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get_initializer

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

get_initializer appears in the code Syntology harvested for 32 papers, as 23 distinct code bodies found in 34 places (a place is one code body under one paper). At least one of them ran in 21 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_initializer 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 12 of the 23 distinct code bodies named get_initializer; 11 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
10ran · our draft was wrong
0ran · fixture could not drive it
2ran
11unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 8 of the 34 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

32 papers shown of 32, newest first; 34 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
SpectraFM: Tuning into Stellar Foundation Models 2024-11 (from id) NolanKoblischke/SpectraFM_NeurIPS_FM4Science/model_core/stellarperceptron/layers.py b76ac4984902fcc3 unverified no licence file found · pointer only
Estimating Probability Densities with Transformer and Denoising Diffusion 22 Jul 2024 henrysky/stars_foundation_diffusion/stellarperceptron/layers.py 181136b0f2ef71bb ran MIT (permissive)
Intervention-Aware Forecasting: Breaking Historical Limits from a System Perspective 22 May 2024 vewoxic/fits/layers/mwt.py c12f08051ec5fa06 ran Apache-2.0 (permissive)
The Rise of Diffusion Models in Time-Series Forecasting 5 Jan 2024 ai4healthuol/sssd/src/imputers/CSDIS4.py 49d9214ec79a8edb ran · our draft was wrong MIT (permissive)
Towards an astronomical foundation model for stars with a Transformer-based model 2023-08 (from id) henrysky/astronn_stars_foundation/stellarperceptron/layers.py b76ac4984902fcc3 unverified MIT (permissive)
All Points Matter: Entropy-Regularized Distribution Alignment for Weakly-supervised 3D Segmentation 25 May 2023 LiyaoTang/ERDA/models/head.py b9980bfcdc9fa508 ran · our draft was wrong MIT (permissive)
Diffusion-based Conditional ECG Generation with Structured State Space Models 19 Jan 2023 ai4healthuol/sssd-ecg/src/sssd/models/S4Model.py 49d9214ec79a8edb ran · our draft was wrong MIT (permissive)
Robust Speech Recognition via Large-Scale Weak Supervision 6 Dec 2022 robflynnyh/long-context-asr/lcasr/components/long_conv.py ee3c8d2a605ac8bc unverified Apache-2.0 (permissive)
Simplifying and Understanding State Space Models with Diagonal Linear RNNs 1 Dec 2022 identical code first harvested elsewhere f3449af8fae1d130 ran · our draft was wrong licence of this copy not recorded
What Makes Convolutional Models Great on Long Sequence Modeling? 17 Oct 2022 ctlllll/sgconv/gconv_standalone.py 49d9214ec79a8edb ran · our draft was wrong no licence file found · pointer only
FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting 18 May 2022 tianzhou2011/FiLM/layers/S4.py f3449af8fae1d130 ran · our draft was wrong MIT (permissive)
FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting 18 May 2022 tianzhou2011/FiLM/layers/mwt.py c12f08051ec5fa06 ran MIT (permissive)
Long Movie Clip Classification with State-Space Video Models 4 Apr 2022 identical code first harvested elsewhere 49d9214ec79a8edb ran · our draft was wrong licence of this copy not recorded
Diagonal State Spaces are as Effective as Structured State Spaces 27 Mar 2022 ag1988/dss/src/models/sequence/ss/standalone/dss.py f3449af8fae1d130 ran · our draft was wrong Apache-2.0 (permissive)
It's Raw! Audio Generation with State-Space Models 20 Feb 2022 identical code first harvested elsewhere f3449af8fae1d130 ran · our draft was wrong licence of this copy not recorded
Efficiently Modeling Long Sequences with Structured State Spaces 31 Oct 2021 identical code first harvested elsewhere f3449af8fae1d130 ran · our draft was wrong licence of this copy not recorded
Multiwavelet-based Operator Learning for Differential Equations 28 Sep 2021 gaurav71531/mwt-operator/tests/test_NS_MWT_N_1000.py 3896da4413c00301 ran · our draft was wrong no licence file found · pointer only
HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis 12 Oct 2020 TensorSpeech/TensorflowTTS/tensorflow_tts/models/hifigan.py e3ecb51f94828d3b ran · our draft was wrong Apache-2.0 (permissive)
HiPPO: Recurrent Memory with Optimal Polynomial Projections 17 Aug 2020 identical code first harvested elsewhere f3449af8fae1d130 ran · our draft was wrong licence of this copy not recorded
HiPPO: Recurrent Memory with Optimal Polynomial Projections 17 Aug 2020 HazyResearch/hippo-code/model/components.py 261aab6fe51ea01d unverified Apache-2.0 (permissive)
Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors 15 Jun 2020 rohanshenoy/qkeras/qkeras/qlayers.py 0933ca9566b5853e unverified Apache-2.0 (permissive)
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech 8 Jun 2020 dathudeptrai/TensorflowTTS/tensorflow_tts/models/fastspeech2.py dc0497b8e8f5b178 ran · our draft was wrong Apache-2.0 (permissive)
Transferring Inductive Biases through Knowledge Distillation 31 May 2020 samiraabnar/Reflect/tf2_models/common_layers.py 488a1d723fa95280 unverified MIT (permissive)
DeFormer: Decomposing Pre-trained Transformers for Faster Question Answering 2 May 2020 StonyBrookNLP/deformer/models/layers/transformer.py 4f59e68b4035cf8a ran · our draft was wrong MIT (permissive)
TAPAS: Weakly Supervised Table Parsing via Pre-training 5 Apr 2020 kamalkraj/TAPAS-TF2/tapas/models/modeling.py d05bac24655b6e92 unverified Apache-2.0 (permissive)
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter 2 Oct 2019 mkavim/finetune_bert/finetune/modeling_distilbert.py 333abb7d28cd1f9f ran · our draft was wrong Apache-2.0 (permissive)
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations 26 Sep 2019 kamalkraj/ALBERT-TF2.0/albert.py 591598c6e9e9294a ran · our draft was wrong Apache-2.0 (permissive)
Question Generation by Transformers 9 Sep 2019 artitw/BERT_QA/bert_qa/bert_modeling.py 591598c6e9e9294a ran · our draft was wrong Apache-2.0 (permissive)
Differentiable Product Quantization for End-to-End Embedding Compression 26 Aug 2019 chentingpc/dpq_embedding_compression/nmt/model_helper.py a37117218aee8b75 unverified MIT (permissive)
A Signal Propagation Perspective for Pruning Neural Networks at Initialization 14 Jun 2019 namhoonlee/spp-public/spp/network.py b532f60f30000832 unverified MIT (permissive)
A Style-Based Generator Architecture for Generative Adversarial Networks 12 Dec 2018 mgmk2/StyleGAN/src/model/stylegan/network.py 971808e87a2c4936 ran · our draft was wrong Apache-2.0 (permissive)
Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling 3 Apr 2018 taoshen58/BiBloSA/context_fusion/general.py e7841940b013046c unverified Apache-2.0 (permissive)
Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs 15 Dec 2016 solgaardlab/neurophox/neurophox/initializers.py d958ae5b8ac823b4 unverified MIT (permissive)
arXiv:2021.emnlp-main.154 Hazelsuko07/TextHide/transformers_hide/modeling_tf_utils.py 99fba512df728841 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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