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New method estimates neural network training curvature

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel stochastic estimator to calculate the trace of diagonal blocks of the Hessian matrix for neural networks. This method, which combines Hutchinson's estimator with a single Hessian-vector product, allows for unbiased per-layer trace estimation in a single backward pass. The technique is particularly useful for monitoring neural network training, as it can distinguish between healthy and pathological training regimes by analyzing the curvature of the empirical risk, which is otherwise inaccessible for large networks. The estimator has demonstrated effectiveness in detecting label memorization in models like ResNet and VGG when trained on CIFAR datasets. AI

IMPACT Provides a more accessible method for understanding and monitoring the internal dynamics of neural network training, potentially aiding in debugging and improving model performance.

RANK_REASON The cluster contains a research paper detailing a new computational method for analyzing neural network training dynamics.

Read on arXiv cs.LG →

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

New method estimates neural network training curvature

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Maxim Bolshim (ITMO University, Saint Petersburg, Russia), Alexander Kugaevskikh (ITMO University, Saint Petersburg, Russia) ·

    Stochastic Estimation of the Layer-wise Hessian Trace for Monitoring Neural-network Training

    arXiv:2605.25674v1 Announce Type: new Abstract: The loss and the norm of its gradient separate the healthy and the pathological regimes of neural-network training only weakly, whilst the curvature of the empirical risk differs qualitatively between them but is inaccessible explic…

  2. arXiv cs.LG TIER_1 English(EN) · Alexander Kugaevskikh ·

    Stochastic Estimation of the Layer-wise Hessian Trace for Monitoring Neural-network Training

    The loss and the norm of its gradient separate the healthy and the pathological regimes of neural-network training only weakly, whilst the curvature of the empirical risk differs qualitatively between them but is inaccessible explicitly at parameter counts $P\sim 10^{6}-10^{8}$. …

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