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

mfbalin/Concrete-Autoencoders 27 Jan 2019

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

vojha-code/multi-output-neural-tree 9 Oct 2020

We propose an algorithm and a new method to tackle the classification problems.