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average_gradients

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

average_gradients appears in the code Syntology harvested for 20 papers, as 18 distinct code bodies found in 20 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 average_gradients 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 18 distinct code bodies named average_gradients; 18 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
18unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 0 of the 20 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

20 papers shown of 20, newest first; 20 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. 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
3D Object Detection with a Self-supervised Lidar Scene Flow Backbone 2 May 2022 WeijingShi/Point-GNN/util/tf_util.py a0172c48afaa4a94 unverified MIT (permissive)
SUPERB: Speech processing Universal PERformance Benchmark 3 May 2021 usc-sail/fed-ser-leakage/mitigation/update.py e5cb81a78546f033 unverified MIT (permissive)
Integrating Distributed Architectures in Highly Modular RL Libraries 6 Jul 2020 PyTorchRL/pytorchrl/pytorchrl/scheme/utils.py 9c88cb72fa692623 unverified MIT (permissive)
Clean-Label Backdoor Attacks on Video Recognition Models 6 Mar 2020 ShihaoZhaoZSH/Video-Backdoor-Attack/utils.py 8553e59eb6f6feab unverified Apache-2.0 (permissive)
Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features 22 Apr 2019 KuangenZhang/ldgcnn/part_seg/train_multi_gpu.py 34732848a23a9c10 unverified MIT (permissive)
Deep High-Resolution Representation Learning for Human Pose Estimation 25 Feb 2019 mks0601/PoseFix_RELEASE/lib/tfflat/net_utils.py 94ffb5d47eee6270 unverified MIT (permissive)
Deep Bilevel Learning 5 Sep 2018 sjenni/DeepBilevel/utils.py c9376dbf970ce49f unverified MIT (permissive)
Semi-Autoregressive Neural Machine Translation 26 Aug 2018 chqiwang/sa-nmt/utils.py 3ad1487952836d07 unverified Apache-2.0 (permissive)
Instance-level Human Parsing via Part Grouping Network 1 Aug 2018 Engineering-Course/CIHP_PGN/train_pgn.py c598804162457140 unverified MIT (permissive)
PReMVOS: Proposal-generation, Refinement and Merging for Video Object Segmentation 24 Jul 2018 AyaLotfy/PReMVOS-trial/code/ReID_net/Util.py d82c26b8b96a063f unverified MIT (permissive)
Differentiable Compositional Kernel Learning for Gaussian Processes 12 Jun 2018 thjashin/spectral-stein-grad/utils/multi_gpu.py 19a2e9b1682d85e8 unverified MIT (permissive)
Behavioral Cloning from Observation 4 May 2018 greerviau/GlorifiedCruiseControl/SCNN_lanenet/train_lanenet.py 10e8814dfd1ac64c unverified MIT (permissive)
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters 30 Mar 2018 xyf513/SpiderCNN/train_xyz.py 0e0c2135e80629bb unverified MIT (permissive)
Variance Networks: When Expectation Does Not Meet Your Expectations 10 Mar 2018 da-molchanov/variance-networks/variance-networks-tf/nets/utils.py 8553e59eb6f6feab unverified Apache-2.0 (permissive)
Deep contextualized word representations 15 Feb 2018 allenai/bilm-tf/bilm/training.py 821ed0b50fc8c75a unverified Apache-2.0 (permissive)
Dynamic Routing Between Capsules 26 Oct 2017 naturomics/CapsNet-Tensorflow/dist_version/distributed_train.py 25474dc6ec455a0d unverified Apache-2.0 (permissive)
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments 7 Jun 2017 Yutongamber/MADDPG/maddpg-pytorch/algorithms/maddpg.py a3e542c9d6910a27 unverified MIT (permissive)
Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets 15 Mar 2017 ZhenYangIACAS/NMT_GAN/model.py 8a792606b6037ff1 unverified Apache-2.0 (permissive)
The Predictron: End-To-End Learning and Planning 28 Dec 2016 zhongwen/predictron/train_multigpu.py a7a9c8edfe63551c unverified MIT (permissive)
All you need is a good init 19 Nov 2015 JonasWechsler/DeepLearningLab5/cifar10/cifar10_multi_gpu_train.py 25474dc6ec455a0d 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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