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linear_rampup

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

linear_rampup appears in the code Syntology harvested for 33 papers, as 15 distinct code bodies found in 37 places (a place is one code body under one paper). At least one of them ran in 9 of the papers; 5 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 linear_rampup 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 7 of the 15 distinct code bodies named linear_rampup; 8 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
2ran
8unverified
5fingerprinted

Licence is a property of each copy, so it is counted per place: 14 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

33 papers shown of 33, 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; 5 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
PRESTO: Preimage-Informed Instruction Optimization for Prompting Black-Box LLMs added by Syntology 2025-10 (from id) mlvlab/PRESTO/Induction/experiments/LlamaForMLPRegression.py c5fbe85a81a540d2 unverified MIT (permissive)
OTSurv: A Novel Multiple Instance Learning Framework for Survival Prediction with Heterogeneity-aware Optimal Transport 25 Jun 2025 y-research-sbu/otsurv/src/mil_models/otsurv_component/ot_attn.py 069122bd5268cc56 unverified licence not identified · pointer only
PMT: Progressive Mean Teacher via Exploring Temporal Consistency for Semi-Supervised Medical Image Segmentation 8 Sep 2024 axi404/pmt/code/utils/ramps.py 069122bd5268cc56 unverified no licence file found · pointer only
Tackling Noisy Clients in Federated Learning with End-to-end Label Correction 8 Aug 2024 sprinter1999/fedelc/fl_models/update.py 6944acdaa94d65ef ran · honoured contract fingerprinted MIT (permissive)
This Probably Looks Exactly Like That: An Invertible Prototypical Network 16 Jul 2024 craymichael/ProtoFlow/protoflow/training.py 4eae3eb7fc10b826 ran GPL-2.0 (copyleft) · pointer only
The Victim and The Beneficiary: Exploiting a Poisoned Model to Train a Clean Model on Poisoned Data 17 Apr 2024 zixuan-zhu/vab/attention_mix_ImageNet.py f90d0f73baf01696 ran no licence file found · pointer only
Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image Segmentation 13 Apr 2024 MQinghe/MiDSS/code/utils/ramps.py 069122bd5268cc56 unverified Apache-2.0 (permissive)
P$^2$OT: Progressive Partial Optimal Transport for Deep Imbalanced Clustering 17 Jan 2024 rhfeiyang/ppot/losses/ramps.py 069122bd5268cc56 unverified no licence file found · pointer only
Dirichlet-Based Prediction Calibration for Learning with Noisy Labels 13 Jan 2024 chenchenzong/dpc/AAAI2024_DPC_code/Train_cifar.py 767f49a34c3cb79a unverified no licence file found · pointer only
FreeAL: Towards Human-Free Active Learning in the Era of Large Language Models 27 Nov 2023 Justherozen/FreeAL/self_training_slm/mytrainer.py 069122bd5268cc56 unverified no licence file found · pointer only
Latent Class-Conditional Noise Model 19 Feb 2023 identical code first harvested elsewhere 767f49a34c3cb79a unverified licence of this copy not recorded
Avoiding spurious correlations via logit correction 2 Dec 2022 shengliu66/lc/util.py 069122bd5268cc56 unverified MIT (permissive)
Learning Debiased Classifier with Biased Committee 22 Jun 2022 nayeong-v-kim/lwbc/SSL/main_byol.py 069122bd5268cc56 unverified MIT (permissive)
Rethinking Noisy Label Models: Labeler-Dependent Noise with Adversarial Awareness 28 May 2021 LiJunnan1992/DivideMix/Train_cifar.py 767f49a34c3cb79a unverified MIT (permissive)
Rethinking Noisy Label Models: Labeler-Dependent Noise with Adversarial Awareness 28 May 2021 LiJunnan1992/DivideMix/Train_webvision.py 640159e7c5de036e unverified MIT (permissive)
Rethinking Noisy Label Models: Labeler-Dependent Noise with Adversarial Awareness 28 May 2021 LiJunnan1992/DivideMix/Train_webvision_parallel.py 796e68a685d5c33a unverified MIT (permissive)
Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labels 25 Mar 2021 ContrastToDivide/C2D/main_cifar.py 6944acdaa94d65ef ran · honoured contract fingerprinted MIT (permissive)
Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labels 25 Mar 2021 ContrastToDivide/C2D/Train_webvision.py 371eb42215de99b0 unverified MIT (permissive)
FINE Samples for Learning with Noisy Labels 23 Feb 2021 kthyeon/fine_official/dividemix/Train_cifar.py 767f49a34c3cb79a unverified no licence file found · pointer only
Semi-supervised Medical Image Segmentation through Dual-task Consistency 9 Sep 2020 HiLab-git/DTC/code/utils/ramps.py 069122bd5268cc56 unverified MIT (permissive)
Why Normalizing Flows Fail to Detect Out-of-Distribution Data 15 Jun 2020 identical code first harvested elsewhere 435e9f0bb0206bec ran · honoured contract fingerprinted licence of this copy not recorded
Towards Accurate and Robust Domain Adaptation under Noisy Environments 27 Apr 2020 zhyhan/RDA/model/RDA.py b62a22ec4dd59e09 ran · honoured contract fingerprinted Apache-2.0 (permissive)
Semi-Supervised Semantic Segmentation with Cross-Consistency Training 19 Mar 2020 Luoxd1996/DTC/code/utils/ramps.py 069122bd5268cc56 unverified MIT (permissive)
DivideMix: Learning with Noisy Labels as Semi-supervised Learning 18 Feb 2020 identical code first harvested elsewhere 767f49a34c3cb79a unverified licence of this copy not recorded
Semi-Supervised Learning with Normalizing Flows 30 Dec 2019 izmailovpavel/flowgmm/experiments/train_flows/train_semisup_cons.py 435e9f0bb0206bec ran · honoured contract fingerprinted no licence file found · pointer only
Learning to Impute: A General Framework for Semi-supervised Learning 22 Dec 2019 VICO-UoE/L2I/Classification/train-L2I-L.py b01ff1066793657e ran · honoured contract fingerprinted no licence file found · pointer only
Triple Generative Adversarial Networks 20 Dec 2019 taufikxu/Triple-GAN/library/loss_cla.py 069122bd5268cc56 unverified MIT (permissive)
Rethinking deep active learning: Using unlabeled data at model training 19 Nov 2019 osimeoni/RethinkingDeepActiveLearning/lib/ramps.py 069122bd5268cc56 unverified MIT (permissive)
Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation 16 Jul 2019 bx0903/pdc/code/utils/ramps.py 069122bd5268cc56 unverified MIT (permissive)
MixMatch: A Holistic Approach to Semi-Supervised Learning 6 May 2019 dLutscher/MixMatch-TransferLearning/utils/utils.py 0aaac678344cd89f ran · honoured contract fingerprinted no licence file found · pointer only
Label Propagation for Deep Semi-supervised Learning 9 Apr 2019 ahmetius/LP-DeepSSL/mean_teacher/ramps.py 069122bd5268cc56 unverified MIT (permissive)
arXiv:aaai_29250 Feng-peng-Li/Regroup-Loss-Median-to-Combat-Label-Noise/Train_cifar_semi.py d20f25261a80c92b unverified MIT (permissive)
arXiv:aaai_29250 Feng-peng-Li/Regroup-Loss-Median-to-Combat-Label-Noise/Train_clothing1M.py 7a80c07e68676a15 unverified MIT (permissive)
arXiv:Wang_MCF_Mutual_Correction_Framework_for_Semi-Supervised_Medical_Image_Segmentation_CVPR_2023_paper WYC-321/MCF/code/utils/ramps.py 069122bd5268cc56 unverified MIT (permissive)
arXiv:Miao_CauSSL_Causality-inspired_Semi-supervised_Learning_for_Medical_Image_Segmentation_ICCV_2023_paper JuzhengMiao/CauSSL/utils/ramps.py 069122bd5268cc56 unverified MIT (permissive)
arXiv:Chen_MagicNet_Semi-Supervised_Multi-Organ_Segmentation_via_Magic-Cube_Partition_and_Recovery_CVPR_2023_paper DeepMed-Lab-ECNU/MagicNet/code/utils/ramps.py 069122bd5268cc56 unverified MIT (permissive)
arXiv:136640190 MengyuanChen21/ECCV2022-DELU/utils/wsad_utils.py 069122bd5268cc56 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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