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train_val_split

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

train_val_split appears in the code Syntology harvested for 14 papers, as 16 distinct code bodies found in 16 places (a place is one code body under one paper). At least one of them ran in 6 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 train_val_split 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 16 distinct code bodies named train_val_split; 10 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
1ran · violated contract
0ran · our draft was wrong
2ran · fixture could not drive it
3ran
10unverified
1fingerprinted

Licence is a property of each copy, so it is counted per place: 6 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

14 papers shown of 14, 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 1 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
High-Resolution Range Profile Classifiers Require Aspect-Angle Awareness added by Syntology 2026-03 (from id) EdwynBrient/HRRPclf-req-angles/src/clf_req_asp/utils.py 01d465a2ac096f9d unverified MIT (permissive)
Looking Inward: Language Models Can Learn About Themselves by Introspection 17 Oct 2024 felixbinder/introspection_self_prediction/evals/extract_strings_from_dataframe.py 43066566684c556a ran no licence file found · pointer only
CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patching 16 Oct 2024 decisionintelligence/catch/ts_benchmark/baselines/catch/CATCH.py a59a0451698b9bad ran · fixture could not drive it no licence file found · pointer only
Improving Group Robustness on Spurious Correlation Requires Preciser Group Inference 22 Apr 2024 yujinhanml/gic/utils/load_data.py 338f487e5aaa1dbe ran · metamorphic tier: deterministic no licence file found · pointer only
Reliable Generation of Privacy-preserving Synthetic Electronic Health Record Time Series via Diffusion Models 23 Oct 2023 muhangtian/timediff/baselines/halo_train.py 19841fc582fd6a91 ran no licence file found · pointer only
Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable Data 6 Jun 2022 OxWearables/ssl-wearables/downstream_task_evaluation.py e79a7e27f77244d5 ran · fixture could not drive it fingerprinted licence not identified · pointer only
Evaluating Gradient Inversion Attacks and Defenses in Federated Learning 30 Nov 2021 Princeton-SysML/GradAttack/gradattack/datamodules.py 491cc519cd049dad unverified MIT (permissive)
Learning Temporally Causal Latent Processes from General Temporal Data 11 Oct 2021 talmo/leap/leap/training.py 30ad80c64b2a2dc6 unverified Apache-2.0 (permissive)
Probabilistic task modelling for meta-learning 9 Jun 2021 cnguyen10/probabilistic_task_modelling/utils.py fefff9ac7d01b12a unverified MIT (permissive)
Generative Latent Implicit Conditional Optimization when Learning from Small Sample 31 Mar 2020 IdanAzuri/glico-learning-small-sample/glico_model/cifar10.py a727540bc318bdd2 unverified MIT (permissive)
Generative Latent Implicit Conditional Optimization when Learning from Small Sample 31 Mar 2020 IdanAzuri/glico-learning-small-sample/glico_model/cifar100.py 3fb532813fcf4bb5 unverified MIT (permissive)
Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning 8 Aug 2019 EricArazo/PseudoLabeling/cifar10/dataset/cifar10.py 9ff47a9283dff48c unverified MIT (permissive)
MixMatch: A Holistic Approach to Semi-Supervised Learning 6 May 2019 kevinghst/mixmatch/dataset/cifar10.py 035b6b2199fe55d1 unverified MIT (permissive)
MixMatch: A Holistic Approach to Semi-Supervised Learning 6 May 2019 narendoraiswamy/MixMatch-pytorch-demo/dataset/_cifar10.py 41670d21181a8756 unverified MIT (permissive)
Fighting Fire with Fire: Using Antidote Data to Improve Polarization and Fairness of Recommender Systems 2 Dec 2018 rastegarpanah/antidote-data-framework/MF.py a37b461d017fb3c9 ran · violated contract no licence file found · pointer only
MMDenseLSTM: An efficient combination of convolutional and recurrent neural networks for audio source separation 2018-05 (from id) tsurumeso/vocal-remover/lib/dataset.py 8f3394212ec277e8 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".

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