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Learner

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

Learner appears in the code Syntology harvested for 17 papers, as 21 distinct code bodies found in 21 places (a place is one code body under one paper). At least one of them ran in 8 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 Learner 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 9 of the 21 distinct code bodies named Learner; 12 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
9ran
12unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 8 of the 21 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

17 papers shown of 17, newest first; 21 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. 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
One Adapter for All: Towards Unified Representation in Step-Imbalanced Class-Incremental Learning added by Syntology 2026-03 (from id) xiaoyanzhang1/One-A/models/onea.py 68a29c9cb0466184 unverified MIT (permissive)
Order-Robust Class Incremental Learning: Graph-Driven Dynamic Similarity Grouping 27 Feb 2025 AIGNLAI/GDDSG/GDDSG.py 95f34200e2374020 unverified no licence file found · pointer only
LoRanPAC: Low-rank Random Features and Pre-trained Models for Bridging Theory and Practice in Continual Learning 1 Oct 2024 liangzu/loranpac/models/ranpac.py c60ef1ac569aff94 unverified no licence file found · pointer only
An Accelerated Algorithm for Stochastic Bilevel Optimization under Unbounded Smoothness 28 Sep 2024 mingruiliu-ml-lab/accelerated-bilevel-optimization-unbounded-smoothness/auc_maximization/methods/accbo.py 2c7767b37ecd4981 unverified MIT (permissive)
Expandable Subspace Ensemble for Pre-Trained Model-Based Class-Incremental Learning 18 Mar 2024 sun-hailong/cvpr24-ease/models/ease.py fc5a5e1686368710 unverified no licence file found · pointer only
Bilevel Optimization under Unbounded Smoothness: A New Algorithm and Convergence Analysis 17 Jan 2024 mingruiliu-ml-lab/bilevel-optimization-under-unbounded-smoothness/meta_learning/bo_rep.py 4c731af150f1c43c unverified MIT (permissive)
Feature Importance Disparities for Data Bias Investigations 3 Mar 2023 safr-ai-lab/xai-disparity/constrained_opt.py 147066ba0d7d77f3 ran no licence file found · pointer only
Anti-Retroactive Interference for Lifelong Learning 27 Aug 2022 bhrqw/ari/learner_task_ari.py 67219a1ac58de3c5 ran no licence file found · pointer only
MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge 17 Jun 2022 pku-rl/copl/src/core/ppo.py e9c9518cae5dc1e0 unverified MIT (permissive)
ACIL: Analytic Class-Incremental Learning with Absolute Memorization and Privacy Protection 30 May 2022 ZHUANGHP/Analytic-continual-learning/analytic/ACIL.py fd5487bdd1bc5fe1 unverified MIT (permissive)
MAML is a Noisy Contrastive Learner in Classification 29 Jun 2021 iandrover/maml_noisy_contrasive_learner/exact_contrastiveness/learner.py 55e421a8caf78a25 ran MIT (permissive)
Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data 14 Jun 2021 fengliu90/MetaTesting/MetaTST.py e03c2077ec04aa3a ran MIT (permissive)
Few-Shot Unsupervised Continual Learning through Meta-Examples 17 Sep 2020 alessiabertugli/FUSION/model/meta_learner.py 93eab00ecefb419a unverified Apache-2.0 (permissive)
Lifelong Learning of Compositional Structures 15 Jul 2020 GRASP-ML/Mendez2020Compositional/learners/er_compositional.py 455d4f9b54f7de05 ran Apache-2.0 (permissive)
Meta-Learning with Implicit Gradients 10 Sep 2019 aravindr93/imaml_dev/implicit_maml/learner_model.py 973d52f05dd6e23a ran MIT (permissive)
IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures 5 Feb 2018 seolhokim/DistributedRL-Pytorch-Ray/agents/runners/learners/impala_learner.py 3fdd0c84f214c2bb ran MIT (permissive)
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks 9 Mar 2017 damedollaforthree/nrml/meta.py 6f410bc3b799fb12 ran · metamorphic tier: deterministic MIT (permissive)
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks 9 Mar 2017 csyanbin/MAML-Pytorch-Multi-GPUs/meta.py 23f25fc3752e2b12 ran no licence file found · pointer only
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks 9 Mar 2017 dragen1860/Reptile-Pytorch/meta.py f735a270b8af92fc unverified no licence file found · pointer only
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks 9 Mar 2017 JeonMinkyu/MAML_Pytorch/meta.py 58ec79d486693632 unverified no licence file found · pointer only
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks 9 Mar 2017 dragen1860/MAML-Pytorch/meta.py 0422a0a59c73941e 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".

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