AIBullishMarkTechPost ยท Mar 167/10
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Moonshot AI Releases ๐จ๐๐๐๐๐๐๐๐ ๐น๐๐๐๐ ๐๐๐๐ to Replace Fixed Residual Mixing with Depth-Wise Attention for Better Scaling in Transformers
Moonshot AI has released Attention Residuals, a new approach that replaces traditional fixed residual connections in Transformer architectures with depth-wise attention mechanisms. The innovation addresses structural problems in PreNorm architectures where all prior layer outputs are mixed equally, potentially improving model scaling capabilities.
