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weight_variable

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

weight_variable appears in the code Syntology harvested for 29 papers, as 27 distinct code bodies found in 37 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 weight_variable 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 27 distinct code bodies named weight_variable; 27 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
27unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 6 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

29 papers shown of 29, 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. 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 Flexible Body Collision Dynamics with Hierarchical Contact Mesh Transformer 19 Dec 2023 google-deepmind/deepmind-research/alphafold_casp13/two_dim_convnet.py 7d7dfab4bbd647b3 unverified Apache-2.0 (permissive)
Individual Fairness Guarantees for Neural Networks 11 May 2022 eliasbenussi/nn-cert-individual-fairness/training/SenSR.py 06fe887d57990942 unverified MIT (permissive)
Omni-supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoning 21 May 2021 azuki-miho/RFCR/KPConv_deform_S3DIS/models/network_blocks.py 5a6188c7d118c3fc unverified MIT (permissive)
Weakly Supervised Generative Network for Multiple 3D Human Pose Hypotheses 13 Aug 2020 chaneyddtt/weakly-supervised-3d-pose-generator/layers.py 43d3063aefdee74a unverified MIT (permissive)
JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds 14 Jul 2020 hzykent/JSENet/JSENet_code/models/network_blocks.py 5a6188c7d118c3fc unverified MIT (permissive)
Understanding the Role of Training Regimes in Continual Learning 12 Jun 2020 imirzadeh/stable-continual-learning/external_libs/continual_learning_algorithms/model/model.py 70fae4aed55bdadc unverified MIT (permissive)
Multi-Path Region Mining For Weakly Supervised 3D Semantic Segmentation on Point Clouds 29 Mar 2020 plusmultiply/mprm/models/network_blocks_mprm.py 5a6188c7d118c3fc unverified MIT (permissive)
FoCL: Feature-Oriented Continual Learning for Generative Models 9 Mar 2020 nvcuong/variational-continual-learning/ddm/alg/cla_models_multihead.py 00e7f93923732123 unverified Apache-2.0 (permissive)
D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features 6 Mar 2020 XuyangBai/D3Feat/models/network_blocks.py 5a6188c7d118c3fc unverified MIT (permissive)
Overcoming Long-term Catastrophic Forgetting through Adversarial Neural Pruning and Synaptic Consolidation 19 Dec 2019 GeoX-Lab/ANPyC/Permuted_MNIST/ANPyC_wi_NP.py 61fec95c88135011 unverified MIT (permissive)
Overcoming Long-term Catastrophic Forgetting through Adversarial Neural Pruning and Synaptic Consolidation 19 Dec 2019 GeoX-Lab/ANPyC/Permuted_MNIST/SI.py ee16754dcbf1a272 unverified MIT (permissive)
Overcoming Long-term Catastrophic Forgetting through Adversarial Neural Pruning and Synaptic Consolidation 19 Dec 2019 GeoX-Lab/ANPyC/Permuted_MNIST/ewc/model.py cb07eb141f5648be unverified MIT (permissive)
DropConnect Is Effective in Modeling Uncertainty of Bayesian Deep Networks 7 Jun 2019 hula-ai/mc_dropconnect/classification/ops.py bdbb15cd5c6c501a unverified MIT (permissive)
Heterogeneous Gaussian Mechanism: Preserving Differential Privacy in Deep Learning with Provable Robustness 2 Jun 2019 haiphanNJIT/StoBatch/MNIST/StoBatch.py 2a775c69ec858b1e unverified MIT (permissive)
KPConv: Flexible and Deformable Convolution for Point Clouds 18 Apr 2019 HuguesTHOMAS/KPConv/models/network_blocks.py 5a6188c7d118c3fc unverified MIT (permissive)
