Function regression using the forward forward training and inferring paradigm

• URL: http://arxiv.org/abs/2510.06762v2
• Date: Wed, 15 Oct 2025 15:30:47 GMT
• Title: Function regression using the forward forward training and inferring paradigm
• Authors: Shivam Padmani, Akshay Joshi,
• Abstract summary: Forward-Forward learning algorithm is a novel approach for training neural networks without backpropagation.<n>This paper introduces a new methodology for approximating functions (function regression) using the Forward-Forward algorithm.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. Furthermore, the paper evaluates the developed methodology on univariate and multivariate functions, and provides preliminary studies of extending the proposed Forward-Forward regression to Kolmogorov Arnold Networks, and Deep Physical Neural Networks.

Related papers

• Faster Predictive Coding Networks via Better Initialization [52.419343840654186]
  We propose a new technique for predictive coding networks that aims to preserve the iterative progress made on previous training samples.<n>Our experiments demonstrate substantial improvements in convergence speed and final test loss in both supervised and unsupervised settings.
  arXiv  Detail & Related papers  (2026-01-28T08:52:19Z)
• Weights initialization of neural networks for function approximation [0.9099663022952497]
  Neural network-based function approximation plays a pivotal role in the advancement of scientific computing and machine learning.<n>We propose a reusable framework based on on basis function pretraining.<n>In this approach, basis neural networks are first trained to approximate families of structural correspondences on a reference domain.<n>Their learned parameters are then used to initialize networks for more complex target functions.
  arXiv  Detail & Related papers  (2025-10-09T19:56:26Z)
• Continual Learning via Sequential Function-Space Variational Inference [65.96686740015902]
  We propose an objective derived by formulating continual learning as sequential function-space variational inference. Compared to objectives that directly regularize neural network predictions, the proposed objective allows for more flexible variational distributions. We demonstrate that, across a range of task sequences, neural networks trained via sequential function-space variational inference achieve better predictive accuracy than networks trained with related methods.
  arXiv  Detail & Related papers  (2023-12-28T18:44:32Z)
• Efficient and Flexible Neural Network Training through Layer-wise Feedback Propagation [49.44309457870649]
  Layer-wise Feedback feedback (LFP) is a novel training principle for neural network-like predictors.<n>LFP decomposes a reward to individual neurons based on their respective contributions.<n>Our method then implements a greedy reinforcing approach helpful parts of the network and weakening harmful ones.
  arXiv  Detail & Related papers  (2023-08-23T10:48:28Z)
• Using Linear Regression for Iteratively Training Neural Networks [4.873362301533824]
  We present a simple linear regression based approach for learning the weights and biases of a neural network. The approach is intended to be to larger, more complex architectures.
  arXiv  Detail & Related papers  (2023-07-11T11:53:25Z)
• Contrastive-Signal-Dependent Plasticity: Self-Supervised Learning in Spiking Neural Circuits [61.94533459151743]
  This work addresses the challenge of designing neurobiologically-motivated schemes for adjusting the synapses of spiking networks. Our experimental simulations demonstrate a consistent advantage over other biologically-plausible approaches when training recurrent spiking networks.
  arXiv  Detail & Related papers  (2023-03-30T02:40:28Z)
• Global quantitative robustness of regression feed-forward neural networks [0.0]
  We adapt the notion of the regression breakdown point to regression neural networks. We compare the performance, measured by the out-of-sample loss, by a proxy of the breakdown rate. The results indeed motivate to use robust loss functions for neural network training.
  arXiv  Detail & Related papers  (2022-11-18T09:57:53Z)
• Spiking neural network for nonlinear regression [68.8204255655161]
  Spiking neural networks carry the potential for a massive reduction in memory and energy consumption. They introduce temporal and neuronal sparsity, which can be exploited by next-generation neuromorphic hardware. A framework for regression using spiking neural networks is proposed.
  arXiv  Detail & Related papers  (2022-10-06T13:04:45Z)
• Dynamic Neural Diversification: Path to Computationally Sustainable Neural Networks [68.8204255655161]
  Small neural networks with a constrained number of trainable parameters, can be suitable resource-efficient candidates for many simple tasks. We explore the diversity of the neurons within the hidden layer during the learning process. We analyze how the diversity of the neurons affects predictions of the model.
  arXiv  Detail & Related papers  (2021-09-20T15:12:16Z)
• Self-adaptive deep neural network: Numerical approximation to functions and PDEs [3.6525914200522656]
  We introduce a self-adaptive algorithm for designing an optimal deep neural network for a given task. The ANE method is written as loops of the form train, estimate and enhance. We demonstrate that the ANE method can automatically design a nearly minimal NN for learning functions exhibiting sharp transitional layers.
  arXiv  Detail & Related papers  (2021-09-07T03:16:57Z)
• Measurement error models: from nonparametric methods to deep neural networks [3.1798318618973362]
  We propose an efficient neural network design for estimating measurement error models. We use a fully connected feed-forward neural network to approximate the regression function $f(x)$. We conduct an extensive numerical study to compare the neural network approach with classical nonparametric methods.
  arXiv  Detail & Related papers  (2020-07-15T06:05:37Z)
• Modeling from Features: a Mean-field Framework for Over-parameterized Deep Neural Networks [54.27962244835622]
  This paper proposes a new mean-field framework for over- parameterized deep neural networks (DNNs) In this framework, a DNN is represented by probability measures and functions over its features in the continuous limit. We illustrate the framework via the standard DNN and the Residual Network (Res-Net) architectures.
  arXiv  Detail & Related papers  (2020-07-03T01:37:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.