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bias_variable

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

bias_variable appears in the code Syntology harvested for 24 papers, as 17 distinct code bodies found in 30 places (a place is one code body under one paper). At least one of them ran in 6 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 bias_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 5 of the 17 distinct code bodies named bias_variable; 12 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
5ran · our draft was wrong
0ran · fixture could not drive it
0ran
12unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 5 of the 30 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

24 papers shown of 24, newest first; 30 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; 1 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 Flexible Body Collision Dynamics with Hierarchical Contact Mesh Transformer 19 Dec 2023 google-deepmind/deepmind-research/alphafold_casp13/two_dim_convnet.py 3bf8634c88f6bebc unverified Apache-2.0 (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 6a88f1ae24fa26b2 unverified MIT (permissive)
Weakly Supervised Generative Network for Multiple 3D Human Pose Hypotheses 13 Aug 2020 chaneyddtt/weakly-supervised-3d-pose-generator/layers.py 9fdd8216a2990a2b 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 6a88f1ae24fa26b2 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 a96da1b71ad1a9ef 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 6a88f1ae24fa26b2 unverified MIT (permissive)
FoCL: Feature-Oriented Continual Learning for Generative Models 9 Mar 2020 nvcuong/variational-continual-learning/ddm/alg/cla_models_multihead.py 5d78e1f7a1afb766 ran · our draft was wrong 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 6a88f1ae24fa26b2 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 5d78e1f7a1afb766 ran · our draft was wrong 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 58a8b70ac32deb13 unverified MIT (permissive)
DropConnect Is Effective in Modeling Uncertainty of Bayesian Deep Networks 7 Jun 2019 hula-ai/mc_dropconnect/classification/ops.py 9fdd8216a2990a2b unverified MIT (permissive)
Heterogeneous Gaussian Mechanism: Preserving Differential Privacy in Deep Learning with Provable Robustness 2 Jun 2019 haiphanNJIT/StoBatch/MNIST/StoBatch.py aafb9bf91f6ef802 unverified MIT (permissive)
KPConv: Flexible and Deformable Convolution for Point Clouds 18 Apr 2019 HuguesTHOMAS/KPConv/models/network_blocks.py 6a88f1ae24fa26b2 unverified MIT (permissive)
Class-incremental Learning via Deep Model Consolidation 19 Mar 2019 juntingzh/incremental-learning-baselines/model/model.py a96da1b71ad1a9ef unverified MIT recorded; this copy not marked cleared · pointer only
Efficient Lifelong Learning with A-GEM 2 Dec 2018 facebookresearch/agem/model/model.py a96da1b71ad1a9ef unverified MIT (permissive)
MesoNet: a Compact Facial Video Forgery Detection Network 4 Sep 2018 Raj-08/Deepfake-Detection-Mesonet/ops.py d76e69553bdf6bda unverified MIT (permissive)
SOM-VAE: Interpretable Discrete Representation Learning on Time Series 6 Jun 2018 ratschlab/SOM-VAE/som_vae/somvae_model.py 217082b54273942a unverified MIT (permissive)
Adaptive Federated Learning in Resource Constrained Edge Computing Systems 14 Apr 2018 IBM/adaptive-federated-learning/models/cnn_cifar10.py 5d78e1f7a1afb766 ran · our draft was wrong MIT (permissive)
End-to-end Driving via Conditional Imitation Learning 6 Oct 2017 carla-simulator/imitation-learning/agents/imitation/imitation_learning_network.py a9219407ace66492 unverified MIT (permissive)
Semantic Image Inpainting with Deep Generative Models 26 Jul 2016 ahmedabdel-hady/Space-Xplores-Nasaspaceapp2020/remove_noise.py d76e69553bdf6bda unverified MIT (permissive)
Context Encoders: Feature Learning by Inpainting 25 Apr 2016 shekkizh/TensorflowProjects/MNIST/Uncertainty_modelling.py 5d78e1f7a1afb766 ran · our draft was wrong MIT (permissive)
End to End Learning for Self-Driving Cars 25 Apr 2016 HarshaVardhanVanama/Autopilot-Steering-Control/model.py 5d78e1f7a1afb766 ran · our draft was wrong MIT (permissive)
End to End Learning for Self-Driving Cars 25 Apr 2016 SullyChen/Autopilot-TensorFlow/model.py 993d22ff0d2b0b3d unverified MIT (permissive)
Spatial Transformer Networks 5 Jun 2015 mimikaan/Attention-Model/MNIST/ram/utils.py 4c60a7a3b18a04ae unverified MIT (permissive)
U-Net: Convolutional Networks for Biomedical Image Segmentation 18 May 2015 FelixGruen/tensorflow-u-net/architecture/networks.py ebc72e459cc54be3 ran · our draft was wrong GPL-3.0 (copyleft) · pointer only
U-Net: Convolutional Networks for Biomedical Image Segmentation 18 May 2015 YudeWang/UNet-Satellite-Image-Segmentation/factory.py a4408c7d23c6fd24 ran · our draft was wrong no licence file found · pointer only
U-Net: Convolutional Networks for Biomedical Image Segmentation 18 May 2015 jakeret/tf_unet/tf_unet/unet.py 9c7c4be45da4ca84 ran · our draft was wrong GPL-3.0 (copyleft) · pointer only
U-Net: Convolutional Networks for Biomedical Image Segmentation 18 May 2015 jerichooconnell/tf_unet/tf_unet/unet.py 5d78e1f7a1afb766 ran · our draft was wrong GPL-3.0 (copyleft) · pointer only
U-Net: Convolutional Networks for Biomedical Image Segmentation 18 May 2015 tanyanair/segmentation_uncertainty/bunet/models/bunet.py 3dd616e5541b49c0 ran · our draft was wrong MIT (permissive)
arXiv:2021.naacl-main.222 microsoft/flin-nl2web/code/nsm_model/Neural_Semantic_Matcher.py 1ca788417568cac1 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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