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Complexity of ReLU Network Synthesis Explored in New Research

By PulseAugur Editorial · [1 sources] ·

Researchers have investigated the computational complexity of synthesizing ReLU networks when the parameter values are provided in advance. The study explores how difficult it is to construct a ReLU network with a given set of parameters and a bound on the number of neurons. The findings indicate that problems such as matching two labeled examples exactly are NP-complete, and agreeing with a Boolean circuit on every input is Σ₂ᴾ-complete even for a small number of neurons. Approximating a ReLU network and determining expected squared loss also present significant computational challenges, with complexities reaching DP-complete and NPᴾᴾ-complete respectively. AI

IMPACT This research delves into the theoretical underpinnings of neural network construction, potentially informing future algorithm design and optimization strategies.

RANK_REASON The cluster contains a research paper detailing theoretical computational complexity findings related to neural network synthesis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Complexity of ReLU Network Synthesis Explored in New Research

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The cluster contains a research paper detailing theoretical computational complexity findings related to neural network synthesis. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    The Complexity of ReLU Network Synthesis from a Parameter Multiset

    We ask how hard it is to build a ReLU network when its parameter values are given in advance. An instance supplies an appropriate encoding of a target behavior, a multiset of rational numbers, and a bound on the number of neurons (output included). The question is whether one can…

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