Hands-on Bayesian Neural Networks – a Tutorial for Deep Learning Users

07/14/2020 ∙ by Laurent Valentin Jospin, et al. ∙ 356 ∙ share

Modern deep learning methods have equipped researchers and engineers with incredibly powerful tools to tackle problems that previously seemed impossible. However, since deep learning methods operate as black boxes, the uncertainty associated with their predictions is often challenging to quantify. Bayesian statistics offer a formalism to understand and quantify the uncertainty associated with deep neural networks predictions. This paper provides a tutorial for researchers and scientists who are using machine learning, especially deep learning, with an overview of the relevant literature and a complete toolset to design, implement, train, use and evaluate Bayesian neural networks.

READ FULL TEXT

The Relevance of Bayesian Layer Positioning to Model Uncertainty in Deep Bayesian Active Learning

One of the main challenges of deep learning tools is their inability to ...
11/29/2018 ∙ by Jiaming Zeng, et al. ∙ 0 ∙ share

read it
BayesOD: A Bayesian Approach for Uncertainty Estimation in Deep Object Detectors

One of the challenging aspects of incorporating deep neural networks int...
03/09/2019 ∙ by Ali Harakeh, et al. ∙ 28 ∙ share

read it
URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks

While deep learning methods continue to improve in predictive accuracy o...
07/08/2020 ∙ by Meet P. Vadera, et al. ∙ 0 ∙ share

read it
BDNNSurv: Bayesian deep neural networks for survival analysis using pseudo values

There has been increasing interest in modeling survival data using deep ...
01/07/2021 ∙ by Dai Feng, et al. ∙ 13 ∙ share

read it
Ramifications of Approximate Posterior Inference for Bayesian Deep Learning in Adversarial and Out-of-Distribution Settings

Deep neural networks have been successful in diverse discriminative clas...
09/03/2020 ∙ by John Mitros, et al. ∙ 0 ∙ share

read it
Structure preserving deep learning

Over the past few years, deep learning has risen to the foreground as a ...
06/05/2020 ∙ by Elena Celledoni, et al. ∙ 0 ∙ share

read it
Graphs for deep learning representations

In recent years, Deep Learning methods have achieved state of the art pe...
12/14/2020 ∙ by Carlos Lassance, et al. ∙ 78 ∙ share

read it

Hands-on Bayesian Neural Networks – a Tutorial for Deep Learning Users

Authors

The Relevance of Bayesian Layer Positioning to Model Uncertainty in Deep Bayesian Active Learning

BayesOD: A Bayesian Approach for Uncertainty Estimation in Deep Object Detectors

URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks

BDNNSurv: Bayesian deep neural networks for survival analysis using pseudo values

Ramifications of Approximate Posterior Inference for Bayesian Deep Learning in Adversarial and Out-of-Distribution Settings

Structure preserving deep learning

Graphs for deep learning representations

This week in AI