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Research paper details finite precision limits for tanh neural networks

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the limitations of training tanh neural networks using finite precision and L^p accuracy guarantees. The study demonstrates that no adaptive randomized algorithm with a finite number of samples can achieve a convergence rate better than the Monte Carlo rate in the L^p norm. These findings highlight fundamental constraints imposed by finite precision on the learnability of networks containing localized bump functions. AI

IMPACT Highlights theoretical constraints on neural network training, potentially influencing future algorithm design and hardware precision requirements.

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

COVERAGE [1]

  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…

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