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load_batch

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

load_batch appears in the code Syntology harvested for 18 papers, as 13 distinct code bodies found in 24 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 load_batch 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 13 distinct code bodies named load_batch; 11 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
0ran · our draft was wrong
0ran · fixture could not drive it
2ran
11unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 2 of the 24 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

18 papers shown of 18, newest first; 24 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; 3 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
Robust Classification by Coupling Data Mollification with Label Smoothing 3 Jun 2024 markusheinonen/supervised-mollification/src/results.py cec2c92b1206664a ran no licence file found · pointer only
Outlier Detection in the DESI Bright Galaxy Survey 2023-07 (from id) pmelchior/spender/spender/util.py 98d5335a1f96e36b ran MIT (permissive)
Tight Certification of Adversarially Trained Neural Networks via Nonconvex Low-Rank Semidefinite Relaxations 30 Nov 2022 IBM/CROWN-Robustness-Certification/setup_cifar.py ab7faf0047f2c872 unverified Apache-2.0 (permissive)
Tight Certification of Adversarially Trained Neural Networks via Nonconvex Low-Rank Semidefinite Relaxations 30 Nov 2022 IBM/CROWN-Robustness-Certification/setup_cifar.py 3b4fb87c66244ebf unverified Apache-2.0 (permissive)
Symbolic Regression via Neural-Guided Genetic Programming Population Seeding 29 Oct 2021 brendenpetersen/deep-symbolic-optimization/dso/dso/memory.py b7cae67defd7ffaf unverified BSD-3-Clause (permissive)
Hybrid Models for Learning to Branch 26 Jun 2020 pg2455/Hybrid-learn2branch/utilities_hybrid.py 01d962ccce151759 unverified MIT (permissive)
Proper Network Interpretability Helps Adversarial Robustness in Classification 26 Jun 2020 identical code first harvested elsewhere 3b4fb87c66244ebf unverified licence of this copy not recorded
Wasserstein-2 Generative Networks 28 Sep 2019 lit-leo/inner-convex-nn/vae/vanila_vae.py 79775596097e420c unverified MIT (permissive)
3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions 15 May 2019 prajwalsingh/TreeGCN-ED/utils.py 7d586ed5128718bb unverified MIT (permissive)
Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces 8 Feb 2019 eric-vader/HD-BO-Additive-Models/hdbo/boattack/objective_func/tf_models/setup_cifar.py ab7faf0047f2c872 unverified MIT (permissive)
Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces 8 Feb 2019 eric-vader/HD-BO-Additive-Models/hdbo/boattack/objective_func/tf_models/setup_cifar.py 3b4fb87c66244ebf unverified MIT (permissive)
Efficient Neural Network Robustness Certification with General Activation Functions 2 Nov 2018 huanzhang12/CertifiedReLURobustness/setup_cifar.py ab7faf0047f2c872 unverified BSD-2-Clause (permissive)
Efficient Neural Network Robustness Certification with General Activation Functions 2 Nov 2018 huanzhang12/CertifiedReLURobustness/setup_cifar.py 3b4fb87c66244ebf unverified BSD-2-Clause (permissive)
AutoZOOM: Autoencoder-based Zeroth Order Optimization Method for Attacking Black-box Neural Networks 30 May 2018 IBM/Autozoom-Attack/setup_cifar.py ab7faf0047f2c872 unverified Apache-2.0 (permissive)
AutoZOOM: Autoencoder-based Zeroth Order Optimization Method for Attacking Black-box Neural Networks 30 May 2018 IBM/Autozoom-Attack/setup_cifar.py 3b4fb87c66244ebf unverified Apache-2.0 (permissive)
GenAttack: Practical Black-box Attacks with Gradient-Free Optimization 28 May 2018 nesl/adversarial_genattack/setup_cifar.py ab7faf0047f2c872 unverified MIT (permissive)
GenAttack: Practical Black-box Attacks with Gradient-Free Optimization 28 May 2018 nesl/adversarial_genattack/setup_cifar.py 3b4fb87c66244ebf unverified MIT (permissive)
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks 7 Oct 2016 hendrycks/error-detection/Vision/load_cifar10.py 8633f606824fb007 unverified MIT (permissive)
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks 7 Oct 2016 hendrycks/error-detection/Vision/load_cifar100.py 4e5831192b762838 unverified MIT (permissive)
Early Methods for Detecting Adversarial Images 1 Aug 2016 hendrycks/fooling/CIFAR/load_cifar10.py 8633f606824fb007 unverified MIT (permissive)
Gaussian Error Linear Units (GELUs) 27 Jun 2016 hendrycks/GELUs/load_cifar10.py 8633f606824fb007 unverified MIT (permissive)
arXiv:aaai_5987 RingBDStack/MA-GCNNs/M-GCNNs/utils.py 98185a078f416a58 unverified MIT (permissive)
arXiv:aaai_20650 IBM/UAE/Supervised/setup_cifar.py b60dcd06b47c331c unverified Apache-2.0 (permissive)
arXiv:Schelling_RADU_Ray-Aligned_Depth_Update_Convolutions_for_ToF_Data_Denoising_CVPR_2022_paper schellmi42/RADU/code_dl/data_ops/data_loader.py 88770eb719772acf 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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