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shuffle_data

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

shuffle_data appears in the code Syntology harvested for 34 papers, as 19 distinct code bodies found in 34 places (a place is one code body under one paper). At least one of them ran in 18 of the papers; 2 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 shuffle_data 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 4 of the 19 distinct code bodies named shuffle_data; 15 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
2ran
15unverified
2fingerprinted

Licence is a property of each copy, so it is counted per place: 4 of the 34 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

34 papers shown of 34, newest first; 34 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; 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
Learning Multimodal Volumetric Features for Large-Scale Neuron Tracing 5 Jan 2024 Levishery/Flywire-Neuron-Tracing/Pointnet/provider.py 06353aadebc1724b ran no licence file found · pointer only
TEILP: Time Prediction over Knowledge Graphs via Logical Reasoning 25 Dec 2023 xiongsiheng/TEILP/src/utlis.py 79d4517e4b0c6309 unverified MIT (permissive)
3DCoMPaT$^{++}$: An improved Large-scale 3D Vision Dataset for Compositional Recognition 27 Oct 2023 cattalyya/3dcompat-challenge/models/3D/provider.py 4794ca8cee394259 ran BSD-3-Clause (permissive)
Open-Vocabulary Affordance Detection in 3D Point Clouds 4 Mar 2023 Fsoft-AIC/Open-Vocabulary-Affordance-Detection-in-3D-Point-Clouds/utils/provider.py 06353aadebc1724b ran MIT (permissive)
Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous Actions 24 Oct 2022 haanvid/kmis/util.py a66e33d74b337fed unverified MIT (permissive)
e-CARE: a New Dataset for Exploring Explainable Causal Reasoning 12 May 2022 Waste-Wood/e-CARE/code/adversarial_filtering.py 1d79ee29f386b615 unverified MIT (permissive)
Benchmarking and Analyzing Point Cloud Classification under Corruptions 7 Feb 2022 jiawei-ren/modelnetc/GDANet/provider.py 06353aadebc1724b ran Apache-2.0 (permissive)
Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions 28 Jan 2022 dogyoonlee/RSMix/pointnet2_rsmix/modelnet_h5_dataset.py 06353aadebc1724b ran MIT (permissive)
RibSeg Dataset and Strong Point Cloud Baselines for Rib Segmentation from CT Scans 17 Sep 2021 m3dv/ribseg/data_utils/data_aug.py 06353aadebc1724b ran Apache-2.0 (permissive)
Zero-Shot Knowledge Distillation from a Decision-Based Black-Box Model 7 Jun 2021 zwang84/zsdb3kd/train_model_kd.py d60c92fc9f2f5916 unverified no licence file found · pointer only
3D AffordanceNet: A Benchmark for Visual Object Affordance Understanding 30 Mar 2021 Gorilla-Lab-SCUT/AffordanceNet/utils/provider.py 06353aadebc1724b ran MIT (permissive)
RPM-Net: Recurrent Prediction of Motion and Parts from Point Cloud 26 Jun 2020 Salingo/RPM-Net/code/utils/provider.py 06353aadebc1724b ran MIT (permissive)
Generative PointNet: Deep Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classification 2 Apr 2020 fei960922/GPointNet/utils/data_util.py d4b3c3236d23c44f unverified MIT (permissive)
Learning to Segment 3D Point Clouds in 2D Image Space 12 Mar 2020 Zhang-VISLab/Learning-to-Segment-3D-Point-Clouds-in-2D-Image-Space/fun_provider.py 06353aadebc1724b ran MIT (permissive)
Classifying the classifier: dissecting the weight space of neural networks 13 Feb 2020 gabrieleilertsen/nws/util.py 2820413d08417942 ran · our draft was wrong fingerprinted BSD-3-Clause (permissive)
ABCNet: An attention-based method for particle tagging 2020-01 (from id) ViniciusMikuni/ABCNet/utils/provider.py 5efaa25385e2684f unverified MIT (permissive)
Confident Learning: Estimating Uncertainty in Dataset Labels 31 Oct 2019 chang-yue/ctrl/tabular/utils.py 9225a7b054268f0b unverified MIT (permissive)
Rotation Invariant Convolutions for 3D Point Clouds Deep Learning 17 Aug 2019 hkust-vgd/riconv/utils/provider.py 780276d57931e243 unverified MIT (permissive)
Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data 13 Aug 2019 hkust-vgd/scanobjectnn/3DmFV-Net/provider.py 06353aadebc1724b ran MIT (permissive)
Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor Infiltrating Lymphocytes in Invasive Breast Cancer 26 May 2019 SBU-BMI/quip_cancer_segmentation/training/tumor_utils.py 9e209b818d05bc4c unverified BSD-3-Clause (permissive)
Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules 14 May 2019 arcelien/pba/pba/data_utils.py 9da9105a07289a15 unverified Apache-2.0 recorded; this copy not marked cleared · pointer only
Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features 22 Apr 2019 KuangenZhang/ldgcnn/provider.py d7c961afec8cc839 unverified MIT (permissive)
Episodic Training for Domain Generalization 31 Jan 2019 HAHA-DL/Episodic-DG/common/utils.py e66d8098b50090e3 unverified MIT (permissive)
Path-Invariant Map Networks 31 Dec 2018 zaiweizhang/path_invariance_map_network/provider.py 06353aadebc1724b ran BSD-3-Clause (permissive)
PointConv: Deep Convolutional Networks on 3D Point Clouds 17 Nov 2018 DylanWusee/pointconv_pytorch/provider.py 06353aadebc1724b ran MIT (permissive)
Accelerated Bregman Proximal Gradient Methods for Relatively Smooth Convex Optimization 2018-08 (from id) Microsoft/accbpg/accbpg/utils.py fb69d5f3793a0e5b unverified MIT (permissive)
MGGAN: Solving Mode Collapse using Manifold Guided Training 12 Apr 2018 QuickSolverKyle/Tensorflow-MyGANs/utils.py 9518c248bfcc27be unverified MIT (permissive)
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters 30 Mar 2018 xyf513/SpiderCNN/utils/provider.py 06353aadebc1724b ran MIT (permissive)
Local Spectral Graph Convolution for Point Set Feature Learning 15 Mar 2018 fate3439/LocalSpecGCN/utils/provider.py 06353aadebc1724b ran MIT (permissive)
Progressive Growing of GANs for Improved Quality, Stability, and Variation 27 Oct 2017 AndreasWieg/PC-PGGAN/util.py c1ac6b8f2fcdbe94 unverified MIT (permissive)
Learning to Generalize: Meta-Learning for Domain Generalization 10 Oct 2017 HAHA-DL/MLDG/utils.py e66d8098b50090e3 unverified MIT (permissive)
Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data 18 Oct 2016 ashishdandekar/Privacy-at-risk/PATE/teachers.py 506614109988e897 ran · honoured contract fingerprinted GPL-3.0 (copyleft) · pointer only
arXiv:aaai_27944 FengZicai/Interpretable3D/PointNet2/provider.py 06353aadebc1724b ran MIT (permissive)
arXiv:136620053 Tianxinhuang/PCDNet/provider.py 06353aadebc1724b ran 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".

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