On autoencoder scoring

By Hanna Kamyshanska, Roland Memisevic. 2013

40 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Salvador Garcia
2. Francisco Herrera
3. Francisco Charte
4. M. J. Jesús
5. Alberto Fernández
6. David Charte
7. Martin Längkvist
8. Amy Loutfi

Citations in the corpus

• A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines (2018, methodology): “1) and that minimizes the sum of squared errors is known to be equivalent to obtaining the k principal components of the feature space via PCA [41–43].”
• A review of unsupervised feature learning and deep learning for time-series modeling (2014, methodology): “Even though the auto-encoder is not generative, a probabilistic interpretation can be made using auto-encoder scoring [63,10].”