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placeholder_inputs

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

placeholder_inputs appears in the code Syntology harvested for 17 papers, as 23 distinct code bodies found in 26 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 placeholder_inputs 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 23 distinct code bodies named placeholder_inputs; 23 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
23unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 0 of the 26 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

17 papers shown of 17, newest first; 26 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
SkeletonNet: A Topology-Preserving Solution for Learning Mesh Reconstruction of Object Surfaces from RGB Images 13 Aug 2020 tangjiapeng/SkeletonNet/SkeDISN/cam_est/model_cam.py 9a91699ee2fe9841 unverified MIT (permissive)
SkeletonNet: A Topology-Preserving Solution for Learning Mesh Reconstruction of Object Surfaces from RGB Images 13 Aug 2020 tangjiapeng/SkeletonNet/SkeDISN/models/model_add_skevox.py 7cacff7bd55a7cc6 unverified MIT (permissive)
SkeletonNet: A Topology-Preserving Solution for Learning Mesh Reconstruction of Object Surfaces from RGB Images 13 Aug 2020 tangjiapeng/SkeletonNet/SkeDISN/models/model_add_skevox_multiview.py e8a74ed2dd506d62 unverified MIT (permissive)
SkeletonNet: A Topology-Preserving Solution for Learning Mesh Reconstruction of Object Surfaces from RGB Images 13 Aug 2020 tangjiapeng/SkeletonNet/SkeDISN/models/model_normalization.py bac3c27707f4b8e1 unverified MIT (permissive)
RPM-Net: Recurrent Prediction of Motion and Parts from Point Cloud 26 Jun 2020 Salingo/RPM-Net/code/model_rpm.py 9240c7268167d920 unverified MIT (permissive)
Detailed 2D-3D Joint Representation for Human-Object Interaction 17 Apr 2020 DirtyHarryLYL/DJ-RN/Feature_extraction/pointnet_hico.py f4d4327d825f56f5 unverified Apache-2.0 (permissive)
Clean-Label Backdoor Attacks on Video Recognition Models 6 Mar 2020 ShihaoZhaoZSH/Video-Backdoor-Attack/utils.py 3a790fcc84f46286 unverified Apache-2.0 (permissive)
PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling 1 Mar 2020 yanx27/PointASNL/models/pointasnl_cls.py 336713a309d1bdf0 unverified MIT (permissive)
PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling 1 Mar 2020 yanx27/PointASNL/models/pointasnl_sem_seg.py 109643a8ca7cd438 unverified MIT (permissive)
ABCNet: An attention-based method for particle tagging 2020-01 (from id) ViniciusMikuni/ABCNet/models/gapnet_PU.py 891a58684657e6f5 unverified MIT (permissive)
ABCNet: An attention-based method for particle tagging 2020-01 (from id) ViniciusMikuni/ABCNet/models/gapnet_QG.py c4f3a3da4a34cc0a unverified MIT (permissive)
Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds 20 Sep 2019 hlei-ziyan/SPH3D-GCN/modelnet40_cls/evaluate_modelnet.py 97381bcd3d858fe2 unverified MIT (permissive)
Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds 20 Sep 2019 zb12138/sph3dR/modelnet40_cls/evaluate_modelnet.py 3db6c8e24ce42b3c unverified MIT (permissive)
Rotation Invariant Convolutions for 3D Point Clouds Deep Learning 17 Aug 2019 hkust-vgd/riconv/RIConv.py d00422a0bd6befee unverified MIT (permissive)
Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features 22 Apr 2019 KuangenZhang/ldgcnn/models/ldgcnn.py 223ffcb80e41a2b4 unverified MIT (permissive)
Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features 22 Apr 2019 KuangenZhang/ldgcnn/models/ldgcnn_classifier.py b9d15d9626a4c7c4 unverified MIT (permissive)
Path-Invariant Map Networks 31 Dec 2018 zaiweizhang/path_invariance_map_network/pointnet2_sem_seg.py 0d4c28990da51518 unverified BSD-3-Clause (permissive)
Path-Invariant Map Networks 31 Dec 2018 zaiweizhang/path_invariance_map_network/pointnet2_sem_seg_voxel.py 96e3a1a94a60fb90 unverified BSD-3-Clause (permissive)
TextureNet: Consistent Local Parametrizations for Learning from High-Resolution Signals on Meshes 30 Nov 2018 hjwdzh/TextureNet/src/models/texturenet.py d7c375f4f920af84 unverified MIT (permissive)
Generating 3D Adversarial Point Clouds 19 Sep 2018 xiangchong1/3d-adv-pc/models/pointnet_cls.py 223ffcb80e41a2b4 unverified MIT (permissive)
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters 30 Mar 2018 xyf513/SpiderCNN/models/spidercnn_part_seg_one_hot.py 68dc0303fa0b7ba8 unverified MIT (permissive)
Local Spectral Graph Convolution for Point Set Feature Learning 15 Mar 2018 fate3439/LocalSpecGCN/part_seg/models/pointnet2_part_ssg_spec_cp_onehot.py 68dc0303fa0b7ba8 unverified MIT (permissive)
Local Spectral Graph Convolution for Point Set Feature Learning 15 Mar 2018 fate3439/LocalSpecGCN/classification/models/pointnet2_cls_ssg.py 654734421947670b unverified MIT (permissive)
arXiv:aaai_6994 dlinzhao/JSNet/models/JISS/model.py 83d1a754b154c5a7 unverified MIT (permissive)
arXiv:Wang_PWCLO-Net_Deep_LiDAR_Odometry_in_3D_Point_Clouds_Using_Hierarchical_CVPR_2021_paper IRMVLab/PWCLONet/PWCLO_Net.py 779b4209b47d4435 unverified MIT (permissive)
arXiv:136620053 Tianxinhuang/PCDNet/pointnet_cls.py 223ffcb80e41a2b4 unverified Apache-2.0 (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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