference for neural networks using TensorFlow
CNN, RNN, and Bayesian NN classification for ECG time-series (using TensorFlow in Swift and Python)
giacomodeodato / Bayesian-Neural-Networks-for-Cellular-Image-Classification-and-Uncertainty-Analysis
Code to reproduce the results from the corresponding preprint
Analysis of bayesian classification for image content recognition, generating synthetic data for testing, assuming gaussian distribution of data. Also bivariate gaussian distribution parameters est…
Hyperpatameter Bayesian Optimization for Image Classification in PyTorch
A curated list of resources dedicated to bayesian deep learning
Code to accompany the paper 'Improving model calibration with accuracy versus uncertainty optimization'.
This repository aims to create various notebooks which can be used to fit data in a Bayesian fashion, using MCMC sampling to generate predictions and model uncertainties.
Pytorch Bayesian UNet model for segmentation and uncertainty prediction
Research on the topic of uncertainty prediction, using bayesian approaches, calibration and popular methods like MC Dropout or Natural Gradient
Using bayesian neural networks to measure uncertainty in predictions
Baysian Deep Learning with fast.ai, using a pretrained resnet50
CIFAR-10 dataset has 60,000 32x32 rgb images and those images are recognized using ResNet50 deep learning model with an accuracy of 71%.
Contains all the code of my bachelor thesis Uncertainty quantification using bayesian modeling for finite volume neural networks
Uncertainty quantification using Bayesian neural networks in classification (MIDL 2018, CSDA)
Uncertainty quantification of molecular property prediction using Bayesian deep learning
Implementation of Segnet, FCN, UNet , PSPNet and other models in Keras.
Master 1 project . Recently, deep learning models have drastically outperformed benchmark results on miscellaneous datasets. It’s has become state-of-art in various domain especially computer visio…
This is a PyTorch implementation of a Bayesian Convolutional Neural Network (BCNN) for Semantic Scene Completion on the SUNCG dataset. Given a depth image the network outputs a semantic segmentatio…
A repository contains the code for various semantic segmentation in TensorFlow and PyTorch framework.