Papers for Bayesian-NN

Implementation of Bayesian Recurrent Neural Networks by Fortunato et. al

We use a modified neural network instead of Gaussian process for Bayesian optimization.

AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'

CNN, RNN, and Bayesian NN classification for ECG time-series (using TensorFlow in Swift and Python)

Classification uncertainty using Bayesian neural networks

Predict earthquake rupture probability with bayesian deep learning

Jupyter notebook with the code of a probabilistic neural network in PyTorch

Implementing a bayesian neural network in TensorFlow

This project uses Bayesian Optimization to find the optimal hyperparameters for a fully-connected feed-forward neural network used to estimate the heating load on a building given eight different input features.

Basics of AI including PyPlot tutorials, Fuzzy Logic, Genetic Algorithms, Bayesian Networks, Perceptrons and NN's.

Bayesian Neural Networks in PyTorch

A toy Bayesian neural network example.

2021 Python, Deep Archetypal Analysis (Bayesian NN, Deep Variational Autoencoder), Compressed Sensing, Kmeans, setuptools, tensorflow (keras), tensorflow_probability, sklearn (cluster, metrics, model_selection, preprocessing), pandas, typing

Framework for the dynamic optimization of financial portfolio that employs the neural network with stochastic weights and Bayesian inference

BayesianNN: Implementation of BNNs for virtual monitoring (offshore wind farms) and uncertainty quantification

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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 estimates from NN.

Pre-Trained TensorFlow Bayesian Neural Networks in Fortran

For testing whether Bayesian NN priors are generalization-agnostic...or nah.

We investigate different algorithms such as Neural Network, Bayesian Naive Bayes, K-NN, K-Means Clustering and Logistic Regression along with hybrid techniques involving the above used algorithms for the diagnosis of heart disease.

Demonstration for using dropout as a means of bayesian approximation in Deep Neural Network

这是一个示例性的小项目，展示了如何将因果推断、数据增广和贝叶斯深度学习结合起来，尝试在小数据场景下的效果

This project predicts the likelihood for heart failure. The project takes place in three parts: exploratory data analysis (EDA) and data preparation, the creation of three initial binary classification models including logistic regression, random forests, and a neural network. Then, the hyperparameters of the neural net were optimized using Bayesian Optimization.

Implements the Nearest Neighbor Dirichlet Mixture (NN-DM) algorithm for embarrassingly parallel pseudo-Bayesian density estimation.

Reduced Bayesian Neural Networks are Deterministic Neural Networks with a Bayesian subset of weights. redBNN class computes a MAP estimate of the entire NN and then performs Bayesian inference on a chosen layer or block.

Lightweight Bayesian neural net library for tensorflow

Bayesian Neural Network

In this repository is a copy of my research of Bayesian neuronal networks with a stan implementation of NN with 1 hidden layer and 4 neurons

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