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distributed_sinkhorn

Syntologyentry name in harvested coderead from the graph 2026-09-24

distributed_sinkhorn appears in the code Syntology harvested for 17 papers, as 14 distinct code bodies found in 18 places (a place is one code body under one paper). At least one of them ran in 9 of the papers; 1 of the code bodies carries a behaviour fingerprint.

What this page is not. Routines are grouped here by the exact string of their function or class name. Nothing asserts that two samples named distributed_sinkhorn do the same thing, share code, or are comparable; the name is a string, not an identity. Behaviour outputs (what a fingerprinted sample returned on the shared battery) are not in this export and are not shown here; the graph at syntology.ai holds them. "Ran" means executed on a synthesized fixture, not that the code is correct or reproduces a paper.

Samples Syntology

Syntology ran 6 of the 14 distinct code bodies named distributed_sinkhorn; 8 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
5ran · our draft was wrong
1ran · fixture could not drive it
0ran
8unverified
1fingerprinted

Licence is a property of each copy, so it is counted per place: 8 of the 18 places are pointer only (Syntology does not serve that copy's text). This site shows no code text for any sample; every row below links to the file in its repository where the record names one.

“Ran” means the sample executed on a synthesized input; it does not mean the output is correct. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code, and those samples did run. The ran count above is every status except unverified, the same rule as each paper page.

Papers

17 papers shown of 17, newest first; 18 places in the table. A paper with no recorded date is placed by the month its arXiv id encodes, shown in the Date column as YYYY-MM (from id). One row per place: a paper whose repository defines the name more than once appears more than once, and the same code body held for several papers appears once under each, with the same status. Titles and dates are the archive's archive 2025-07-28 for papers in the archive, and the graph's for 2 papers added by Syntology; 1 papers have no page here and are shown by arXiv id only. Status and fingerprint are Syntology's record of each code body; licence is recorded for each place. The File cell ends with the code body's code_sha256, Syntology's identity for that exact code: an agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

PaperDateFileStatus SyntologyLicence
What Makes Synthetic Data Effective in Image Segmentation added by Syntology 2026-05 (from id) zhang0jhon/SENSE/sense_dpt.py c7953025c1e3b82c ran · our draft was wrong no licence file found · pointer only
Domain-aware Category-level Geometry Learning Segmentation for 3D Point Clouds added by Syntology 2025-08 (from id) identical code first harvested elsewhere be669c7787c0a93c ran · our draft was wrong licence of this copy not recorded
Conformal Prediction for Zero-Shot Models 30 May 2025 jusiro/CLIP-Conformal/solvers/transductive/confot.py 774cb5ff0c9b4941 ran · our draft was wrong no licence file found · pointer only
Learning Clustering-based Prototypes for Compositional Zero-shot Learning 10 Feb 2025 quhongyu/cluspro/cluspro.py be669c7787c0a93c ran · our draft was wrong no licence file found · pointer only
Enhancing Unsupervised Graph Few-shot Learning via Set Functions and Optimal Transport 10 Jan 2025 keaml-jlu/star/data_split.py 9ad49972980faced unverified MIT (permissive)
Enhancing Unsupervised Graph Few-shot Learning via Set Functions and Optimal Transport 10 Jan 2025 keaml-jlu/star/validation_concatenation.py 7a2650bf070523dd unverified MIT (permissive)
Bringing Masked Autoencoders Explicit Contrastive Properties for Point Cloud Self-Supervised Learning 8 Jul 2024 amazingren/point-cmae/utils/loss_msn.py 7478f8e64cee8964 ran · fixture could not drive it fingerprinted MIT (permissive)
Semi-supervised learning made simple with self-supervised clustering 13 Jun 2023 pietroastolfi/suave-daino/suave/main_suave.py e886904d62004eb3 unverified licence not identified · pointer only
Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels 21 Feb 2023 ML-GSAI/DPT/src/losses.py 7478f8e64cee8964 ran · fixture could not drive it fingerprinted MIT (permissive)
COARSE3D: Class-Prototypes for Contrastive Learning in Weakly-Supervised 3D Point Cloud Segmentation 4 Oct 2022 cv-rits/coarse3d/pc_processor/models/sinkhorn.py c64374ec52225f59 unverified Apache-2.0 (permissive)
Visual Recognition with Deep Nearest Centroids 15 Sep 2022 chenghan111/dnc/DNC_classification/mmcls/models/heads/SubCentroids_head_Formal.py a9f32f28a6eb26c1 ran · our draft was wrong MIT (permissive)
Improving Self-supervised Learning with Automated Unsupervised Outlier Arbitration 15 Dec 2021 ssl-codelab/uota/main_uota.py 5f8b63dcbe021398 unverified no licence file found · pointer only
Prototypical Graph Contrastive Learning 17 Jun 2021 ha-lins/pgcl/unsupervised_TU/pgcl_main.py a5ce8d6b2c6af43f unverified MIT (permissive)
Unsupervised Action Segmentation by Joint Representation Learning and Online Clustering 27 May 2021 trquhuytin/TOT-CVPR22/ute/models/training_embed.py 2afdf62a2f77e421 unverified MIT (permissive)
Representation Learning for Clustering via Building Consensus 4 May 2021 JayanthRR/ConCURL_NCE/losses.py 2afdf62a2f77e421 unverified MIT (permissive)
Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples 28 Apr 2021 facebookresearch/msn/src/losses.py 7478f8e64cee8964 ran · fixture could not drive it fingerprinted licence not identified · pointer only
Self-labelling via simultaneous clustering and representation learning 13 Nov 2019 hsfzxjy/swavx/main_swav.py 103cb98c938400f1 ran · our draft was wrong licence not identified · pointer only
arXiv:2024.acl-long.502 GJZhang2866/HMPEAE/sinkhorn.py f4e1ca92be031891 unverified Apache-2.0 (permissive)

This site shows no code text; each File cell links to the file on GitHub at the repository's current default branch, which may have changed since the harvest. "Pointer only" means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence cell for the reason. Per-sample records for a paper are on its paper page under "Code Syntology ran".

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