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New algorithm learns ReLU combinations in polynomial time

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new algorithm capable of PAC learning positive linear combinations of ReLU activations in fully polynomial time. This algorithm requires a polynomial number of samples and runs in polynomial time relative to the dimensions and number of activations. The approach utilizes a novel boosting procedure with implicit tensor computation and Hermite tensors to amplify separations among weight vectors, enabling their recovery to constant error. A crucial component is a pruning method that maintains only the relevant vectors approximating the target accuracy. AI

IMPACT This research advances theoretical understanding and algorithmic efficiency in learning complex neural network components.

RANK_REASON The item is an academic paper detailing a new algorithm for a machine learning task. [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 algorithm learns ReLU combinations in polynomial time

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The item is an academic paper detailing a new algorithm for a machine learning task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ilias Diakonikolas, Jelena Diakonikolas, Puqian Wang, Nikos Zarifis ·

    Learning Positive Linear Combinations of ReLUs in Fully Polynomial Time

    arXiv:2610.32940v1 Announce Type: cross Abstract: We study the fundamental problem of PAC learning positive linear combinations of $k$ arbitrary ReLU activations on $\mathbb{R}^d$ with respect to the Gaussian distribution, in $L_2$ loss. We give the first fully-polynomial time al…

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