QIDINNs define parameter updates as kernel-weighted integrals of past gradients, essentially continuous-time momentum, and claim superior streaming learning without providing the backpropagation cost they claim to avoid.
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Quantum-Inspired Differentiable Integral Neural Networks (QIDINNs): A Feynman-Based Architecture for Continuous Learning Over Streaming Data
QIDINNs define parameter updates as kernel-weighted integrals of past gradients, essentially continuous-time momentum, and claim superior streaming learning without providing the backpropagation cost they claim to avoid.