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Random noise doesn't simplify exact ReLU network verification

By PulseAugur Editorial · [2 sources] ·

A new research paper explores the complexity of verifying Rectified Linear Unit (ReLU) neural networks under adversarial smoothing. The study demonstrates that adding random parameter noise, clipping, and rounding network weights does not simplify the exact verification process. The findings suggest that, assuming NP is not a subset of BPP, no efficient verifier can exist for all base instances, even with a fixed noise level for certain network configurations. AI

IMPACT This research indicates that adversarial smoothing techniques may not offer a straightforward path to efficient verification of neural networks.

RANK_REASON The cluster contains an academic paper discussing theoretical computer science and neural network verification.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Random noise doesn't simplify exact ReLU network verification

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The cluster contains an academic paper discussing theoretical computer science and neural network verification.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Mojtaba Soltanalian ·

    Random Parameter Noise Does Not Make Exact ReLU Verification Easy

    arXiv:2607.14375v1 Announce Type: cross Abstract: We study exact verification of ReLU networks in an adversarial smoothed model. Every network weight and bias is independently perturbed by Gaussian noise, clipped to $[-2,2]$, and rounded to the exact dyadic grid determined by the…

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

    Random Parameter Noise Does Not Make Exact ReLU Verification Easy

    We study exact verification of ReLU networks in an adversarial smoothed model. Every network weight and bias is independently perturbed by Gaussian noise, clipped to $[-2,2]$, and rounded to the exact dyadic grid determined by the input bit complexity. We show that, under the sta…

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