Bayesian Inference

369 papers with code • 0 benchmarks • 4 datasets

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

Greatest papers with code

Weight Uncertainty in Neural Networks

tensorflow/models 20 May 2015

We introduce a new, efficient, principled and backpropagation-compatible algorithm for learning a probability distribution on the weights of a neural network, called Bayes by Backprop.

Bayesian Inference General Classification

How Good is the Bayes Posterior in Deep Neural Networks Really?

google-research/google-research ICML 2020

In this work we cast doubt on the current understanding of Bayes posteriors in popular deep neural networks: we demonstrate through careful MCMC sampling that the posterior predictive induced by the Bayes posterior yields systematically worse predictions compared to simpler methods including point estimates obtained from SGD.

Bayesian Inference

Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm

pyro-ppl/pyro NeurIPS 2016

We propose a general purpose variational inference algorithm that forms a natural counterpart of gradient descent for optimization.

Bayesian Inference Variational Inference

ZhuSuan: A Library for Bayesian Deep Learning

thu-ml/zhusuan 18 Sep 2017

In this paper we introduce ZhuSuan, a python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and deep learning.

Probabilistic Programming

Neural Tangents: Fast and Easy Infinite Neural Networks in Python

google/neural-tangents ICLR 2020

Neural Tangents is a library designed to enable research into infinite-width neural networks.

Bayesian Inference

Simulation-Based Inference for Global Health Decisions

mrc-ide/covid-sim 14 May 2020

The COVID-19 pandemic has highlighted the importance of in-silico epidemiological modelling in predicting the dynamics of infectious diseases to inform health policy and decision makers about suitable prevention and containment strategies.

Bayesian Inference Epidemiology

A Scalable Laplace Approximation for Neural Networks

JavierAntoran/Bayesian-Neural-Networks ICLR 2018

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace and more

Bayesian Inference

A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference

kumar-shridhar/PyTorch-BayesianCNN 8 Jan 2019

In this paper, Bayesian Convolutional Neural Network (BayesCNN) using Variational Inference is proposed, that introduces probability distribution over the weights.

Bayesian Inference General Classification +3

Uncertainty Estimations by Softplus normalization in Bayesian Convolutional Neural Networks with Variational Inference

kumar-shridhar/BayesianConvNet 15 Jun 2018

On multiple datasets in supervised learning settings (MNIST, CIFAR-10, CIFAR-100), this variational inference method achieves performances equivalent to frequentist inference in identical architectures, while the two desiderata, a measure for uncertainty and regularization are incorporated naturally.

Bayesian Inference General Classification +1

Variational Dropout and the Local Reparameterization Trick

kumar-shridhar/BayesianConvNet NeurIPS 2015

Our method allows inference of more flexibly parameterized posteriors; specifically, we propose variational dropout, a generalization of Gaussian dropout where the dropout rates are learned, often leading to better models.

Bayesian Inference