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int_parameter

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

int_parameter appears in the code Syntology harvested for 35 papers, as 12 distinct code bodies found in 37 places (a place is one code body under one paper). At least one of them ran in 24 of the papers; 6 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 int_parameter 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 12 distinct code bodies named int_parameter; 6 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

5ran · honoured contract
0ran · violated contract
0ran · our draft was wrong
0ran · fixture could not drive it
1ran
6unverified
6fingerprinted

Licence is a property of each copy, so it is counted per place: 8 of the 37 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

35 papers shown of 35, newest first; 37 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; 2 papers have no page here and are shown by arXiv id only. 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
CARPRT: Class-Aware Zero-Shot Prompt Reweighting for Black-Box Vision-Language Models added by Syntology 2026-07 (from id) tmlr-group/CARPRT/datasets/augmix_ops.py beaa91443124b324 ran · honoured contract fingerprinted no licence file found · pointer only
EntAugment: Entropy-Driven Adaptive Data Augmentation Framework for Image Classification 10 Sep 2024 jackbrocp/entaugment/augmentation/entaugment.py b3a390f777b2388c unverified no licence file found · pointer only
Distribution-Aware Data Expansion with Diffusion Models 11 Mar 2024 haoweiz23/distdiff/augmentations/augmentations.py fa762f2f1e10f2e4 ran · honoured contract fingerprinted no licence file found · pointer only
Dirichlet-Based Prediction Calibration for Learning with Noisy Labels 13 Jan 2024 chenchenzong/dpc/AAAI2024_DPC_code/augmentation_archive.py 7900a3a6deef908b ran fingerprinted no licence file found · pointer only
Class Gradient Projection For Continual Learning 25 Nov 2023 zackschen/CGP/augmentations.py fa762f2f1e10f2e4 ran · honoured contract fingerprinted no licence file found · pointer only
Robust Data Pruning under Label Noise via Maximizing Re-labeling Accuracy 2 Nov 2023 kaist-dmlab/Prune4Rel/deepcore/datasets/augmentation_archive.py 7900a3a6deef908b ran fingerprinted MIT (permissive)
Domain Adaptation based Object Detection for Autonomous Driving in Foggy and Rainy Weather 18 Jul 2023 jinlong17/da-detect/efficientderain-master/augmentations.py fa762f2f1e10f2e4 ran · honoured contract fingerprinted MIT (permissive)
Stimulative Training++: Go Beyond The Performance Limits of Residual Networks 4 May 2023 sunshine-ye/nips22-st/aug_lib.py b3a390f777b2388c unverified MIT (permissive)
Towards Multi-Modal Sarcasm Detection via Hierarchical Congruity Modeling with Knowledge Enhancement 7 Oct 2022 less-and-less-bugs/hkemodel/aug_lib.py f0777303762c1ba8 unverified MIT (permissive)
Attention Consistency on Visual Corruptions for Single-Source Domain Generalization 27 Apr 2022 explainableml/acvc/preprocessing/image/AugMixGenerator.py fa762f2f1e10f2e4 ran · honoured contract fingerprinted MIT (permissive)
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification 30 Mar 2022 activatedgeek/bayesian-classification/src/data_aug/augmentations.py beaa91443124b324 ran · honoured contract fingerprinted Apache-2.0 (permissive)
A ConvNet for the 2020s 10 Jan 2022 0jason000/convnext/src/transforms.py e5a370ee6856bd8d unverified Apache-2.0 (permissive)
PRIME: A few primitives can boost robustness to common corruptions 27 Dec 2021 amodas/PRIME-augmentations/utils/augmix.py 8b20dcbdf9e59b56 ran · honoured contract fingerprinted Apache-2.0 (permissive)
PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures 9 Dec 2021 andyzoujm/pixmix/pixmix_utils.py fa762f2f1e10f2e4 ran · honoured contract fingerprinted MIT (permissive)