Compressing deep neural networks by matrix product operators 11 Apr 2019 zfgao66/deeplearning-mpo/LeNet5/mpo_lenet5/inference.py eb3fe697183c4324 unverified MIT (permissive)
Class-incremental Learning via Deep Model Consolidation 19 Mar 2019 juntingzh/incremental-learning-baselines/model/model.py 70fae4aed55bdadc unverified MIT recorded; this copy not marked cleared · pointer only
Quasi-Newton Methods for Machine Learning: Forget the Past, Just Sample 28 Jan 2019 OptMLGroup/SQN/network.py 242517e581c4f091 unverified MIT (permissive)
Efficient Lifelong Learning with A-GEM 2 Dec 2018 facebookresearch/agem/model/model.py 70fae4aed55bdadc unverified MIT (permissive)
MesoNet: a Compact Facial Video Forgery Detection Network 4 Sep 2018 Raj-08/Deepfake-Detection-Mesonet/ops.py 9c44b685b4a9551a unverified MIT (permissive)
SOM-VAE: Interpretable Discrete Representation Learning on Time Series 6 Jun 2018 ratschlab/SOM-VAE/som_vae/somvae_model.py 27a65c2665dbbd2d unverified MIT (permissive)
Particle Filter Networks with Application to Visual Localization 23 May 2018 AdaCompNUS/pfnet/transformer/tf_utils.py 6e35969a165c8996 unverified MIT (permissive)
Adaptive Federated Learning in Resource Constrained Edge Computing Systems 14 Apr 2018 IBM/adaptive-federated-learning/models/cnn_cifar10.py cb07eb141f5648be unverified MIT (permissive)
Graph Partition Neural Networks for Semi-Supervised Classification 16 Mar 2018 Microsoft/graph-partition-neural-network-samples/gpnn/model/nn_cells.py 3fe890ee11cb4fad unverified MIT (permissive)
Wasserstein GAN 26 Jan 2017 shekkizh/WassersteinGAN.tensorflow/utils.py b1cce189d8a4c490 unverified MIT (permissive)
Semantic Image Inpainting with Deep Generative Models 26 Jul 2016 ahmedabdel-hady/Space-Xplores-Nasaspaceapp2020/remove_noise.py 9c44b685b4a9551a unverified MIT (permissive)
Context Encoders: Feature Learning by Inpainting 25 Apr 2016 shekkizh/TensorflowProjects/MNIST/Uncertainty_modelling.py cb07eb141f5648be unverified MIT (permissive)
End to End Learning for Self-Driving Cars 25 Apr 2016 HarshaVardhanVanama/Autopilot-Steering-Control/model.py cb07eb141f5648be unverified MIT (permissive)
End to End Learning for Self-Driving Cars 25 Apr 2016 Mohamed-ElhajAbdou/Self-driving-car/model.py ac237542eee6cf2f unverified MIT (permissive)
End to End Learning for Self-Driving Cars 25 Apr 2016 SullyChen/Autopilot-TensorFlow/model.py d5ef859d6dcd9a83 unverified MIT (permissive)
Deep Residual Learning for Image Recognition 10 Dec 2015 seishinkikuchi/test/resnet.py 8d26c7a555eb9a68 unverified no licence file found · pointer only
Spatial Transformer Networks 5 Jun 2015 mimikaan/Attention-Model/MNIST/ram/utils.py 9de931a21d52f0d1 unverified MIT (permissive)
U-Net: Convolutional Networks for Biomedical Image Segmentation 18 May 2015 FelixGruen/tensorflow-u-net/architecture/networks.py 45dec85bff64e3e8 unverified GPL-3.0 (copyleft) · pointer only
U-Net: Convolutional Networks for Biomedical Image Segmentation 18 May 2015 YudeWang/UNet-Satellite-Image-Segmentation/factory.py 4797a9cbb59bba3b unverified no licence file found · pointer only
U-Net: Convolutional Networks for Biomedical Image Segmentation 18 May 2015 jakeret/tf_unet/tf_unet/unet.py 72aea187ac2125bf unverified GPL-3.0 (copyleft) · pointer only
U-Net: Convolutional Networks for Biomedical Image Segmentation 18 May 2015 jerichooconnell/tf_unet/tf_unet/unet.py f5e4d4c31d5349ce unverified GPL-3.0 (copyleft) · pointer only
U-Net: Convolutional Networks for Biomedical Image Segmentation 18 May 2015 tanyanair/segmentation_uncertainty/bunet/models/bunet.py 48ba93f55d8e9985 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".

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