{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/code/learner","entry":"Learner","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":17,"n_papers_ran":8,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":21,"n_samples_ran":9,"n_samples_fingerprinted":0,"n_places":21,"n_places_pointer_only":8,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":9,"unverified":12},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2603.10237","paper":"/paper/arxiv-2603-10237","title":"One Adapter for All: Towards Unified Representation in Step-Imbalanced Class-Incremental Learning","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"xiaoyanzhang1/One-A","path":"models/onea.py","file_url":"https://github.com/xiaoyanzhang1/One-A/blob/HEAD/models/onea.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"68a29c9cb0466184","mcp_get_code":{"code_sha256":"68a29c9cb0466184"}},{"arxiv_id":"2502.20032","paper":"/paper/order-robust-class-incremental-learning-graph","title":"Order-Robust Class Incremental Learning: Graph-Driven Dynamic Similarity Grouping","date":"2025-02-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AIGNLAI/GDDSG","path":"GDDSG.py","file_url":"https://github.com/AIGNLAI/GDDSG/blob/HEAD/GDDSG.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"95f34200e2374020","mcp_get_code":{"code_sha256":"95f34200e2374020"}},{"arxiv_id":"2410.00645","paper":"/paper/icl-tsvd-bridging-theory-and-practice-in","title":"LoRanPAC: Low-rank Random Features and Pre-trained Models for Bridging Theory and Practice in Continual Learning","date":"2024-10-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liangzu/loranpac","path":"models/ranpac.py","file_url":"https://github.com/liangzu/loranpac/blob/HEAD/models/ranpac.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c60ef1ac569aff94","mcp_get_code":{"code_sha256":"c60ef1ac569aff94"}},{"arxiv_id":"2409.19212","paper":"/paper/an-accelerated-algorithm-for-stochastic","title":"An Accelerated Algorithm for Stochastic Bilevel Optimization under Unbounded Smoothness","date":"2024-09-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mingruiliu-ml-lab/accelerated-bilevel-optimization-unbounded-smoothness","path":"auc_maximization/methods/accbo.py","file_url":"https://github.com/mingruiliu-ml-lab/accelerated-bilevel-optimization-unbounded-smoothness/blob/HEAD/auc_maximization/methods/accbo.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2c7767b37ecd4981","mcp_get_code":{"code_sha256":"2c7767b37ecd4981"}},{"arxiv_id":"2403.12030","paper":"/paper/expandable-subspace-ensemble-for-pre-trained","title":"Expandable Subspace Ensemble for Pre-Trained Model-Based Class-Incremental Learning","date":"2024-03-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sun-hailong/cvpr24-ease","path":"models/ease.py","file_url":"https://github.com/sun-hailong/cvpr24-ease/blob/HEAD/models/ease.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fc5a5e1686368710","mcp_get_code":{"code_sha256":"fc5a5e1686368710"}},{"arxiv_id":"2401.09587","paper":"/paper/bilevel-optimization-under-unbounded","title":"Bilevel Optimization under Unbounded Smoothness: A New Algorithm and Convergence Analysis","date":"2024-01-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mingruiliu-ml-lab/bilevel-optimization-under-unbounded-smoothness","path":"meta_learning/bo_rep.py","file_url":"https://github.com/mingruiliu-ml-lab/bilevel-optimization-under-unbounded-smoothness/blob/HEAD/meta_learning/bo_rep.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4c731af150f1c43c","mcp_get_code":{"code_sha256":"4c731af150f1c43c"}},{"arxiv_id":"2303.01704","paper":"/paper/model-explanation-disparities-as-a-fairness","title":"Feature Importance Disparities for Data Bias Investigations","date":"2023-03-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"safr-ai-lab/xai-disparity","path":"constrained_opt.py","file_url":"https://github.com/safr-ai-lab/xai-disparity/blob/HEAD/constrained_opt.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"147066ba0d7d77f3","mcp_get_code":{"code_sha256":"147066ba0d7d77f3"}},{"arxiv_id":"2208.12967","paper":"/paper/anti-retroactive-interference-for-lifelong","title":"Anti-Retroactive Interference for Lifelong Learning","date":"2022-08-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bhrqw/ari","path":"learner_task_ari.py","file_url":"https://github.com/bhrqw/ari/blob/HEAD/learner_task_ari.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"67219a1ac58de3c5","mcp_get_code":{"code_sha256":"67219a1ac58de3c5"}},{"arxiv_id":"2206.08853","paper":"/paper/minedojo-building-open-ended-embodied-agents","title":"MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge","date":"2022-06-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pku-rl/copl","path":"src/core/ppo.py","file_url":"https://github.com/pku-rl/copl/blob/HEAD/src/core/ppo.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e9c9518cae5dc1e0","mcp_get_code":{"code_sha256":"e9c9518cae5dc1e0"}},{"arxiv_id":"2205.14922","paper":"/paper/acil-analytic-class-incremental-learning-with","title":"ACIL: Analytic Class-Incremental Learning with Absolute Memorization and Privacy Protection","date":"2022-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ZHUANGHP/Analytic-continual-learning","path":"analytic/ACIL.py","file_url":"https://github.com/ZHUANGHP/Analytic-continual-learning/blob/HEAD/analytic/ACIL.