Function regression using the forward forward training and inferring paradigm

Shivam Padmani, Akshay Joshi

Keywords: Deep Learning, Forward Forward algorithm, Function Regression, Physical Neural Networks, Kolmogrov Arnold Networks

TL;DR: Training Neural Networks to perform function regression without backpropagation using the Forward Forward method.

Abstract: Function regression/approximation is a fundamental application of machine learning. Neural networks (NNs) can be easily trained for function regression using a sufficient number of neurons and epochs. The forward-forward learning algorithm is a novel approach for training neural networks without backpropagation, and is well suited for implementation in neuromorphic computing and physical analogs for neural networks. To the best of the authors' knowledge, the Forward Forward paradigm of training and inferencing NNs is currently only restricted to classification tasks. This paper introduces a new methodology for approximating functions (function regression) using the Forward-Forward algorithm. The paper further evaluates the developed methodology on univariate and multivariate functions and benchmarks the framework on open source regression data, while comparing its performance to other regression techniques.

Primary Area: other topics in machine learning (i.e., none of the above)

Submission Number: 13548