Spooky Action at a Distance: Normalization Layers Enable Side-Channel Spatial Communication
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909677314375680 |
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| author | Pfrommer, Samuel Ma, George Huang, Yixiao Sojoudi, Somayeh |
| author_facet | Pfrommer, Samuel Ma, George Huang, Yixiao Sojoudi, Somayeh |
| contents | This work shows that normalization layers can facilitate a surprising degree of communication across the spatial dimensions of an input tensor. We study a toy localization task with a convolutional architecture and show that normalization layers enable an iterative message passing procedure, allowing information aggregation from well outside the local receptive field. Our results suggest that normalization layers should be employed with caution in applications such as diffusion-based trajectory generation, where maintaining a spatially limited receptive field is crucial. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_04709 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Spooky Action at a Distance: Normalization Layers Enable Side-Channel Spatial Communication Pfrommer, Samuel Ma, George Huang, Yixiao Sojoudi, Somayeh Machine Learning This work shows that normalization layers can facilitate a surprising degree of communication across the spatial dimensions of an input tensor. We study a toy localization task with a convolutional architecture and show that normalization layers enable an iterative message passing procedure, allowing information aggregation from well outside the local receptive field. Our results suggest that normalization layers should be employed with caution in applications such as diffusion-based trajectory generation, where maintaining a spatially limited receptive field is crucial. |
| title | Spooky Action at a Distance: Normalization Layers Enable Side-Channel Spatial Communication |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.04709 |