Multi-Gate Residuals
For deep learning practitioners, MGR offers a communication-efficient solution to activation growth in residual networks, though improvements are incremental.
Multi-Gate Residuals (MGR) stabilizes activation scales in deep residual networks without communication overhead, achieving practical performance improvements over existing architectures in large-scale training.
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.