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New research details derivative bounds for random tanh neural networks

By PulseAugur Editorial · [1 sources] ·

Researchers have established high-probability bounds for the mixed input derivatives of wide random neural networks that utilize hyperbolic tangent (tanh) activation functions. The study, which focuses on networks with Xavier initialization, demonstrates that derivative bounds can be substantially improved for sufficiently wide Gaussian networks by isolating specific terms and controlling tangent directions. This work connects derivative estimates to quasi-Monte Carlo integration, suggesting potential applications in the analysis of QMC-based training. AI

IMPACT Provides theoretical underpinnings for understanding neural network behavior and potential improvements in training methodologies.

RANK_REASON Academic paper detailing theoretical advancements in neural network derivatives. [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 →

New research details derivative bounds for random tanh neural networks

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Academic paper detailing theoretical advancements in neural network derivatives. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Josef Dick, Michael Feischl, Fabian Zehetgruber ·

    High Probability Derivative Bounds for Random tanh Neural Networks on a Hypercube

    arXiv:2608.26526v1 Announce Type: new Abstract: We establish high-probability bounds for mixed input derivatives of wide random neural networks whose activation derivatives satisfy a factorial growth bound. Our main result specializes these estimates to $\tanh$ networks with Xavi…

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