Graph Structure of Neural Networks: Difference between revisions
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= Introduction = | = Introduction = | ||
= Major Conclusions = | = Relational Graph = | ||
= Parameter Definition = | |||
(1) Clustering Coefficient | |||
(2) Average Path Length | |||
= Experimental Setup (Section 4 in the paper) = | |||
= Major Conclusions (Section 5 in the paper) = | |||
(1) graph structure of neural networks matters; | (1) graph structure of neural networks matters; | ||
| Line 18: | Line 28: | ||
(6) well-performing neural networks have graph structure surprisingly similar to those of real biological neural networks. | (6) well-performing neural networks have graph structure surprisingly similar to those of real biological neural networks. | ||
= Critique = | = Critique = | ||
Revision as of 15:55, 10 November 2020
Presented By
Xiaolan Xu, Robin Wen, Yue Weng, Beizhen Chang
Introduction
Relational Graph
Parameter Definition
(1) Clustering Coefficient
(2) Average Path Length
Experimental Setup (Section 4 in the paper)
Major Conclusions (Section 5 in the paper)
(1) graph structure of neural networks matters;
(2) a “sweet spot” of relational graphs lead to neural networks with significantly improved predictive performance;
(3) neural network’s performance is approximately a smooth function of the clustering coefficient and average path length of its relational graph;
(4) our findings are consistent across many different tasks and datasets;
(5) top architectures can be identified efficiently;
(6) well-performing neural networks have graph structure surprisingly similar to those of real biological neural networks.