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Paper reveals finite precision limits Tanh neural network learning

By PulseAugur Editorial · [2 sources] ·

A new paper explores the inherent limitations of training $\tanh$ neural networks using finite-precision computations. The research demonstrates that under such conditions, adaptive randomized algorithms are bound by the Monte Carlo convergence rate. This limitation persists unless the computational budget scales exponentially with network size, highlighting fundamental constraints on learnability for networks with localized bump functions. AI

IMPACT Highlights theoretical constraints on training efficiency for certain neural network architectures.

RANK_REASON The cluster contains an academic paper detailing theoretical limitations of neural network training.

Read on arXiv cs.LG →

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

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Matěj Trödler ·

    Limitations of Learning Tanh Neural Networks with Finite Precision

    We investigate limitations of learning $\tanh$ neural networks from point evaluations under finite-precision computations and $L^p$ accuracy guarantees, building on Berner, Grohs, and Voigtländer (2023). Our approach is based on a novel construction of sharply localized bump func…

  2. arXiv stat.ML TIER_1 English(EN) · Philipp Grohs, Mat\v{e}j Tr\"odler ·

    Limitations of Learning Tanh Neural Networks with Finite Precision

    arXiv:2606.11104v1 Announce Type: cross Abstract: We investigate limitations of learning $\tanh$ neural networks from point evaluations under finite-precision computations and $L^p$ accuracy guarantees, building on Berner, Grohs, and Voigtl\"ander (2023). Our approach is based on…

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