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Minimum neuron count for ReLU networks proven NP-hard

By PulseAugur Editorial · [1 sources] ·

A new paper published on arXiv demonstrates that determining the minimum number of neurons required for a two-hidden-layer ReLU neural network to approximate a target function is an NP-hard problem. This finding holds true for various approximation constraints and even for simplified network architectures. The research suggests that heuristic methods are likely necessary for optimizing ReLU neural networks, as achieving an optimal configuration in polynomial time is computationally infeasible. AI

IMPACT Theoretical findings suggest heuristic approaches are necessary for optimizing neural network architectures, impacting future research in efficient model design.

RANK_REASON Academic paper detailing theoretical computational complexity of neural network architecture. [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 →

Minimum neuron count for ReLU networks proven NP-hard

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Academic paper detailing theoretical computational complexity of neural network architecture. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Sangrock Lee ·

    NP-Hardness of Minimizing Neurons in Two-Hidden-Layer ReLU Neural Networks

    arXiv:2610.11313v1 Announce Type: new Abstract: A fundamental question in neural network architecture optimization is whether the minimum hidden-neuron count required to approximate a target function within a prescribed tolerance can be computed efficiently. This paper resolves t…

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