Semantic Scholar Open Access 2020 838 sitasi

Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users

Laurent Valentin Jospin Wray L. Buntine F. Boussaid Hamid Laga Bennamoun

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Abstrak

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.

Topik & Kata Kunci

Computer Science Mathematics

Penulis (5)

Laurent Valentin Jospin

Wray L. Buntine

F. Boussaid

Hamid Laga

Bennamoun

Format Sitasi

APA MLA BibTeX

Jospin, L.V., Buntine, W.L., Boussaid, F., Laga, H., Bennamoun (2020). Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users. https://doi.org/10.1109/MCI.2022.3155327

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Lihat di Sumber doi.org/10.1109/MCI.2022.3155327

Informasi Jurnal

Tahun Terbit: 2020
Bahasa: en
Total Sitasi: 838×
Sumber Database: Semantic Scholar
DOI: 10.1109/MCI.2022.3155327
Akses: Open Access ✓