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Figure 2: (a): Comparison of locally accumulated focus scores of different transformer variants. The x-axis denotes ratio of neighbourhood dimensions relative towards the enter size; the y-axis denotes accumulated consideration scores inside of this neighbourhood for the centering token.
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%X Linear transformers goal to decrease the quadratic Place-time complexity of vanilla transformers. However, they usually experience degraded performances on many tasks and corpus. On this paper, we take a look at present kernel-centered linear transformers and determine two crucial troubles that bring on such overall performance gaps: 1) unbounded gradients in the attention computation adversely influence the convergence of linear transformer versions; 2) focus dilution which trivially distributes focus scores above prolonged sequences even though neglecting neighbouring constructions. To handle these troubles, we 1st discover that the scaling of interest matrices will be the devil in unbounded gradients, which seems pointless in linear attention as we clearly show theoretically and empirically.
which trivially distributes consideration scores about extensive sequences while neglecting neighbouring structures.
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