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Found 4 papers, 1 papers with code
Autoencoders
An autoencoder is a specific type of a neural network, which is mainly designed to encode the input into a compressed and meaningful representation, and then decode it back such that the reconstructed input is similar as possible to the original one.
Detecting Change in Seasonal Pattern via Autoencoder and Temporal Regularization
Change-point detection problem consists of discovering abrupt property changes in the generation process of time-series.
An ETF view of Dropout regularization
Dropout is a popular regularization technique in deep learning.
Improved Training for Self-Training by Confidence Assessments
Our suggested approaches were applied on the MNIST data-set as a proof of concept for a vision classification task and on the ADE20K data-set in order to tackle the semi-supervised semantic segmentation problem.
