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 explicitly at parameter counts $P\sim 10^{6}-10^{8}$. We present a stochastic estimator of the trace of the diagonal blocks of the Hessian matrix of the empirical risk of a neural network. The procedure combines the Hutchinson stochastic trace estimator with a single Hessian-vector product over the whole parameter vector and
The continuous drive to improve neural network training efficiency and understanding, especially with increasingly complex models, necessitates better monitoring tools.
This development offers a potential pathway to more stable and efficient AI model development, which is crucial for advancing AI capabilities and reducing computational waste.
The ability to better monitor neural network training through stochastic Hessian trace estimation could lead to more predictable and robust AI model performance.
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Improved monitoring leads to more efficient and stable neural network training processes.
Faster development and deployment of advanced AI models across various applications become possible.
Reduced compute requirements for training due to increased efficiency could temper the energy demands of large AI models, indirectly impacting the 'energy-bottleneck' narrative.
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Read at arXiv cs.LG