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Attention head fusion in neural networks may require exponential atoms

By PulseAugur Editorial · [1 sources] ·

A new paper published on arXiv explores the computational complexity of fusing attention heads in neural networks. The research demonstrates that combining independently normalized attention heads can significantly increase the number of explicit key-value entries, or atoms, required to maintain their output. Specifically, the paper proves a sharp separation for a function W_d(q), showing that its d two-atom heads necessitate 2d head-atom terms, while a single-head representation requires 2^d atoms. AI

IMPACT This research highlights a potential computational bottleneck in attention mechanisms, suggesting that certain fusion strategies could lead to exponentially increased resource requirements.

RANK_REASON The cluster contains a research paper published on arXiv detailing theoretical findings about neural network architecture. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.NE (Neural & Evolutionary) →

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

Attention head fusion in neural networks may require exponential atoms

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The cluster contains a research paper published on arXiv detailing theoretical findings about neural network architecture. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Naohiko Matsuda ·

    Exact Fusion of Softmax Attention Heads Can Require Exponentially Many Atoms

    Fusing independently normalized attention heads can increase the number of explicit key-value entries, or atoms, needed to preserve their output. We prove a sharp separation for $W_d(q)=\sum_{j=1}^dσ(q_j)$: its $d$ two-atom heads use $2d$ head-atom terms, whereas its minimum sing…

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