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LinearActivation

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

LinearActivation appears in the code Syntology harvested for 16 papers, as 12 distinct code bodies found in 18 places (a place is one code body under one paper). At least one of them ran in 15 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 LinearActivation 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 11 of the 12 distinct code bodies named LinearActivation; 1 is unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
9ran · our draft was wrong
0ran · fixture could not drive it
2ran
1unverified
1fingerprinted

Licence is a property of each copy, so it is counted per place: 5 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 3 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
Taming foundation model with invariance-oriented pre-training for broad-spectrum EEG analysis across signal-level, brain-state, and brain-health tasks added by Syntology 2026-08 (from id) jingyingma01/CodeBrain/Models/SSSM.py 2dcd0e47512ab0d7 ran · our draft was wrong Apache-2.0 (permissive)
REPO: Language Models with Context Re-Positioning added by Syntology 2025-12 (from id) SakanaAI/repo/OLMo/olmo/custom_modules/sequential_pe.py 279011e122526eef ran · metamorphic tier: invariant fingerprinted no licence file found · pointer only
EvoBrain: Dynamic Multi-Channel EEG Graph Modeling for Time-Evolving Brain Networks added by Syntology 2025-09 (from id) Kotoge/EvoBrain/model/s4.py 400c752a0d8d06f7 ran · our draft was wrong MIT (permissive)
Utilizing Image Transforms and Diffusion Models for Generative Modeling of Short and Long Time Series 25 Oct 2024 azencot-group/ImagenTime/models/testing_models/s4.py 898daef1784ec4bd ran · our draft was wrong no licence file found · pointer only
ANT: Adaptive Noise Schedule for Time Series Diffusion Models 18 Oct 2024 seunghan96/ANT/src/ANT/arch/s4.py 400c752a0d8d06f7 ran · our draft was wrong no licence file found · pointer only
Mamba: Linear-Time Sequence Modeling with Selective State Spaces 1 Dec 2023 lab-emi/cleanumamba/src/network/S4/MambaS4.py 76fa2650a64a7d15 ran · our draft was wrong no licence file found · pointer only
Efficient Movie Scene Detection using State-Space Transformers 29 Dec 2022 md-mohaiminul/trans4mer/trans4mer/model/s4.py 898daef1784ec4bd ran · our draft was wrong Apache-2.0 (permissive)
Efficient Long Sequence Modeling via State Space Augmented Transformer 15 Dec 2022 microsoft/efficientlongsequencemodeling/spade-modules/s4.py 898daef1784ec4bd 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 6ecbcf4f3d00f7ed unverified Apache-2.0 (permissive)
Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models 21 Nov 2022 tsy935/graphs4mer/model/s4.py 400c752a0d8d06f7 ran · our draft was wrong MIT (permissive)
What Makes Convolutional Models Great on Long Sequence Modeling? 17 Oct 2022 ctlllll/sgconv/gconv_standalone.py f73a9491e3447518 ran · our draft was wrong no licence file found · pointer only
Long Movie Clip Classification with State-Space Video Models 4 Apr 2022 md-mohaiminul/ViS4mer/models.py 8538f8c63d35c959 ran · our draft was wrong MIT (permissive)
Efficiently Modeling Long Sequences with Structured State Spaces 31 Oct 2021 raminmh/liquid-s4/src/models/s4/s4.py 898daef1784ec4bd ran · our draft was wrong Apache-2.0 (permissive)
Efficiently Modeling Long Sequences with Structured State Spaces 31 Oct 2021 elgazzarr/fmri-s4/src/models/sequence/ss/standalone/s4.py 522a1fc1276ec144 ran · our draft was wrong MIT (permissive)
Efficiently Modeling Long Sequences with Structured State Spaces 31 Oct 2021 leonty1/essm/src/models/sequence/ss/standalone/dss.py 6c960a9598a04692 ran · our draft was wrong Apache-2.0 (permissive)
DiffWave: A Versatile Diffusion Model for Audio Synthesis 21 Sep 2020 albertfgu/diffwave-sashimi/models/sashimi.py 4f863cfb47139983 ran · our draft was wrong MIT (permissive)
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding 11 Oct 2018 yifding/hetseq/hetseq/bert_modeling.py 69b2af37fc2ae145 ran MIT (permissive)
Playing Atari with Deep Reinforcement Learning 19 Dec 2013 proroklab/popgym/popgym/baselines/models/s4d.py 400c752a0d8d06f7 ran · our draft was wrong 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".

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections