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get_normalization

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

get_normalization appears in the code Syntology harvested for 25 papers, as 13 distinct code bodies found in 25 places (a place is one code body under one paper). At least one of them ran in 3 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 get_normalization 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 3 of the 13 distinct code bodies named get_normalization; 10 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
0ran · fixture could not drive it
1ran
10unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 8 of the 25 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

25 papers shown of 25, newest first; 25 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 4 papers added by Syntology. 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
KVAE: Family of Tokenizers for Multimodal Generative Models added by Syntology 2026-08 (from id) kandinskylab/kvae/kvae/layers/norm.py f2d28f519435983f unverified MIT (permissive)
Towards Robust Zero-Shot Reinforcement Learning added by Syntology 2025-10 (from id) seohongpark/HILP/hilp_gcrl/src/d4rl_utils.py e60bfdd0574ddc5f unverified no licence file found · pointer only
CuMPerLay: Learning Cubical Multiparameter Persistence Vectorizations added by Syntology 2025-10 (from id) circle-group/cumperlay/lmp/nn/modules/blocks.py ee251c006ca33abd unverified MIT (permissive)
HazeFlow: Revisit Haze Physical Model as ODE and Non-Homogeneous Haze Generation for Real-World Dehazing added by Syntology 2025-09 (from id) cloor/HazeFlow/models/normalization.py 1b8adb3294e53c6d unverified MIT (permissive)
ZAPBench: A Benchmark for Whole-Brain Activity Prediction in Zebrafish 4 Mar 2025 google-research/zapbench/zapbench/models/nunet.py a0d57de50a77a653 unverified Apache-2.0 (permissive)
Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training 13 Dec 2024 uzn36/dp-syngen/model/normalization.py 1b8adb3294e53c6d unverified no licence file found · pointer only
FlowPolicy: Enabling Fast and Robust 3D Flow-based Policy via Consistency Flow Matching for Robot Manipulation 2024-12 (from id) zql-kk/flowpolicy/FlowPolicy/flow_policy_3d/models/normalization.py 1b8adb3294e53c6d unverified MIT (permissive)
SlimFlow: Training Smaller One-Step Diffusion Models with Rectified Flow 17 Jul 2024 yuanzhi-zhu/SlimFlow/models/normalization.py 1b8adb3294e53c6d unverified no licence file found · pointer only
Deep Diffusion Image Prior for Efficient OOD Adaptation in 3D Inverse Problems 15 Jul 2024 hj-harry/ddip3d/models/normalization.py 1b8adb3294e53c6d unverified no licence file found · pointer only
PROUD: PaRetO-gUided Diffusion Model for Multi-objective Generation 5 Jul 2024 EvaFlower/Pareto-guided-diffusion-model/CIFAR10/models/normalization.py a1389a38edfc6029 unverified no licence file found · pointer only
Consistency Flow Matching: Defining Straight Flows with Velocity Consistency 2 Jul 2024 yangling0818/consistency_flow_matching/models/normalization.py 1b8adb3294e53c6d unverified MIT (permissive)
Flow Priors for Linear Inverse Problems via Iterative Corrupted Trajectory Matching 29 May 2024 YasminZhang/ICTM/models/normalization.py 1b8adb3294e53c6d unverified MIT (permissive)
Uncertainty quantification for data-driven weather models 20 Mar 2024 cbuelt/dduq/utils/utils.py 61c6be889b9a539d ran MIT (permissive)
Foundation Policies with Hilbert Representations 23 Feb 2024 seohongpark/hilp/hilp_gcrl/src/d4rl_utils.py e60bfdd0574ddc5f unverified no licence file found · pointer only
HIQL: Offline Goal-Conditioned RL with Latent States as Actions 22 Jul 2023 seohongpark/hiql/src/d4rl_utils.py e60bfdd0574ddc5f unverified MIT (permissive)
DPM-OT: A New Diffusion Probabilistic Model Based on Optimal Transport 21 Jul 2023 cognaclee/dpm-ot/models/normalization.py a1389a38edfc6029 unverified no licence file found · pointer only
Reinforcement Learning from Passive Data via Latent Intentions 10 Apr 2023 dibyaghosh/icvf_release/icvf_envs/antmaze/d4rl_utils.py e60bfdd0574ddc5f unverified MIT (permissive)
Region-Conditioned Orthogonal 3D U-Net for Weather4Cast Competition 5 Dec 2022 hyeonjeong1/22-neurips-competition-baseline/models/baseline_UNET3D_bottleneck.py e90f1b8757aa4598 ran · our draft was wrong no licence file found · pointer only
gDDIM: Generalized denoising diffusion implicit models 11 Jun 2022 qsh-zh/gDDIM/blur_jax/models/normalization.py bc1b60776e93f98c unverified Apache-2.0 (permissive)
Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation 10 Jun 2021 Kim-Dongjun/Soft-Truncation/models/normalization.py 1b8adb3294e53c6d unverified Apache-2.0 (permissive)
SNIPS: Solving Noisy Inverse Problems Stochastically 31 May 2021 bahjat-kawar/snips_torch/runners/ncsn_runner.py 1dc9e4e73ba16aac unverified MIT (permissive)
Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs 11 Mar 2020 Daulbaev/IRDM/experiments/classification/train_cifar.py 092979912309090a ran · our draft was wrong MIT (permissive)
Generative Modeling by Estimating Gradients of the Data Distribution 12 Jul 2019 ermongroup/ncsnv2/models/ncsnv2.py c0db9b95b0194d7a unverified MIT (permissive)
On Output Activation Functions for Adversarial Losses: A Theoretical Analysis via Variational Divergence Minimization and An Empirical Study on MNIST Classification 25 Jan 2019 salu133445/dan/src/dan/presets/ops.py 6c50317414bf1d88 unverified MIT (permissive)
Convolutional Generative Adversarial Networks with Binary Neurons for Polyphonic Music Generation 25 Apr 2018 salu133445/musegan/src/musegan/presets/ops.py 6c50317414bf1d88 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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