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Weight perturbation approaches backpropagation accuracy on MNIST

By PulseAugur Editorial · [2 sources] ·

A new research paper explores the potential of weight perturbation (WP) as an alternative to backpropagation (BP) for training neural networks. The study demonstrates that WP can achieve accuracy levels close to BP on the MNIST dataset, particularly in shallow network configurations with large batch sizes. However, WP shows a significant efficiency gap compared to BP, requiring more training epochs to reach comparable performance. While WP shows promise for stabilizing deeper networks, it has not yet matched BP-level accuracy in those configurations. AI

IMPACT This research explores alternative training methods that could potentially reduce computational costs or improve model training efficiency.

RANK_REASON Research paper detailing a novel training technique for neural networks.

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

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Weight perturbation approaches backpropagation accuracy on MNIST

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Patrick Inoue ·

    Approaching Backpropagation Accuracy with Weight Perturbation on MNIST

    arXiv:2610.21665v1 Announce Type: new Abstract: 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 a…

  2. 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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