Variational AutoEncoder for synthetic insurance data
This article explores the application of Variational AutoEncoders (VAEs) to insurance data. Previous research has demonstrated the successful implementation of generative models, especially VAEs, across various domains, such as image recognition, text classification, and recommender systems. However...
Saved in:
| Main Authors: | Charlotte Jamotton, Donatien Hainaut |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | Intelligent Systems with Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667305324001297 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Generative autoencoder to prevent overregularization of variational autoencoder
by: YoungMin Ko, et al.
Published: (2025-02-01) -
Robust Synthetic Data Generation for Sequential Financial Models Using Hybrid Variational Autoencoder–Markov Chain Monte Carlo Architectures
by: Francesco Bruni Prenestino, et al.
Published: (2025-02-01) -
Into the latent space of capacitive sensors: interpolation and synthetic data generation using variational autoencoders
by: Miguel Monteagudo Honrubia, et al.
Published: (2025-01-01) -
Optimal Dimensionality Reduction using Conditional Variational AutoEncoder
by: Sana Boussam, et al.
Published: (2025-06-01) -
Aviation Fuel Pump Fault Diagnosis Based on Conditional Variational Self-Encoder Adaptive Synthetic Less Data Enhancement
by: Tiejun Liu, et al.
Published: (2025-07-01)