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loss_func

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

loss_func appears in the code Syntology harvested for 26 papers, as 26 distinct code bodies found in 28 places (a place is one code body under one paper). At least one of them ran in 17 of the papers; 5 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 loss_func 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 17 of the 26 distinct code bodies named loss_func; 9 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

2ran · honoured contract
1ran · violated contract
3ran · our draft was wrong
3ran · fixture could not drive it
8ran
9unverified
5fingerprinted

Licence is a property of each copy, so it is counted per place: 14 of the 28 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

26 papers shown of 26, newest first; 28 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; 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
Trojaning the Alignment: Stealthy Backdoor Attacks against Graph Foundation Models added by Syntology 2026-08 (from id) ventr1c/STAG/graphclip/model/reconstruct.py 8460148151bb97d8 ran Apache-2.0 (permissive)
Revisiting K-mer Profile for Effective and Scalable Genome Representation Learning 4 Nov 2024 abdcelikkanat/revisitingkmers/src/nonlinear.py b8415a5ca4e28ff0 ran · fixture could not drive it no licence file found · pointer only
Read-ME: Refactorizing LLMs as Router-Decoupled Mixture of Experts with System Co-Design 24 Oct 2024 vita-group/read-me/moe_fication/train_red.py c2877c234b2d0e3f ran · our draft was wrong no licence file found · pointer only
HLM-Cite: Hybrid Language Model Workflow for Text-based Scientific Citation Prediction 10 Oct 2024 tsinghua-fib-lab/H-LM/code/model_train/functions.py df4f39f6cba1abae ran no licence file found · pointer only
IterComp: Iterative Composition-Aware Feedback Learning from Model Gallery for Text-to-Image Generation 9 Oct 2024 YangLing0818/IterComp/train/train_reward_models.py 1164a205d991f3d8 ran · our draft was wrong fingerprinted MIT (permissive)
Towards Unified Multimodal Editing with Enhanced Knowledge Collaboration 30 Sep 2024 beepkh/unike/easyeditor/models/unike/unike_main.py 7ba48886f3a5f906 ran · fixture could not drive it no licence file found · pointer only
Generative Semi-supervised Graph Anomaly Detection 19 Feb 2024 mala-lab/GGAD/ocgnn.py 7290fd3be1378160 unverified no licence file found · pointer only
Deep adaptive sampling for surrogate modeling without labeled data 17 Feb 2024 MJfadeaway/DAS-2/Lid-driven_cavity_flow/das_train.py c8171b470687f787 ran Apache-2.0 (permissive)
Deep adaptive sampling for surrogate modeling without labeled data 17 Feb 2024 MJfadeaway/DAS-2/Operator_learning/das_oplearning.py feabd500549c048c ran Apache-2.0 (permissive)
Deep adaptive sampling for surrogate modeling without labeled data 17 Feb 2024 MJfadeaway/DAS-2/Lid-driven_cavity_flow/das_fixed.py 2767233d06d2a32a unverified Apache-2.0 (permissive)
Two Tales of Single-Phase Contrastive Hebbian Learning 13 Feb 2024 Rasmuskh/dualprop_icml_2024/config/cli_config.py 8a9389d40f435482 unverified no licence file found · pointer only
The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents 5 Feb 2024 idephics/benefit-reusing-batch/simulations_PyTorch.py 7f257a6a2b02c83f ran fingerprinted no licence file found · pointer only
Robust Estimation of Causal Heteroscedastic Noise Models 15 Dec 2023 quangdzuytran/ROCHE/causa/roche.py 4a43f04570f2e3bd ran MIT (permissive)
Label-based Graph Augmentation with Metapath for Graph Anomaly Detection 21 Aug 2023 missinghwan/MSAD/random_search.py 461ba2662d434a33 ran no licence file found · pointer only
How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model 5 Jul 2023 pcsl-epfl/hierarchy-learning/optim_loss.py c23da6206919edb5 ran MIT (permissive)
A Neural Collapse Perspective on Feature Evolution in Graph Neural Networks 4 Jul 2023 kvignesh1420/gnn_collapse/gufm.py 16d68f9aa5f47c6b unverified Apache-2.0 (permissive)
Graph-level Anomaly Detection via Hierarchical Memory Networks 3 Jul 2023 niuchx/himnet/loss.py 00f227f6ee7015cb unverified MIT (permissive)
Transformer-Patcher: One Mistake worth One Neuron 24 Jan 2023 ZeroYuHuang/Transformer-Patcher/src/models/patch.py 3ef32b5700dabe8c ran · fixture could not drive it no licence file found · pointer only
Self-Supervised Learning via Maximum Entropy Coding 20 Oct 2022 xinliu20/mec/main_pretrain.py 2d6e7120fd354e50 unverified MIT (permissive)
Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning 14 Jun 2022 jmaces/aapm-ct-challenge/aapm-ct/script_train_ddnet_inv.py bd9b4f62ae1d8205 ran · honoured contract fingerprinted MIT (permissive)
Encoding physics to learn reaction-diffusion processes 9 Jun 2021 Raocp/PeRCNN/3d_gs_rd/train_3drd.py 32af45a8240c8744 unverified no licence file found · pointer only
TeraPipe: Token-Level Pipeline Parallelism for Training Large-Scale Language Models 16 Feb 2021 zhuohan123/terapipe/terapipe.py 49e2a4eb90f7b049 ran · our draft was wrong fingerprinted no licence file found · pointer only
The troublesome kernel -- On hallucinations, no free lunches and the accuracy-stability trade-off in inverse problems 5 Jan 2020 vegarant/troublesome_kernel/ellipses/script_train_fourier_unet_it_jit-nojit.py bd9b4f62ae1d8205 ran · honoured contract fingerprinted no licence file found · pointer only
Temporal-Coded Deep Spiking Neural Network with Easy Training and Robust Performance 24 Sep 2019 zbs881314/Temporal-Coded-Deep-SNN/MNIST/SNN.py 5d997d1e25178db8 unverified MIT (permissive)
Unsupervised Learning from Video with Deep Neural Embeddings 28 May 2019 neuroailab/VIE/tf_model/train_vie.py 56be0c434fce98a7 ran · honoured contract no licence file found · pointer only
Quantum autoencoders for efficient compression of quantum data 2016-12 (from id) theodoradragan/QuantumAutoencoder/utils.py a0cb57b5b394f6f4 ran · violated contract fingerprinted no licence file found · pointer only
Supervised learning based on temporal coding in spiking neural networks 27 Jun 2016 TianjianCai/SNN/SNN.py 5d997d1e25178db8 unverified MIT (permissive)
arXiv:Yu_SSHNet_Unsupervised_Cross-modal_Homography_Estimation_via_Problem_Reformulation_and_Split_CVPR_2025_paper Junchen-Yu/SSHNet/utils/loss.py cbc8d3baf37188e5 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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