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The implicit bias of diagonal linear networks under infinitesimal initialization reveals a surprising alignment with a modified \( \mathcal{l}_1 \) norm, reshaping our understanding of their training dynamics.
Neuromorphic systems can achieve deterministic computation despite temporal stochasticity by enforcing charge conservation, enabling a direct mapping to quantized ANNs.
Adam can achieve linear convergence on highly degenerate polynomials without careful tuning, thanks to a built-in mechanism that exponentially amplifies the effective learning rate.