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fd5487bdd1bc5fe1","mcp_get_code":{"code_sha256":"fd5487bdd1bc5fe1"}},{"arxiv_id":"2106.15367","paper":"/paper/maml-is-a-noisy-contrastive-learner","title":"MAML is a Noisy Contrastive Learner in Classification","date":"2021-06-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"iandrover/maml_noisy_contrasive_learner","path":"exact_contrastiveness/learner.py","file_url":"https://github.com/iandrover/maml_noisy_contrasive_learner/blob/HEAD/exact_contrastiveness/learner.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"55e421a8caf78a25","mcp_get_code":{"code_sha256":"55e421a8caf78a25"}},{"arxiv_id":"2106.07636","paper":"/paper/meta-two-sample-testing-learning-kernels-for","title":"Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data","date":"2021-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fengliu90/MetaTesting","path":"MetaTST.py","file_url":"https://github.com/fengliu90/MetaTesting/blob/HEAD/MetaTST.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e03c2077ec04aa3a","mcp_get_code":{"code_sha256":"e03c2077ec04aa3a"}},{"arxiv_id":"2009.08107","paper":"/paper/few-shot-unsupervised-continual-learning","title":"Few-Shot Unsupervised Continual Learning through Meta-Examples","date":"2020-09-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alessiabertugli/FUSION","path":"model/meta_learner.py","file_url":"https://github.com/alessiabertugli/FUSION/blob/HEAD/model/meta_learner.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"93eab00ecefb419a","mcp_get_code":{"code_sha256":"93eab00ecefb419a"}},{"arxiv_id":"2007.07732","paper":"/paper/lifelong-learning-of-compositional-structures","title":"Lifelong Learning of Compositional Structures","date":"2020-07-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"GRASP-ML/Mendez2020Compositional","path":"learners/er_compositional.py","file_url":"https://github.com/GRASP-ML/Mendez2020Compositional/blob/HEAD/learners/er_compositional.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"455d4f9b54f7de05","mcp_get_code":{"code_sha256":"455d4f9b54f7de05"}},{"arxiv_id":"1909.04630","paper":"/paper/meta-learning-with-implicit-gradients","title":"Meta-Learning with Implicit Gradients","date":"2019-09-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aravindr93/imaml_dev","path":"implicit_maml/learner_model.py","file_url":"https://github.com/aravindr93/imaml_dev/blob/HEAD/implicit_maml/learner_model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"973d52f05dd6e23a","mcp_get_code":{"code_sha256":"973d52f05dd6e23a"}},{"arxiv_id":"1802.01561","paper":"/paper/impala-scalable-distributed-deep-rl-with","title":"IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures","date":"2018-02-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"seolhokim/DistributedRL-Pytorch-Ray","path":"agents/runners/learners/impala_learner.py","file_url":"https://github.com/seolhokim/DistributedRL-Pytorch-Ray/blob/HEAD/agents/runners/learners/impala_learner.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3fdd0c84f214c2bb","mcp_get_code":{"code_sha256":"3fdd0c84f214c2bb"}},{"arxiv_id":"1703.03400","paper":"/paper/model-agnostic-meta-learning-for-fast","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","date":"2017-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"damedollaforthree/nrml","path":"meta.py","file_url":"https://github.com/damedollaforthree/nrml/blob/HEAD/meta.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6f410bc3b799fb12","mcp_get_code":{"code_sha256":"6f410bc3b799fb12"}},{"arxiv_id":"1703.03400","paper":"/paper/model-agnostic-meta-learning-for-fast","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","date":"2017-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"csyanbin/MAML-Pytorch-Multi-GPUs","path":"meta.py","file_url":"https://github.com/csyanbin/MAML-Pytorch-Multi-GPUs/blob/HEAD/meta.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"23f25fc3752e2b12","mcp_get_code":{"code_sha256":"23f25fc3752e2b12"}},{"arxiv_id":"1703.03400","paper":"/paper/model-agnostic-meta-learning-for-fast","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","date":"2017-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dragen1860/Reptile-Pytorch","path":"meta.py","file_url":"https://github.com/dragen1860/Reptile-Pytorch/blob/HEAD/meta.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f735a270b8af92fc","mcp_get_code":{"code_sha256":"f735a270b8af92fc"}},{"arxiv_id":"1703.03400","paper":"/paper/model-agnostic-meta-learning-for-fast","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","date":"2017-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JeonMinkyu/MAML_Pytorch","path":"meta.py","file_url":"https://github.com/JeonMinkyu/MAML_Pytorch/blob/HEAD/meta.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"58ec79d486693632","mcp_get_code":{"code_sha256":"58ec79d486693632"}},{"arxiv_id":"1703.03400","paper":"/paper/model-agnostic-meta-learning-for-fast","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","date":"2017-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dragen1860/MAML-Pytorch","path":"meta.py","file_url":"https://github.com/dragen1860/MAML-Pytorch/blob/HEAD/meta.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0422a0a59c73941e","mcp_get_code":{"code_sha256":"0422a0a59c73941e"}}]}