Home › Code › training_step

training_step

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

training_step 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 2 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 training_step 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 10 distinct code bodies named training_step; 8 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

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

Licence is a property of each copy, so it is counted per place: 3 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, 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
Outcome-Aware Spectral Feature Learning for Instrumental Variable Regression added by Syntology 2025-12 (from id) Wornbard/AugSpecIV/perturbed_operator_fitting.py a61b14c297d24b1b unverified no licence file found · pointer only
Redefining Machine Unlearning: A Conformal Prediction-Motivated Approach 31 Jan 2025 TIML-Group/Conformal-Prediction-Unlearning/unlearn.py f27cb338aad835ff unverified MIT (permissive)
Scalable Event-by-event Processing of Neuromorphic Sensory Signals With Deep State-Space Models 29 Apr 2024 Efficient-Scalable-Machine-Learning/event-ssm/event_ssm/train_utils.py 5f8147f4c2b3b6c4 unverified MIT (permissive)
Generative Diffusion-based Downscaling for Climate 27 Apr 2024 robbiewatt1/climatediffuse/src/TrainDiffusion.py 721951068b07a5d4 unverified no licence file found · pointer only
A Fractional Graph Laplacian Approach to Oversmoothing 22 May 2023 rpaolino/flode/node_classification.py 75f8310fd542fd39 ran · honoured contract no licence file found · pointer only
Can Bad Teaching Induce Forgetting? Unlearning in Deep Networks using an Incompetent Teacher 17 May 2022 vikram2000b/bad-teaching-unlearning/utils.py cc6e4e27f3ee438c unverified MIT (permissive)
Zero-Shot Machine Unlearning 14 Jan 2022 ayu987/zero-shot-unlearning/utils.py 1616afe621b4a8da unverified MIT (permissive)
Uncertainty in Neural Networks: Approximately Bayesian Ensembling 12 Oct 2018 giarcieri/assessing-the-influence-of-models-on-the-performance-of-reinforcement-learning-algorithms/training_step.py e55b7a835164bf52 unverified MIT (permissive)
Bayesian Semi-supervised Learning with Graph Gaussian Processes 12 Sep 2018 felixopolka/ggp-tf2/ggp.py cbe1c32a3feb8b50 unverified MIT (permissive)
Backprop KF: Learning Discriminative Deterministic State Estimators 23 May 2016 tiboat/BackpropKF_Reproduction/Position_FF.py 1e50303ff8e538ed ran · our draft was wrong 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".

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