-
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 ...
read it
-
BayesOD: A Bayesian Approach for Uncertainty Estimation in Deep Object Detectors
One of the challenging aspects of incorporating deep neural networks int...
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...
read it
-
BDNNSurv: Bayesian deep neural networks for survival analysis using pseudo values
There has been increasing interest in modeling survival data using deep ...
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...
read it
-
Structure preserving deep learning
Over the past few years, deep learning has risen to the foreground as a ...
read it
-
Graphs for deep learning representations
In recent years, Deep Learning methods have achieved state of the art pe...
read it
Hands-on Bayesian Neural Networks – a Tutorial for Deep Learning Users
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
Comments
There are no comments yet.