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Subspace Levenberg-Marquardt Algorithms Evaluated for Neural Network Training

By PulseAugur Editorial · [1 sources] ·

Researchers have evaluated subspace Levenberg-Marquardt algorithms for training neural networks on regression and classification tasks. These subspace methods, including Krylov subspace LM and hybrid subspace LM, aim to improve the efficiency of second-order algorithms for larger neural networks. The study compares the performance of these subspace LM variants against the traditional LM algorithm and popular first-order methods like SGD and Adam. AI

IMPACT This research could lead to more efficient training methods for large neural networks, potentially accelerating development and deployment.

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

Read on arXiv cs.LG →

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

Subspace Levenberg-Marquardt Algorithms Evaluated for Neural Network Training

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30 / 100
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The cluster contains a research paper detailing new algorithms for training neural networks. [lever_c_demoted from research: ic=1 ai=1.0]
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paper, infra
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High
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Breaking (< 6h)
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · M. Duc Hoang ·

    Subspace Levenberg Marquardt Algorithms in Training Neural Networks

    arXiv:2609.00789v1 Announce Type: new Abstract: The Levenberg-Marquardt (LM) algorithm is a well-known second-order method for rapid convergence and strong robustness when training small- to medium-sized neural networks (NNs). However, its computational and memory costs increase …

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