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Two ReLU Layers Sufficient for Any Neural Network, arXiv Paper Proves

By PulseAugur Editorial · [1 sources] ·

A new paper published on arXiv presents a proof that any Rectified Linear Unit (ReLU) neural network can be precisely simulated by a ReLU network with only two hidden layers. The paper offers two distinct proofs for this assertion: one rooted in homological algebra and another that is a direct combinatorial proof. The research also highlights various tools and platforms associated with academic papers, such as Connected Papers, Litmaps, and Hugging Face, which aid in exploring and utilizing research. AI

IMPACT This theoretical finding could simplify neural network architectures, potentially leading to more efficient model designs and training processes.

RANK_REASON The item is a research paper published on arXiv detailing a theoretical proof about 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 →

Two ReLU Layers Sufficient for Any Neural Network, arXiv Paper Proves

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The item is a research paper published on arXiv detailing a theoretical proof about 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) · Florestan Brunck, Christoph Hertrich, Kilian Ruess, Jack Stade, Amir Yehudayoff ·

    Two $\operatorname{ReLU}$ Layers is All You Need

    arXiv:2610.28587v1 Announce Type: cross Abstract: We prove that every $\operatorname{ReLU}$ network can be exactly simulated by a $\operatorname{ReLU}$ network with two hidden layers. We present two proofs: one that is based on fillings in homological algebra and a direct combina…

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