AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work replaces attention with a stack of autoencoder-based mixing modules, one operating over local neighborhoods, one over the full sequence, and one across attention heads, each compressing and reconstructing its input through a bottleneck, and its width is a hyperparameter rather than a training effect.
It is shown that diffusion models also inherently use an attention mechanism very similar to that of transformers, and similarities in basic functional principle of auto-encoders and attention-based models allows for interchange of designs based on practical requirements.
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