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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 …

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Sep 30, 2020 · This study proposes a VBNN model for ensemble flood forecasting. To avoid heavy computational costs in the traditional BNN, the VBNN combines the variational …

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 …

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 …

Oct 30, 2023 · 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 …

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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Dec 1, 2022 · Variational Neural Networks (VNNs) provide a tool to estimate model uncertainty. PyTorch and Jax implementations of VNNs are provided. PyTorch implementation shows how …