# Autoencoder based Randomized Learning of Feedforward Neural Networks for Regression

arXiv: https://arxiv.org/abs/2107.01711 · HTML: https://mlsanity.com/paper/2107.01711/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 4 Jul 2021.

## Builds on (most cited first, 5 of 5)

- [Stochastic Configuration Networks: Fundamentals and Algorithms](https://mlsanity.com/paper/1702.03180/) (2017)
- [A Method of Generating Random Weights and Biases in Feedforward Neural Networks with Random Hidden Nodes](https://mlsanity.com/paper/1710.04874/) (2017)
- [Generating Random Parameters in Feedforward Neural Networks with Random Hidden Nodes: Drawbacks of the Standard Method and How to Improve It](https://mlsanity.com/paper/1908.05864/) (2019)
- [Stacked Autoencoder Based Deep Random Vector Functional Link Neural Network for Classification](https://mlsanity.com/paper/1910.01858/) (2019)
- [Data-Driven Randomized Learning of Feedforward Neural Networks](https://mlsanity.com/paper/1908.03891/) (2019)

## Abstract (arXiv)

Feedforward neural networks are widely used as universal predictive models to fit data distribution. Common gradient-based learning, however, suffers from many drawbacks making the training process ineffective and time-consuming. Alternative randomized learning does not use gradients but selects hidden node parameters randomly. This makes the training process extremely fast. However, the problem in randomized learning is how to determine the random parameters. A recently proposed method uses autoencoders for unsupervised parameter learning. This method showed superior performance on classification tasks. In this work, we apply this method to regression problems, and, finding that it has some drawbacks, we show how to improve it. We propose a learning method of autoencoders that controls the produced random weights. We also propose how to determine the biases of hidden nodes. We empirically compare autoencoder based learning with other randomized learning methods proposed recently for regression and find that despite the proposed improvement of the autoencoder based learning, it does not outperform its competitors in fitting accuracy. Moreover, the method is much more complex than its competitors.