General Classification Selection
2 papers with code • 1 benchmarks • 0 datasets
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Most implemented papers
Concrete Autoencoders for Differentiable Feature Selection and Reconstruction
We introduce the concrete autoencoder, an end-to-end differentiable method for global feature selection, which efficiently identifies a subset of the most informative features and simultaneously learns a neural network to reconstruct the input data from the selected features.
Multi-Objective Optimisation of Multi-Output Neural Trees
We propose an algorithm and a new method to tackle the classification problems.
