Original paper
Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users
Volume: 17, Issue: 2, Pages: 29 - 48
Published: Apr 13, 2022
Abstract
Modern deep learning methods constitute incredibly powerful tools to tackle a myriad of challenging problems. 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 network predictions. This tutorial provides an overview of the relevant literature...
Paper Details
Title
Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users
Published Date
Apr 13, 2022
Volume
17
Issue
2
Pages
29 - 48
TrendsPro
You’ll need to upgrade your plan to Pro
Looking to understand a paper’s academic impact over time?
- Scinapse’s Citation Trends graph enables the impact assessment of papers in adjacent fields.
- Assess paper quality within the same journal or volume, irrespective of the year or field, and track the changes in the attention a paper received over time.
Citation AnalysisPro
You’ll need to upgrade your plan to Pro
Looking to understand the true influence of a researcher’s work across journals & affiliations?
- Scinapse’s Top 10 Citation Journals & Affiliations graph reveals the quality and authenticity of citations received by a paper.
- Discover whether citations have been inflated due to self-citations, or if citations include institutional bias.