Parallel Algorithms Align with Neural Execution

被引：0
|
作者：
Engelmayer, Valerie [1 ]
Georgiev, Dobrik [2 ]
Velickovic, Petar [3 ]
机构：
[1] Univ Augsburg, Augsburg, Germany
[2] Univ Cambridge, Cambridge, England
[3] Google DeepMind, London, England
来源：
LEARNING ON GRAPHS CONFERENCE, VOL 231 | 2023年 / 231卷
关键词：
D O I：
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中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Neural algorithmic reasoners are parallel processors. Teaching them sequential algorithms contradicts this nature, rendering a significant share of their computations redundant. Parallel algorithms however may exploit their full computational power, therefore requiring fewer layers to be executed. This drastically reduces training times, as we observe when comparing parallel implementations of searching, sorting and finding strongly connected components to their sequential counterparts on the CLRS framework. Additionally, parallel versions achieve (often strongly) superior predictive performance.
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页数：13
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