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

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Greatest papers with code

Weight Uncertainty in Neural Networks

20 May 2015tensorflow/models

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 CLASSIFICATION

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

ICML 2020 google-research/google-research

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

NeurIPS 2016 pyro-ppl/pyro

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

18 Sep 2017thu-ml/zhusuan

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

ICLR 2020 google/neural-tangents

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

BAYESIAN INFERENCE

Simulation-Based Inference for Global Health Decisions

14 May 2020mrc-ide/covid-sim

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

ICLR 2018 JavierAntoran/Bayesian-Neural-Networks

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

BAYESIAN INFERENCE

SAME but Different: Fast and High-Quality Gibbs Parameter Estimation

18 Sep 2014BIDData/BIDMach

SAME (State Augmentation for Marginal Estimation) \cite{Doucet99, Doucet02} is an approach to MAP parameter estimation which gives improved parameter estimates over direct Gibbs sampling.

BAYESIAN INFERENCE

A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference

8 Jan 2019kumar-shridhar/PyTorch-BayesianCNN

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

BAYESIAN INFERENCE CLASSIFICATION IMAGE CLASSIFICATION IMAGE SUPER-RESOLUTION VARIATIONAL INFERENCE

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

15 Jun 2018kumar-shridhar/PyTorch-BayesianCNN

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 CLASSIFICATION VARIATIONAL INFERENCE