Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users
By Laurent Valentin Jospin, Wray L. Buntine, Farid Boussaïd, Hamid Laga, Mohammed Bennamoun. IEEE Computational Intelligence Magazine, 2020
908 citations recorded by the source corpus.
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 deep learning practitioners with an overview of the relevant literature and a complete toolset to design, implement, train, use and evaluate Bayesian neural networks, i.e., stochastic artificial neural networks trained using Bayesian methods.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/9eebd3c7971a239cf69a0358563f397bd8a8f99c
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Dana Rezazadegan
- Vladimir Makarenkov
- Abbas Khosravi
- Mohammad Ghavamzadeh
- Xiaochun Cao
- Paul W. Fieguth
- Saeid Nahavandi
- Sadiq Hussain
Citations in the corpus
- A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges (2020, methodology): “BNNs/BDL are robust to overfitting problem and can be trained on both small and big datasets [27].”