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Bayesian Inference

128 papers with code · Methodology

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 REGRESSION

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

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 REGRESSION

Semi-Supervised Learning with Deep Generative Models

NeurIPS 2014 probtorch/probtorch

The ever-increasing size of modern data sets combined with the difficulty of obtaining label information has made semi-supervised learning one of the problems of significant practical importance in modern data analysis.

BAYESIAN INFERENCE

A Theoretically Grounded Application of Dropout in Recurrent Neural Networks

NeurIPS 2016 HKUST-KnowComp/R-Net

Recent results at the intersection of Bayesian modelling and deep learning offer a Bayesian interpretation of common deep learning techniques such as dropout.

BAYESIAN INFERENCE LANGUAGE MODELLING SENTIMENT ANALYSIS

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 IMAGE CLASSIFICATION IMAGE SUPER-RESOLUTION

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

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

Variational Dropout and the Local Reparameterization Trick

arXiv 2015 JavierAntoran/Bayesian-Neural-Networks

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

Variational Dropout and the Local Reparameterization Trick

NeurIPS 2015 JavierAntoran/Bayesian-Neural-Networks

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