Methods › General › Semi-Supervised Learning Methods › SPS › Papers where code ran, page 1
Semi-Pseudo-Label
SPS
Papers archive 2025-07-28
archive papers tagged: 47 · with a code link: 11 · where Syntology ran a sample: 6 (5 with a run with no instrument failure, 1 where every run was a failure of Syntology's instrument) Syntology
Show: all tagged papersonly where code ran (6 of 47 tagged: 5 with a run with no instrument failure, 1 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 6 of the 6 tagged papers where Syntology ran at least one harvested sample (5 with a run with no instrument failure, 1 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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LLMs Can Simulate Standardized Patients via Agent Coevolution 16 Dec 2024 · 1 repository · arXiv:2412.11716Syntology official (archive's flag): 12 ran · 12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified (of 14 harvested samples) · 14 pointer-only (licence)
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Efficient Temporal Extrapolation of Multimodal Large Language Models with Temporal Grounding Bridge 25 Feb 2024 · 2 repositories · arXiv:2402.16050Syntology official (archive's flag): 7 ran · 7 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified (of 8 harvested samples) · 2 pointer-only (licence)
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Stochastic Gradient Descent with Preconditioned Polyak Step-size 3 Oct 2023 · 1 repository · arXiv:2310.02093Syntology 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, 0 violated, 13 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified (of 15 harvested samples) · 15 pointer-only (licence)
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Locally Adaptive Federated Learning 12 Jul 2023 · 1 repository · arXiv:2307.06306Syntology official (archive's flag): 14 ran · 14 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified (of 16 harvested samples) · 16 pointer-only (licence)
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A Stochastic Proximal Polyak Step Size 12 Jan 2023 · 1 repository · arXiv:2301.04935Syntology official (archive's flag): 3 ran · 3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified (of 3 harvested samples)
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Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees 21 Sep 2022 · 1 repository · arXiv:2209.10492Syntology official (archive's flag): 3 ran · 3 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified (of 4 harvested samples)