Compară metode

	Autoencoder Variațional Multilingv	Autoencoder Variațional
Domeniu	Învățare profundă	Învățare profundă
Familie	Machine learning	Machine learning
Anul apariției	2017-2018	2014
Autorul original	Multiple research groups (Lample, Conneau et al.; Zhao et al.)	Kingma, D. P. & Welling, M.
Tip	Generative latent-variable model	Deep generative latent-variable model (encoder–decoder)
Sursa seminală	Zhao, T., Zhang, Y., & Eskenazi, M. (2018). Zero-shot dialog generation with cross-domain latent actions. In Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue (pp. 1-10). ACL.	Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR).
Denumiri alternative	ML-VAE, cross-lingual VAE, multilingual latent variable model, multilingual generative autoencoder	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model
Înrudite	5	5
Rezumat	A Multilingual Variational Autoencoder (ML-VAE) extends the standard VAE framework to handle multiple languages within a shared probabilistic latent space. Language-specific encoders map text from each language into a common continuous representation, while language-specific decoders reconstruct or translate that text. This enables cross-lingual generation, style transfer, and representation learning with or without parallel corpora.	The Variational Autoencoder (VAE) is a deep generative latent-variable model, introduced by Diederik Kingma and Max Welling in 2014, that encodes data as a probability distribution in a latent space and samples from that distribution to generate new examples. It is used for data generation, anomaly detection, and feature learning.