Methods › Graphs › Graph Models › GNS › Papers where code ran, page 1
Graph Network-based Simulators
GNS
Papers archive 2025-07-28
archive papers tagged: 21 · with a code link: 10 · where Syntology ran a sample: 3 (3 with a run with no instrument failure, 0 where every run was a failure of Syntology's instrument) Syntology
Show: all tagged papersonly where code ran (3 of 21 tagged: 3 with a run with no instrument failure, 0 where every run was a failure of Syntology's instrument)
Syntology We ran code from the paper's repository; we did not isolate this method inside it.
Page 1 of 1: papers 1 to 3 of the 3 tagged papers where Syntology ran at least one harvested sample (3 with a run with no instrument failure, 0 where every run was a failure of Syntology's instrument), newest first by the archive's date (ties by arXiv id). This is a filter on Syntology's record ordered by date only, not a ranking; a run is not a correctness claim. A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.
Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code, as “N ran (of which C constructed an object rather than computing a result; K with no instrument failure: H honoured, V violated, P with no contract checked; I where Syntology's instrument failed) · U unverified”; the instrument figure counts failures of Syntology's instrument, not of the code. It is per sample and not a correctness claim. When the archive marks a repository official for the paper, the line starts with that repository's state (the archive's flag, not a verdict on who wrote the code; “community repositories only” when every sample that ran came from a community repository, “official: no sample here; runs from other or unrecorded repositories” when some came from a repository the paper names or has in its text, or from none recorded); hover it for the repositories the samples that ran came from.
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Normalization Layer Per-Example Gradients are Sufficient to Predict Gradient Noise Scale in Transformers 1 Nov 2024 · 1 repository · arXiv:2411.00999Syntology official (archive's flag): 13 ran · 13 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 2 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified (of 13 harvested samples) · 13 pointer-only (licence)
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LagrangeBench: A Lagrangian Fluid Mechanics Benchmarking Suite 28 Sep 2023 · 2 repositories · arXiv:2309.16342Syntology official: no sample here; runs from other or unrecorded repositories · 23 ran (of which 0 constructed an object rather than computing a result; 17 with no instrument failure: 0 honoured, 0 violated, 17 with no contract checked; 6 where Syntology's instrument failed) · 4 unverified (of 27 harvested samples) · 4 pointer-only (licence)
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Learning to Simulate Complex Physics with Graph Networks 21 Feb 2020 · 13 repositories · arXiv:2002.09405Syntology community repositories only · 17 ran (of which 5 constructed an object rather than computing a result; 16 with no instrument failure: 0 honoured, 1 violated, 15 with no contract checked; 1 where Syntology's instrument failed) · 7 unverified (of 24 harvested samples) · 13 pointer-only (licence)