Random Graph-Based Neuromorphic Learning with a Layer-Weaken Structure
- URL: http://arxiv.org/abs/2111.08888v1
- Date: Wed, 17 Nov 2021 03:37:06 GMT
- Title: Random Graph-Based Neuromorphic Learning with a Layer-Weaken Structure
- Authors: Ruiqi Mao and Rongxin Cui
- Abstract summary: We transform the random graph theory into an NN model with practical meaning and based on clarifying the input-output relationship of each neuron.
Under the usage of this low-operation cost approach, neurons are assigned to several groups of which connection relationships can be regarded as uniform representations of random graphs they belong to.
We develop a joint classification mechanism involving information interaction between multiple RGNNs and realize significant performance improvements in supervised learning for three benchmark tasks.
- Score: 4.477401614534202
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 relationship of each neuron,
we complete data feature mapping by calculating Fourier Random Features (FRFs).
Under the usage of this low-operation cost approach, neurons are assigned to
several groups of which connection relationships can be regarded as uniform
representations of random graphs they belong to, and random arrangement fuses
those neurons to establish the pattern matrix, markedly reducing manual
participation and computational cost without the fixed and deep architecture.
Leveraging this single neuromorphic learning model termed random graph-based
neuro network (RGNN) we develop a joint classification mechanism involving
information interaction between multiple RGNNs and realize significant
performance improvements in supervised learning for three benchmark tasks,
whereby they effectively avoid the adverse impact of the interpretability of
NNs on the structure design and engineering practice.
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