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run_epoch

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

run_epoch appears in the code Syntology harvested for 18 papers, as 19 distinct code bodies found in 20 places (a place is one code body under one paper). At least one of them ran in 3 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 run_epoch 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 19 distinct code bodies named run_epoch; 17 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

1ran · honoured contract
0ran · violated contract
0ran · our draft was wrong
0ran · fixture could not drive it
1ran
17unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 9 of the 20 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

18 papers shown of 18, newest first; 20 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
MedCore: Boundary-Preserving Medical Core Pruning for MedSAM added by Syntology 2026-05 (from id) cenweizhang/MedCore/medcore_pruning/training.py 858e877d2f3108c4 unverified Apache-2.0 (permissive)
Unified Gradient-Based Machine Unlearning with Remain Geometry Enhancement 29 Sep 2024 meghdadk/SCRUB/main_merged.py 9510c6df76e617f1 unverified licence not identified · pointer only
MACE: A Machine learning Approach to Chemistry Emulation 2024-05 (from id) silkemaes/mace/src/mace/integrated.py 285f1ac98285b98b unverified GPL-3.0 (copyleft) · pointer only
MACE: A Machine learning Approach to Chemistry Emulation 2024-05 (from id) silkemaes/mace/src/mace/local.py 597c215894f54d25 unverified GPL-3.0 (copyleft) · pointer only
Reliable Generation of Privacy-preserving Synthetic Electronic Health Record Time Series via Diffusion Models 23 Oct 2023 muhangtian/timediff/baselines/crnngan_train.py f95042366cbc7997 unverified no licence file found · pointer only
LagrangeBench: A Lagrangian Fluid Mechanics Benchmarking Suite 28 Sep 2023 RobDHess/Steerable-E3-GNN/nbody/train_nbody.py f638b3f28da71d6d ran MIT (permissive)
LagrangeBench: A Lagrangian Fluid Mechanics Benchmarking Suite 28 Sep 2023 RobDHess/Steerable-E3-GNN/nbody/train_gravity.py c18c2f1f9a27b909 unverified MIT (permissive)
Semi-Supervised Deep Regression with Uncertainty Consistency and Variational Model Ensembling via Bayesian Neural Networks 15 Feb 2023 xmed-lab/UCVME/ucvme_age.py 1e361124c115325e unverified no licence file found · pointer only
Phase2vec: Dynamical systems embedding with a physics-informed convolutional network 7 Dec 2022 nitzanlab/phase2vec/phase2vec/train/_train.py 8ad9fd3e8ba91ec9 unverified no licence file found · pointer only
Decoupling Local and Global Representations of Time Series 4 Feb 2022 googleinterns/local_global_ts_representation/evaluations/daily_rain.py d279b9f95cb9561b unverified Apache-2.0 (permissive)
Model Zoo: A Growing "Brain" That Learns Continually 6 Jun 2021 grasp-lyrl/modelzoo_continual/utils/run_net.py e3878893e6d057d2 unverified MIT (permissive)
On the Computational Power of Transformers and its Implications in Sequence Modeling 16 Jun 2020 satwik77/Transformer-Computation-Analysis/Transformer/src/model.py c179d23a93633b29 unverified MIT (permissive)
Transformer Networks for Trajectory Forecasting 18 Mar 2020 FGiuliari/Trajectory-Transformer/transformer/flow.py 7ef6f0f168ab5fba unverified MIT (permissive)
Optimal Power Flow Using Graph Neural Networks 21 Oct 2019 tomyvazquez/doraa-uy/no-supervisado/IEEE/entrenamiento/src/train_eval.py e7dc5d3011a0ef63 unverified MIT (permissive)
A Self-Attentive model for Knowledge Tracing 16 Jul 2019 shalini1194/SAKT/2019-EDM/main1.py ce77a64f3aefc943 unverified no licence file found · pointer only
YellowFin and the Art of Momentum Tuning 12 Jun 2017 JianGoForIt/YellowFin/ptb/model/ptb_word_lm.py 34a1343d41b87f8f unverified Apache-2.0 (permissive)
Bayesian Recurrent Neural Networks 10 Apr 2017 mirceamironenco/BayesianRecurrentNN/bayesian_rnn.py a7084a4684a7389e unverified MIT (permissive)
Learning to learn by gradient descent by gradient descent 14 Jun 2016 deepmind/learning-to-learn/util.py 5802125d5f2e3a34 unverified Apache-2.0 (permissive)
Rethinking the Inception Architecture for Computer Vision 2 Dec 2015 mjhucla/TF-mRNN/exp/ms_coco_caption/mrnn_trainer_mscoco.py 0e80fb39f95dde2a ran · honoured contract no licence file found · pointer only
Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN) 20 Dec 2014 identical code first harvested elsewhere 0e80fb39f95dde2a ran · honoured contract licence of this copy not recorded

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