[2307.04049] Parallel Algorithms Align with Neural Execution
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Computer Science > Machine Learning

arXiv:2307.04049 (cs)
[Submitted on 8 Jul 2023 (v1), last revised 3 Jan 2024 (this version, v2)]

Title:Parallel Algorithms Align with Neural Execution

Authors:Valerie Engelmayer, Dobrik Georgiev, Petar Veličković
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Abstract: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.
Comments: 13 pages, 7 figures, Proceedings of the Second Learning on Graphs Conference (LoG 2023), PMLR 231
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2307.04049 [cs.LG]
  (or arXiv:2307.04049v2 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2307.04049

Submission history

From: Valerie Engelmayer [view email]
[v1] Sat, 8 Jul 2023 21:28:20 UTC (214 KB)
[v2] Wed, 3 Jan 2024 12:34:37 UTC (340 KB)
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