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stem

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

stem appears in the code Syntology harvested for 11 papers, as 15 distinct code bodies found in 17 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 stem 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 15 distinct code bodies named stem; 13 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
2ran · our draft was wrong
0ran · fixture could not drive it
0ran
13unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 3 of the 17 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

11 papers shown of 11, newest first; 17 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
The Error of Deep Operator Networks Is the Sum of Its Parts: Branch-Trunk and Mode Error Decompositions added by Syntology 2026-02 (from id) jotaraz/ModeDecomposition-DeepONets/make_seed_sweep.py 1ca271fabe499bd1 unverified no licence file found · pointer only
CAS-ViT: Convolutional Additive Self-attention Vision Transformers for Efficient Mobile Applications 7 Aug 2024 tianfang-zhang/cas-vit/classification/model/rcvit.py b72ba06382340c08 ran · our draft was wrong MIT (permissive)
Rethinking Vision Transformers for MobileNet Size and Speed 15 Dec 2022 identical code first harvested elsewhere 560e3160f50add18 ran · our draft was wrong licence of this copy not recorded
EfficientFormer: Vision Transformers at MobileNet Speed 2 Jun 2022 snap-research/efficientformer/models/efficientformer.py b72ba06382340c08 ran · our draft was wrong licence not identified · pointer only
Cluster & Tune: Boost Cold Start Performance in Text Classification 20 Mar 2022 ibm/intermediate-training-using-clustering/run_experiment.py ac51901ff69f7646 unverified Apache-2.0 (permissive)
Not All Patches are What You Need: Expediting Vision Transformers via Token Reorganizations 16 Feb 2022 Retinal-Research/EVIT-UNET/unet/eff_unet.py 560e3160f50add18 ran · our draft was wrong MIT (permissive)
Next Day Wildfire Spread: A Machine Learning Data Set to Predict Wildfire Spreading from Remote-Sensing Data 4 Dec 2021 satellitevu/satellitevu-aws-disaster-response-hackathon/deep_learning/model_resunet.py 48305b591734eef2 unverified Apache-2.0 (permissive)
Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples 28 Apr 2021 sayakpaul/PAWS-TF/models/resnet20.py 4771975a4479f016 unverified Apache-2.0 (permissive)
FlexiBO: A Decoupled Cost-Aware Multi-Objective Optimization Approach for Deep Neural Networks 18 Jan 2020 softsys4ai/FlexiBO/networks/resnet50.py 6bde0b827d9c7e67 unverified MIT (permissive)
FlexiBO: A Decoupled Cost-Aware Multi-Objective Optimization Approach for Deep Neural Networks 18 Jan 2020 softsys4ai/FlexiBO/networks/squeezenet.py af56cd5321ea5452 unverified MIT (permissive)
Aggregated Residual Transformations for Deep Neural Networks 16 Nov 2016 Sakib1263/1DResNet-Builder-KERAS/Codes/ResNet_1DCNN.py ab72eeba5f2a8e01 unverified MIT (permissive)
Aggregated Residual Transformations for Deep Neural Networks 16 Nov 2016 Sakib1263/1DResNet-Builder-KERAS/Codes/ResNet_2DCNN.py 0ac04442aabc2a42 unverified MIT (permissive)
Aggregated Residual Transformations for Deep Neural Networks 16 Nov 2016 Sakib1263/1DResNet-Builder-KERAS/Codes/ResNet_v2_1DCNN.py 7ef98d5a06da35e4 unverified MIT (permissive)
Aggregated Residual Transformations for Deep Neural Networks 16 Nov 2016 Sakib1263/1DResNet-Builder-KERAS/Codes/ResNet_v2_2DCNN.py 3e01a98528a53c09 unverified MIT (permissive)
Aggregated Residual Transformations for Deep Neural Networks 16 Nov 2016 Sakib1263/1DResNet-Builder-KERAS/Codes/SE_ResNet_1DCNN.py 57b2ad7b14fe19fe unverified MIT (permissive)
Aggregated Residual Transformations for Deep Neural Networks 16 Nov 2016 Sakib1263/1DResNet-Builder-KERAS/Codes/SE_ResNet_2DCNN.py 1ba22ee1d1ad73ef unverified MIT (permissive)
Densely Connected Convolutional Networks 25 Aug 2016 Sakib1263/DenseNet-1D-2D-Tensorflow-Keras/Codes/DenseNet_1DCNN.py 050e4107164ed015 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