A Unified Benchmark for the Unknown Detection Capability of Deep Neural Networks 1 Dec 2021 daintlab/unknown-detection-benchmarks/methods/augmix/augmentations.py beaa91443124b324 ran · honoured contract fingerprinted MIT (permissive)
Masked Autoencoders Are Scalable Vision Learners 11 Nov 2021 0jason000/mae_vit/src/transforms.py e5a370ee6856bd8d unverified Apache-2.0 (permissive)
AugMax: Adversarial Composition of Random Augmentations for Robust Training 26 Oct 2021 VITA-Group/AugMax/augmax_modules/augmentations.py fa762f2f1e10f2e4 ran · honoured contract fingerprinted MIT (permissive)
Test time Adaptation through Perturbation Robustness 19 Oct 2021 prabhuteja12/pest/dataset/augmix.py f0217da1d26e045b unverified MIT (permissive)
MEMO: Test Time Robustness via Adaptation and Augmentation 18 Oct 2021 kowshikthopalli/sista/SISTA_DA/image_target_memo.py cce32b359dda4b20 ran · honoured contract fingerprinted no licence file found · pointer only
Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domain 19 Aug 2021 iCGY96/APR/datasets/APR.py beaa91443124b324 ran · honoured contract fingerprinted MIT (permissive)
Diverse Gaussian Noise Consistency Regularization for Robustness and Uncertainty Calibration 2 Apr 2021 theot1/dign/DiGN.py 8b20dcbdf9e59b56 ran · honoured contract fingerprinted MIT (permissive)
EfficientDeRain: Learning Pixel-wise Dilation Filtering for High-Efficiency Single-Image Deraining 19 Sep 2020 tsingqguo/efficientderain/augmentations.py fa762f2f1e10f2e4 ran · honoured contract fingerprinted MIT (permissive)
OnlineAugment: Online Data Augmentation with Less Domain Knowledge 17 Jul 2020 zhiqiangdon/online-augment/archive_policies.py 7900a3a6deef908b ran fingerprinted Apache-2.0 (permissive)
DADA: Differentiable Automatic Data Augmentation 8 Mar 2020 VDIGPKU/DADA/fast-autoaugment/archive.py 7900a3a6deef908b ran fingerprinted MIT (permissive)
Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning 16 Jan 2020 uvavision/Curriculum-Labeling/utils/archive.py 7900a3a6deef908b ran fingerprinted MIT (permissive)
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty 5 Dec 2019 identical code first harvested elsewhere fa762f2f1e10f2e4 ran · honoured contract fingerprinted licence of this copy not recorded
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty 5 Dec 2019 ma7555/Augz/augmix.py 4eb727da6e791a2f ran · honoured contract fingerprinted no licence file found · pointer only
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty 5 Dec 2019 szacho/augmix-tf/augmix/helpers.py 9fc8a5bff5d4d44e unverified MIT (permissive)
GhostNet: More Features from Cheap Operations 27 Nov 2019 0jason000/S-GhostNet/src/autoaug.py e5a370ee6856bd8d unverified Apache-2.0 (permissive)
Fast AutoAugment 1 May 2019 kakaobrain/fast-autoaugment/archive.py 7900a3a6deef908b ran fingerprinted MIT (permissive)
Unsupervised Data Augmentation for Consistency Training 29 Apr 2019 ildoonet/unsupervised-data-augmentation/archive.py 7900a3a6deef908b ran fingerprinted Apache-2.0 (permissive)
AutoAugment: Learning Augmentation Policies from Data 24 May 2018 TillBeemelmanns/auto-augment-tf2-operations/tfops_aug/augmentation_operations.py d9e70dca8d829825 unverified MIT (permissive)
Densely Connected Convolutional Networks 25 Aug 2016 0jason000/DenseNet/src/transforms.py e5a370ee6856bd8d unverified Apache-2.0 (permissive)
Rethinking the Inception Architecture for Computer Vision 2 Dec 2015 0jason000/inception_v3/src/transforms.py e5a370ee6856bd8d unverified Apache-2.0 (permissive)
Going Deeper with Convolutions 17 Sep 2014 0jason000/GoogleNet/src/transforms.py e5a370ee6856bd8d unverified Apache-2.0 (permissive)
arXiv:aaai_25958 tsingqguo/bgmix/augmentations.py fa762f2f1e10f2e4 ran · honoured contract fingerprinted MIT (permissive)
arXiv:Li_Learning_From_Noisy_Data_With_Robust_Representation_Learning_ICCV_2021_paper salesforce/RRL/augmentations.py fa762f2f1e10f2e4 ran · honoured contract fingerprinted BSD-3-Clause (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