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New algorithms for learning positive ReLU networks under Gaussian inputs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed polynomial-time algorithms for learning positive ReLU networks, specifically addressing scenarios with standard Gaussian inputs. The algorithms achieve polynomial sample and real-arithmetic complexity without requiring separation or conditioning assumptions on hidden directions. The work combines implicit Hermite moments, orthogonal tensor-train compression, and a direction decoder to bound the probability of returning a direction far from any atom. This research also establishes projective direction recovery and confidence amplification with sample complexity proportional to the logarithm of the reciprocal failure probability. AI

IMPACT Advances theoretical understanding of neural network learning capabilities.

RANK_REASON Academic paper on a theoretical machine learning topic. [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 algorithms for learning positive ReLU networks under Gaussian inputs

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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Zhijie Zheng ·

    Polynomial Learning of Positive ReLU Networks under Gaussian Inputs

    arXiv:2610.25116v2 Announce Type: replace Abstract: We study positive, bias-free, one-hidden-layer ReLU networks under standard Gaussian inputs. We obtain algorithms with polynomial sample and real-arithmetic complexity, without separation or conditioning assumptions on the hidde…

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