Multi-Gate Residuals
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Computer Science > Machine Learning
Title:Multi-Gate Residuals
Abstract:While Attention Residuals has shown some effectiveness in addressing the widespread issue of unbounded activation growth across deep residual layers, it inevitably incurs significant communication overhead. To circumvent this bottleneck, we propose Multi-Gate Residuals (MGR), which stabilizes activation scales without additional communication burden. It utilizes a straightforward scoring and gating mechanism to maintain multi-stream context, coupled with Attention Pooling to extract hidden states from the stream states. Empirical experiments demonstrate that MGR is practical for large-scale training and deployment, offering tangible performance improvements over existing architectures.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2605.23259 [cs.LG] |
| (or arXiv:2605.23259v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.23259
arXiv-issued DOI via DataCite (pending registration)
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