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average_weights

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

average_weights appears in the code Syntology harvested for 13 papers, as 9 distinct code bodies found in 16 places (a place is one code body under one paper). At least one of them ran in 7 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 average_weights 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 2 of the 9 distinct code bodies named average_weights; 7 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
1ran · our draft was wrong
0ran · fixture could not drive it
1ran
7unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 10 of the 16 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

13 papers shown of 13, newest first; 16 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 3 papers added by Syntology. 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
Conditional GraphGANFed: Optimizing Graph-Structured Molecule Generation in Federated Generative Adversarial Networks added by Syntology 2026-08 (from id) danielmanu93/Conditional-GraphGANFed/utils.py ab2485d028020739 ran no licence file found · pointer only
Trustworthy Blockchain-based Federated Learning for Electronic Health Records: Securing Participant Identity with Decentralized Identifiers and Verifiable Credentials added by Syntology 2026-02 (from id) rodrigoronner/TBFL-EHR-Framework/TBFL-EHR-Framework/src/main_tbfl_simulation.py d228391d37d7459a unverified no licence file found · pointer only
Cooperative Pseudo Labeling for Unsupervised Federated Classification added by Syntology 2025-10 (from id) krumpguo/FedCoPL/methods/main_UL.py 0a3bd5bcb922a499 unverified no licence file found · pointer only
pFedMMA: Personalized Federated Fine-Tuning with Multi-Modal Adapter for Vision-Language Models 7 Jul 2025 sajjad-ucsb/pfedmma/utils.py ab2485d028020739 ran no licence file found · pointer only
pFedMMA: Personalized Federated Fine-Tuning with Multi-Modal Adapter for Vision-Language Models 7 Jul 2025 sajjad-ucsb/pfedmma/fed_utils.py 697e29e1bbc08d3d unverified no licence file found · pointer only
Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method 29 Sep 2024 PanBikang/PromptFolio/utils.py ab2485d028020739 ran no licence file found · pointer only
Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method 29 Sep 2024 PanBikang/PromptFolio/fed_utils.py 697e29e1bbc08d3d unverified no licence file found · pointer only
Harmonizing Generalization and Personalization in Federated Prompt Learning 16 May 2024 tianyucuiovo/fedpgp/utils.py ab2485d028020739 ran no licence file found · pointer only
Harmonizing Generalization and Personalization in Federated Prompt Learning 16 May 2024 tianyucuiovo/fedpgp/fed_utils.py 697e29e1bbc08d3d unverified no licence file found · pointer only
Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off 10 Feb 2024 6lyc/fedceo_collaborate-with-each-other/utils.py ab2485d028020739 ran no licence file found · pointer only
LR-XFL: Logical Reasoning-based Explainable Federated Learning 24 Aug 2023 yanci87/lr-xfl/experiments/utils.py f78af8bb07ffd223 unverified MIT (permissive)
SUPERB: Speech processing Universal PERformance Benchmark 3 May 2021 usc-sail/fed-ser-leakage/mitigation/update.py 7dd1e77967fbe002 unverified MIT (permissive)
Federated Learning: Challenges, Methods, and Future Directions 21 Aug 2019 AshwinRJ/Federated-Learning-PyTorch/src/utils.py ab2485d028020739 ran MIT (permissive)
Learning Private Neural Language Modeling with Attentive Aggregation 17 Dec 2018 shaoxiongji/fed-att/src/agg/avg.py 7a711a11ae243b32 unverified MIT (permissive)
Hierarchical Graph Representation Learning with Differentiable Pooling 22 Jun 2018 basiralab/reproduciblefedgnn/federated_reproducibility/fed_localmodel.py 63d220d8b4f8afd5 unverified MIT (permissive)
Communication-Efficient Learning of Deep Networks from Decentralized Data 17 Feb 2016 futabato/FutabatedLearning/src/federatedlearning/server/aggregations/aggregators.py 0964f793bbd43856 ran · our draft was wrong 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