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vgg13

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

vgg13 appears in the code Syntology harvested for 10 papers, as 10 distinct code bodies found in 10 places (a place is one code body under one paper). At least one of them ran in 4 of the papers; 0 of the code bodies carry 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 vgg13 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 4 of the 10 distinct code bodies named vgg13; 6 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
0ran · our draft was wrong
0ran · fixture could not drive it
4ran
6unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 4 of the 10 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

10 papers shown of 10, newest first; 10 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; 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
4-bit Shampoo for Memory-Efficient Network Training 28 May 2024 sike-wang/low-bit-shampoo/models/vgg.py d77d79a6e5db71b8 ran no licence file found · pointer only
Learning with Logical Constraints but without Shortcut Satisfaction 1 Mar 2024 SoftWiser-group/NeSy-without-Shortcuts/models/vgg.py 3c7955d7089b595d unverified no licence file found · pointer only
NiteDR: Nighttime Image De-Raining with Cross-View Sensor Cooperative Learning for Dynamic Driving Scenes 28 Feb 2024 cidanshi/nitedr-nighttime-image-de-raining/vgg.py d567ea1f3c15e875 ran no licence file found · pointer only
LM-HT SNN: Enhancing the Performance of SNN to ANN Counterpart through Learnable Multi-hierarchical Threshold Model 1 Feb 2024 hzc1208/LMHT_SNN/ann_models/VGG.py d8bb4a9d1a90845c ran no licence file found · pointer only
Why Does Little Robustness Help? A Further Step Towards Understanding Adversarial Transferability 15 Jul 2023 cgcl-codes/transferattacksurrogates/TransferAttack/models/vgg.py 573861d5c02def4d ran MIT (permissive)
Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains 27 Jan 2022 Alibaba-AAIG/Beyond-ImageNet-Attack/imagenet/vgg.py f9a4c5c980953259 unverified MIT (permissive)
Regularizing Neural Networks via Adversarial Model Perturbation 10 Oct 2020 hiyouga/AMP-Regularizer/models/vgg.py a87c587db9bb46e7 unverified MIT (permissive)
Deep Learning under Privileged Information Using Heteroscedastic Dropout 29 May 2018 johnwlambert/dlupi-heteroscedastic-dropout/cnns/base_networks/vgg.py e4905952077a97ff unverified MIT (permissive)
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima 15 Sep 2016 keskarnitish/large-batch-training/PyTorch/vgg.py 2ad86edffbcbccba unverified MIT (permissive)
arXiv:aaai_26027 UCAS-LCH/Twin-Rep/models/vgg.py ae23e9758bbd9238 unverified MIT (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".

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections