Methods › General › Dimensionality Reduction › LDA › Papers where code ran, page 1
Linear Discriminant Analysis
LDA
Papers archive 2025-07-28
archive papers tagged: 459 · with a code link: 113 · 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 459 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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Topic Modeling with Fine-tuning LLMs and Bag of Sentences 6 Aug 2024 · 1 repository · arXiv:2408.03099Syntology official (archive's flag): 1 ran · 1 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; 1 where Syntology's instrument failed) · 0 unverified (of 1 harvested sample)
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Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrieval 13 Aug 2023 · 1 repository · arXiv:2308.06748Syntology official (archive's flag): 9 ran · 9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified (of 11 harvested samples) · 3 pointer-only (licence)
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Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence 8 Apr 2020 · 3 repositories · arXiv:2004.03974Syntology community repositories only · 1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified (of 1 harvested sample)
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Autoencoding Variational Inference For Topic Models 4 Mar 2017 · 6 repositories · arXiv:1703.01488Syntology 5 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; 1 where Syntology's instrument failed) · 3 unverified (of 8 harvested samples) · 1 pointer-only (licence)
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Contrastive Pessimistic Likelihood Estimation for Semi-Supervised Classification 1 Mar 2015 · 1 repository · arXiv:1503.00269Syntology 3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified (of 3 harvested samples) · 3 pointer-only (licence)
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PCANet: A Simple Deep Learning Baseline for Image Classification? 14 Apr 2014 · 2 repositories · arXiv:1404.3606Syntology 2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified (of 3 harvested samples)