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Bayesian Inference

99 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

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

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

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

15 Jun 2018kumar-shridhar/BayesianConvNet

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

Bayesian regression and Bitcoin

6 Oct 2014panditanvita/BTCpredictor

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.

BAYESIAN INFERENCE

Variational Autoencoders for Collaborative Filtering

16 Feb 2018dawenl/vae_cf

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 INFERENCE COLLABORATIVE FILTERING LANGUAGE MODELLING

Stochastic Backpropagation and Approximate Inference in Deep Generative Models

16 Jan 2014clinicalml/structuredinference

We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised class of deep, directed generative models, endowed with a new algorithm for scalable inference and learning.

BAYESIAN INFERENCE

A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference

8 Jan 2019kumar-shridhar/Master-Thesis-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

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

6 Jun 2015mrahtz/learning-from-human-preferences

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

BAYESIAN INFERENCE