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cal_loss

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

cal_loss appears in the code Syntology harvested for 20 papers, as 12 distinct code bodies found in 20 places (a place is one code body under one paper). At least one of them ran in 16 of the papers; 1 of the code bodies carries 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 cal_loss 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 8 of the 12 distinct code bodies named cal_loss; 4 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
2ran · our draft was wrong
3ran · fixture could not drive it
3ran
4unverified
1fingerprinted

Licence is a property of each copy, so it is counted per place: 5 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, and the graph's for 1 papers added by Syntology; 2 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
Not All Timesteps Matter Equally: Selective Alignment Knowledge Distillation for Spiking Neural Networks added by Syntology 2026-05 (from id) identical code first harvested elsewhere f823c61767cb0e17 ran · our draft was wrong licence of this copy not recorded
Improving Generalization of Universal Adversarial Perturbation via Dynamic Maximin Optimization 17 Mar 2025 yechao-zhang/dm-uap/attack_min_x_theta_adam.py 5976d753d29a1de7 ran MIT (permissive)
Efficient Logit-based Knowledge Distillation of Deep Spiking Neural Networks for Full-Range Timestep Deployment 27 Jan 2025 intelli-chip-lab/snn_temporal_decoupling_distillation/model/layer.py f823c61767cb0e17 ran · our draft was wrong no licence file found · pointer only
Over-parameterized Student Model via Tensor Decomposition Boosted Knowledge Distillation 10 Nov 2024 intell-sci-comput/OPDF/LGTM/utils_glue.py 940dcf82641060b8 ran · our draft was wrong fingerprinted MIT (permissive)
Rethinking Efficient and Effective Point-based Networks for Event Camera Classification and Regression: EventMamba 9 May 2024 rhwxmx/eventmamba/train_classification.py ca8b05abe9056f68 ran · fixture could not drive it MIT (permissive)
Hide in Thicket: Generating Imperceptible and Rational Adversarial Perturbations on 3D Point Clouds 8 Mar 2024 TRLou/HiT-ADV/model/pct_utils.py c6c2758c0c2fa756 ran · fixture could not drive it no licence file found · pointer only
Towards Efficient Communication and Secure Federated Recommendation System via Low-rank Training 8 Jan 2024 nnhieu/colr-fedrec/rec/evaluate.py fee086990f3343e2 ran no licence file found · pointer only
Sample-adaptive Augmentation for Point Cloud Recognition Against Real-world Corruptions 19 Sep 2023 roywangj/adaptpoint/openpoints/function_adaptpoint/ganloss_cls.py 3b189aa530881663 ran MIT (permissive)
Tailoring Instructions to Student's Learning Levels Boosts Knowledge Distillation 16 May 2023 twinkle0331/lgtm/utils_glue.py 940dcf82641060b8 ran · our draft was wrong fingerprinted Apache-2.0 (permissive)
TetraSphere: A Neural Descriptor for O(3)-Invariant Point Cloud Analysis 26 Nov 2022 pavlo-melnyk/tetrasphere/tetrasphere/utils.py c6c2758c0c2fa756 ran · fixture could not drive it MIT (permissive)
PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learning 21 Nov 2022 yangyangyang127/pointclip_v2/zeroshot_seg/util.py c6c2758c0c2fa756 ran · fixture could not drive it MIT (permissive)
Class-Level Confidence Based 3D Semi-Supervised Learning 18 Oct 2022 AutoAILab/Confid-SSL/util.py c6c2758c0c2fa756 ran · fixture could not drive it MIT (permissive)
Shape-invariant 3D Adversarial Point Clouds 8 Mar 2022 shikiw/SI-Adv/model_utils/pct_util.py c6c2758c0c2fa756 ran · fixture could not drive it MIT (permissive)
Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions 28 Jan 2022 jiachens/ModelNet40-C/pointMLP/classification_ModelNet40/helper.py c6c2758c0c2fa756 ran · fixture could not drive it BSD-3-Clause (permissive)
GANSeg: Learning to Segment by Unsupervised Hierarchical Image Generation 2 Dec 2021 xingzhehe/ganseg/train_seg.py 4bae234fe244fb49 unverified no licence file found · pointer only
PointCutMix: Regularization Strategy for Point Cloud Classification 5 Jan 2021 cuge1995/PointCutMix/train_pointcutmix_k.py c6c2758c0c2fa756 ran · fixture could not drive it MIT (permissive)
Rotation Invariant Point Cloud Classification: Where Local Geometry Meets Global Topology 1 Nov 2019 sailor-z/LGR-Net/Network.py 8d5da11417260382 ran · fixture could not drive it MIT (permissive)
Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding 9 Nov 2015 hosshonarvar/Image-Segmentation/others_code/evaluation_object.py 00a137eeb688bbf2 unverified MIT (permissive)
arXiv:2025.naacl-long.61 vonfeng/DPLink/codes/utils.py 64a028b2d831b7b1 unverified MIT (permissive)
arXiv:136630375 mmmmimic/diffConvNet/misc.py a0892f39f9c7e229 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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