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| Skaidrojams Gausa maisījuma modelis× | Variacionālais autoenkoders× | |
|---|---|---|
| Nozare≠ | Mašīnmācīšanās | Dziļā mācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 1995–2020s | 2014 |
| Autors≠ | Reynolds, D. A. & Rose, R. C. (GMM); explainability extensions by various authors | Kingma, D. P. & Welling, M. |
| Tips≠ | Probabilistic clustering with post-hoc or built-in explainability | Deep generative latent-variable model (encoder–decoder) |
| Pirmavots≠ | Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective (Ch. 11 — Mixture Models). MIT Press. ISBN: 978-0-262-01802-9 | Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR). link ↗ |
| Citi nosaukumi | X-GMM, Interpretable GMM, Explainable GMM, Transparent Gaussian Mixture Model | Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model |
| Saistītās≠ | 3 | 5 |
| Kopsavilkums≠ | An Explainable Gaussian Mixture Model (X-GMM) augments the classical GMM probabilistic clustering framework with transparency mechanisms — such as feature-attribution scores, component-level summaries, or sparse covariance structures — so that discovered clusters and density estimates can be understood, communicated, and audited by human experts. | 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. |
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