Local Credit Assignment Could Enhance Network Expressivity
yacineMTB · x · 2026-07-15
The author endorses a research hypothesis suggesting that learning algorithms like local credit assignment could empower neural networks with stronger non-linear weight expressivity, leading to improved noise robustness, expressivity, and neuron-level interpretability.
The cited research further notes that both the KAN and LNN research lines have observed that introducing learnable non-linearities on edges often results in better robustness, interpretability, and expressivity. However, the critical bottleneck remains that excessive non-linearity makes backprop training significantly more difficult.
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