Graph Structure of Neural Networks: Difference between revisions
Jump to navigation
Jump to search
No edit summary |
|||
| Line 5: | Line 5: | ||
= Introduction = | = Introduction = | ||
= | = Major Conclusions = | ||
(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. | |||
= Section 1 = | = Section 1 = | ||
Revision as of 14:35, 10 November 2020
Presented By
Xiaolan Xu, Robin Wen, Yue Weng, Beizhen Chang
Introduction
Major Conclusions
(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.