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embedded_dropout

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

embedded_dropout appears in the code Syntology harvested for 16 papers, as 9 distinct code bodies found in 18 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 embedded_dropout 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 9 distinct code bodies named embedded_dropout; 9 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
9unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 2 of the 18 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

16 papers shown of 16, newest first; 18 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
Consistency Deep Equilibrium Models added by Syntology 2026-02 (from id) landrarwolf/CDEQ/CDEQ-src/lib/optimizations.py df1c4e8a21c5f2ef unverified no licence file found · pointer only
Certified Robustness for Deep Equilibrium Models via Serialized Random Smoothing 1 Nov 2024 WeizhiGao/Serialized-Randomized-Smoothing/DEQ/lib/optimizations.py df1c4e8a21c5f2ef unverified MIT (permissive)
Support or Refute: Analyzing the Stance of Evidence to Detect Out-of-Context Mis- and Disinformation 3 Nov 2023 yx3266/SEN/training_and_evaluation/sent_emb/model.py 62d148258bdd054f unverified no licence file found · pointer only
Dependency-based Mixture Language Models 19 Mar 2022 fadedcosine/dependency-guided-neural-text-generation/DPLM-LSTM/embed_regularize.py 62d148258bdd054f unverified Apache-2.0 (permissive)
Compositional Demographic Word Embeddings 6 Oct 2020 salesforce/awd-lstm-lm/embed_regularize.py 62d148258bdd054f unverified BSD-3-Clause (permissive)
NAS-Bench-NLP: Neural Architecture Search Benchmark for Natural Language Processing 12 Jun 2020 fmsnew/nas-bench-nlp-release/embed_regularize.py 62d148258bdd054f unverified Apache-2.0 (permissive)
Why gradient clipping accelerates training: A theoretical justification for adaptivity 28 May 2019 JingzhaoZhang/why-clipping-accelerates/embed_regularize.py 62d148258bdd054f unverified BSD-3-Clause (permissive)
Reversible Recurrent Neural Networks 25 Oct 2018 matthewjmackay/reversible-rnn/language_modelling/embed_regularize.py 4ed5e00e94ee3740 unverified MIT (permissive)
Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks 22 Oct 2018 IanTheColder/ONLSTM-analysis/embed_regularize.py 62d148258bdd054f unverified BSD-3-Clause (permissive)
Trellis Networks for Sequence Modeling 15 Oct 2018 locuslab/trellisnet/TrellisNet/optimizations.py ad3308380ac371b2 unverified MIT (permissive)
Building Language Models for Text with Named Entities 13 May 2018 uclanlp/NamedEntityLanguageModel/embed_regularize.py 3c2fe307e011be7d unverified BSD-3-Clause (permissive)
Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context 12 May 2018 urvashik/lm-context-analysis/embed_regularize.py 3c2fe307e011be7d unverified Apache-2.0 (permissive)
Spell Once, Summon Anywhere: A Two-Level Open-Vocabulary Language Model 23 Apr 2018 sjmielke/spell-once/embed_regularize.py 85281b50c86cffc8 unverified BSD-3-Clause (permissive)
An Analysis of Neural Language Modeling at Multiple Scales 22 Mar 2018 AtheMathmo/lookahead-lstm/embed_regularize.py 62d148258bdd054f unverified BSD-3-Clause (permissive)
Breaking the Softmax Bottleneck: A High-Rank RNN Language Model 10 Nov 2017 yfreedomliTHU/mos-pytorch1.1/embed_regularize.py 61247fd8db2741cf unverified MIT (permissive)
Breaking the Softmax Bottleneck: A High-Rank RNN Language Model 10 Nov 2017 zihangdai/mos/embed_regularize.py 612788fb8fc53b62 unverified MIT (permissive)
Breaking the Softmax Bottleneck: A High-Rank RNN Language Model 10 Nov 2017 nkcr/overlap-ml/awd/embed_regularize.py 6a3d8df0b754bb01 unverified BSD-3-Clause (permissive)
arXiv:2025.naacl-long.61 vonfeng/DPLink/codes/models.py 61247fd8db2741cf 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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