Random Graph-Based Neuromorphic Learning with a Layer-Weaken Structure. (arXiv:2111.08888v2 [cs.LG] UPDATED)

Unified understanding of neuro networks (NNs) gets the users into great
trouble because they have been puzzled by what kind of rules should be obeyed
to optimize the internal structure of NNs. Considering the potential capability
of random graphs to alter how computation is performed, we demonstrate that
they can serve as architecture generators to optimize the internal structure of
NNs. To transform the random graph theory into an NN model with practical
meaning and based on clarifying the input-output …

arxiv graph graph-based learning random