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This folder contains the Python implementation of the VBNN model presented in the submitted manuscript "Critical Temperature Prediction for a Superconductor: A Variational Bayesian Neural Network Approach".

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2020年9月30日 · This study proposes a VBNN model for ensemble flood forecasting. To avoid heavy computational costs in the traditional BNN, the VBNN combines the variational inference with BNN, and uses the variational distribution to approximate the true posterior distribution of model parameters.

This repository provides a Python implementation of the Variational Bow tie neural network (VBNN). The VBNN is described and derived in a Variational Bayesian Bow tie Neural Networks with Shrinkage paper. VBNN_class.py which contains the main code for the network. helpers.py which contains utilities for easy training and testing.

Conv-VAE-VBnn is a hybird deep-learning approach for rapidly modeling natural gas release and dispersion, which is essentially a probabilistic Convolutional-Variational Autoencoder-Variational Bayesian neural network.

2023年10月30日 · Based on the complexity of the deep neural network (DNN), this paper provides an assessment of the loss in classification accuracy due to VB’s use and guidelines on the characterization of the prior distributions and the variational family.

In recent years, Bayesian neural networks have been used in image detection [33], NLP [34], and time series prediction [35], etc. Zhan [36] used variational Bayesian neural network (VBNN) to...

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2022年12月1日 · Variational Neural Networks (VNNs) provide a tool to estimate model uncertainty. PyTorch and Jax implementations of VNNs are provided. PyTorch implementation shows how to include VNNs into any project. Jax implementation reproduces the uncertainty quality experiments.