Home › Code › get_mnist

get_mnist

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

get_mnist appears in the code Syntology harvested for 10 papers, as 10 distinct code bodies found in 10 places (a place is one code body under one paper). At least one of them ran in 0 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 get_mnist 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 0 of the 10 distinct code bodies named get_mnist; 10 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
0ran
10unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 1 of the 10 places is 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

10 papers shown of 10, newest first; 10 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
SafeAdapt: Provably Safe Policy Updates in Deep Reinforcement Learning added by Syntology 2026-04 (from id) maxanisimov/provably-safe-policy-updates/src/data_utils.py bcd17f3fc951f451 unverified MIT (permissive)
Test-time Adaptation for Regression by Subspace Alignment 4 Oct 2024 kzkadc/regression-tta/dataset/raw_datasets.py dde50ff44f98f9b9 unverified licence not identified · pointer only
Energy-based Automated Model Evaluation 23 Jan 2024 pengr/energy_autoeval/datasets.py 32ba6ca3329fac10 unverified Apache-2.0 (permissive)
Particle algorithms for maximum likelihood training of latent variable models 27 Apr 2022 juankuntz/parem/torch/parem/utils.py 1ae194faab4a6b78 unverified MIT (permissive)
A Bregman Learning Framework for Sparse Neural Networks 10 May 2021 TimRoith/BregmanLearning/utils/datasets.py 2f27d73dd37f8bdc unverified MIT (permissive)
CLIP: Cheap Lipschitz Training of Neural Networks 23 Mar 2021 TimRoith/CLIP/utils/datasets.py b50182fd2c352f6c unverified MIT (permissive)
Reliable Post hoc Explanations: Modeling Uncertainty in Explainability 11 Aug 2020 dylan-slack/modeling-uncertainty-local-explainability/bayes/data_routines.py e947cb61477d2661 unverified MIT (permissive)
Logit Pairing Methods Can Fool Gradient-Based Attacks 29 Oct 2018 uds-lsv/evaluating-logit-pairing-methods/mnist_cifar10/data_loader.py 60f00d58b4540401 unverified Apache-2.0 (permissive)
Faster Neural Network Training with Approximate Tensor Operations 21 May 2018 acsl-technion/approx/src/pytorch/mnist_mlp_pytorch/data.py 485595ea85ffbcc1 unverified BSD-3-Clause (permissive)
Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding 25 Dec 2017 huguyuehuhu/fasttsne/benchmarks/benchmark_tsne.py 923ed39e937ce5e1 unverified 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