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Author: kumar-shridhar

README.md

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-# Bayesian CNN with Variational Inference for Image Recognition task
+# Bayesian CNN with Variational Inference
+
+[![Python 3.7+](https://img.shields.io/badge/python-3.7+-blue.svg)](https://www.python.org/downloads/release/python-376/)
+[![Pytorch 1.3](https://img.shields.io/badge/pytorch-1.3.1-blue.svg)](https://pytorch.org/)
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://github.com/kumar-shridhar/PyTorch-BayesianCNN/blob/master/LICENSE)
+[![arxiv](https://img.shields.io/badge/stat.ML-arXiv%3A2002.02797-B31B1B.svg)](https://arxiv.org/abs/1901.02731)
 
 We introduce **Bayesian convolutional neural networks with variational inference**, a variant of convolutional neural networks (CNNs), in which the intractable posterior probability distributions over weights are inferred by **Bayes by Backprop**. We demonstrate how our proposed variational inference method achieves performances equivalent to frequentist inference in identical architectures on several datasets (MNIST, CIFAR10, CIFAR100) as described in the [paper](https://arxiv.org/abs/1901.02731).