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Arch VGG [33] ResNet [34] Layers 14 16 20 32 44 56 110 1202 Params 14.71 33.63 0.27 0.46 0.66 0.85 1.72 19.33 Acc 89.2989.48 86.72 86.86 87.09 87.17 87.21 86.97 Table 4

Table 4 shows that our method maintains robust performance without significant degradation as depth increases, even with extremely deep networks, confirming the scalability

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cs.AI 1

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2026 1

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Hebbian Learning with Global Direction

cs.AI · 2026-01-29 · unverdicted · novelty 6.0

GHL combines local Oja's rule with competitive learning and a global sign signal to outperform prior Hebbian methods and narrow the performance gap with backpropagation on large-scale tasks.

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  • Hebbian Learning with Global Direction cs.AI · 2026-01-29 · unverdicted · none · ref 4

    GHL combines local Oja's rule with competitive learning and a global sign signal to outperform prior Hebbian methods and narrow the performance gap with backpropagation on large-scale tasks.