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Weight perturbation approaches backpropagation accuracy on MNIST

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the effectiveness of weight perturbation (WP) as an alternative to backpropagation (BP) for training neural networks. The study found that WP can achieve accuracy levels close to BP on the MNIST dataset, particularly with large batch sizes and sufficient training duration. However, WP demonstrated a significant efficiency gap compared to BP, requiring more training epochs to reach comparable performance, especially in deeper network configurations. The research suggests that while WP can approximate BP's accuracy under specific conditions, it does not match BP's efficiency. AI

IMPACT Explores alternative training methods that could offer efficiency gains or new capabilities in neural network development.

RANK_REASON The cluster contains a research paper detailing a new method for training neural networks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.NE (Neural & Evolutionary) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Weight perturbation approaches backpropagation accuracy on MNIST

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The cluster contains a research paper detailing a new method for training neural networks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Patrick Inoue ·

    Approaching Backpropagation Accuracy with Weight Perturbation on MNIST

    Weight perturbation (WP) estimates learning directions from random parameter perturbations and a scalar change in loss, without backpropagating derivatives. We examine how batch size, training duration, and estimator stabilization affect its attainable accuracy in fully connected…

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