Two new arXiv papers explore the approximation power of neural networks when layer normalization techniques are applied adjacent to or in parallel with activation functions. The first paper, "Activation-Adjacent Layer Normalization," investigates how AN and PN networks with RMSNorm and Layer Normalization can approximate MLPs, detailing specific width requirements and error bounds for different activation functions. The second paper, "Parallel Layer Normalization for Universal Approximation," demonstrates that while a single LN unit is non-universal, parallel LN units (PLN-Nets) can achieve universal approximation, with the effectiveness depending on grouping structures and network types like CNNs and ViTs. AI
IMPACT These theoretical explorations into layer normalization and network approximation power could inform the design of more efficient and capable neural network architectures.
RANK_REASON Two academic papers published on arXiv discussing theoretical aspects of neural network architectures.
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