MCSD: Achieving more efficient feature extraction
Multi-Channel Slope adheres to the linguistic principle of "the closer, the more important", while simultaneously applying distinct slopes across multiple feature channels to achieve varying degrees of fusion of preceding features. It further assesses the importance of current position features by leveraging the fused preceding features, thereby generating a feature importance matrix for the current position.
After performing an information compression mapping on the feature importance matrix, the importance scores for each position can be obtained. Subsequently, the Multi-Channel Decay simultaneously applies causal masking and positional decay, thereby enabling a more effective fusion representation of global information.
Brain-like activation mechanism: Significantly reducing computational redundancy
Simulating neuronal activation patterns in the brain to process complex data and tasks more effectively, significantly improving computational efficiency and accuracy, and providing new tools for solving real-world complex problems.
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