Bayesian Inference

621 papers with code • 1 benchmarks • 7 datasets

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

Most implemented papers

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.

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

yaringal/DropoutUncertaintyExps 6 Jun 2015

In comparison, Bayesian models offer a mathematically grounded framework to reason about model uncertainty, but usually come with a prohibitive computational cost.

Semi-Supervised Learning with Deep Generative Models

dpkingma/nips14-ssl NeurIPS 2014

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.

Variational Autoencoders for Collaborative Filtering

dawenl/vae_cf 16 Feb 2018

This non-linear probabilistic model enables us to go beyond the limited modeling capacity of linear factor models which still largely dominate collaborative filtering research. We introduce a generative model with multinomial likelihood and use Bayesian inference for parameter estimation.

Bayesian regression and Bitcoin

panditanvita/BTCpredictor 6 Oct 2014

In this paper, we discuss the method of Bayesian regression and its efficacy for predicting price variation of Bitcoin, a recently popularized virtual, cryptographic currency.

A Theoretically Grounded Application of Dropout in Recurrent Neural Networks

HKUST-KnowComp/R-Net NeurIPS 2016

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

Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm

DartML/Stein-Variational-Gradient-Descent NeurIPS 2016

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

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.

Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows

gpapamak/snl 18 May 2018

We present Sequential Neural Likelihood (SNL), a new method for Bayesian inference in simulator models, where the likelihood is intractable but simulating data from the model is possible.

A Simple Baseline for Bayesian Uncertainty in Deep Learning

wjmaddox/swa_gaussian NeurIPS 2019

We propose SWA-Gaussian (SWAG), a simple, scalable, and general purpose approach for uncertainty representation and calibration in deep learning.