Multi-Gate Residuals
The paper titled 'Multi-Gate Residuals' introduces a new mechanism to address the issue of unbounded activation growth in deep residual layers. This approach, called Multi-Gate Residuals (MGR), aims to stabilize activation scales without incurring additional communication overhead. Empirical results indicate that MGR offers significant performance improvements for large-scale training and deployment compared to existing architectures.
- ▪Multi-Gate Residuals (MGR) is proposed to stabilize activation scales in deep learning models.
- ▪The method utilizes a scoring and gating mechanism to maintain multi-stream context.
- ▪Empirical experiments show that MGR provides tangible performance improvements over current architectures.
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Computer Science > Machine Learning arXiv:2605.23259 (cs) [Submitted on 22 May 2026] Title:Multi-Gate Residuals Authors:Zhizhan Zheng, Feiyun Zhang, Shuchun Liu, Tian Xia, Xi Liu, Dasheng Hu, Hongquan Zhou View a PDF of the paper titled Multi-Gate Residuals, by Zhizhan Zheng and 6 other authors View PDF HTML (experimental) 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.
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