Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-05T21:10:32.331624Z
Paper Citation Record · LEDGER
As of 7 August 2026, this Paper Citation Record lists 69 of 69 outbound references and 0 inbound Pith citation observations for arXiv:2508.09418.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-05T21:10:32.331624Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
69 of 69 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 65d19db2-395f-41de-846c-1293844cd433 · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms In: International Conference on Machine Learning, pp
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Observation 17ae8f28-1a42-415e-84e0-eb812db7ba32 · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms SIAM review60(2), 223–311 (2018)
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Domain-Generalization to Improve Learning in Meta-Learning Algorithms In: International Conference on Machine Learning, pp
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Observation e97dc747-8078-44b4-bb85-1bc95b5e7680 · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp
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Observation af9b296c-3b89-4e9a-8992-0048341a7b78 · outbound
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Observation ae7e4653-f720-419e-8df2-efc6e3ae1181 · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms In: International Conference on Machine Learning, pp
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Observation 3280a3a9-9766-46d9-8a87-81ac8d202a6d · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms Advances in neural information processing systems32(2019)
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Observation a8754bb5-b36a-4bba-bb56-ff8f14b312af · outbound
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Observation c9a5cf64-357e-4047-87c1-07c80e8528c8 · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms Advances in neural information processing systems29(2016)
Reference 10
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Observation 82c3609f-9501-4558-9786-756b5fc60368 · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms On First-Order Meta-Learning Algorithms
Reference 11
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Observation dfcd0321-c71d-484f-b75d-a1133e404738 · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms Advances in neural information processing systems31(2018)
Reference 12
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Observation 1452bcfe-e83f-4ba1-b8a0-1c304a481ad3 · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms Advances in neural information processing systems31(2018)
Reference 13
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Observation 23be1cda-2234-4def-808c-55cb02dc0afe · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms Recasting Gradient-Based Meta-Learning as Hierarchical Bayes
Reference 14
Source-reported events for the cited work
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Observation 124df75e-58ae-440b-8374-090cfc5535c4 · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms 1733–1774 (2022)
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Observation c36013d0-4122-4f1c-b523-86c0774910ca · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms Advances in Neural Information Processing Systems31(2018)
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Observation eba23c3e-a174-4ea8-ba90-ac3cdca994a0 · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms Advances in Neural Information Processing Systems34, 2173–2186 (2021)
Reference 17
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Observation 47df8b3f-8c24-4061-a7c9-0e10334653aa · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms In: Uncertainty in Artificial Intel- ligence, pp
Reference 18
Source-reported events for the cited work
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Observation 080f8052-6930-46ed-85b5-eeb91a8dee5b · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms Neural Computing and Applications, 1–28 (2025) 21
Reference 19
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Observation 45705878-c3e7-4abe-8ac4-7f32003b17c6 · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning
Reference 20
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Observation 65714b17-9d7b-4862-bee2-1d5d3a203724 · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms Advances in Neural Information Processing Systems33, 17886–17895 (2020)
Reference 21
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Observation a0d1904d-fd50-4ffe-97db-c6dfb884f71b · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms In: Proceedings of the 2021 SIAM International Conference on Data Mining (SDM), pp
Reference 22
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Observation a44d1e9e-e2ea-40d5-ba84-b558d1e4af12 · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms Advances in Neural Information Processing Systems34, 3096–3107 (2021)
Reference 23
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Observation f890e959-7062-47eb-a81b-70554b6a3dbf · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms Advances in Neural Information Processing Systems33, 18860–18871 (2020)
Reference 24
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Observation 0ba5dc2a-01f5-4ead-9365-9c854d585758 · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms Advances in Neural Information Processing Systems33, 3557–3568 (2020)
Reference 25
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Observation a64d3de5-77f1-4a6c-9812-360f5a0a2736 · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms In: International Conference on Artificial Intelligence and Statistics, pp
Reference 26
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Observation 99566c6d-402d-49d7-8bcd-813f370ffb8c · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms Sustainability 16(15), 6705 (2024)
Reference 27
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Observation 78b20724-e121-4228-9e98-9b8cfa73de65 · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms Computers & industrial engineering100, 34–51 (2016)
Reference 28
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Observation 75c0636a-7da7-4631-b337-46d246a51182 · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms Scientia Iranica26(5), 3007–3031 (2019)
Reference 29
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Observation f6f84ea8-8b07-46d8-9a78-0d3caf640e9c · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms Interna- tional journal of artificial intelligence16(1), 88–112 (2018)
Reference 30
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Observation fdcbc0db-5595-47ca-a884-c5842c6a8cc8 · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms Journal of 22 industrial and systems engineering10(3), 140–160 (2017)
Reference 31
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Observation 1ec55247-59d8-45f1-904c-c2af2f142bc2 · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp
Reference 32
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Observation a1b63385-8ebe-4862-901c-c1a58218265d · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms Journal of industrial and systems engineering11(2), 134–150 (2018)
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Observation 36501e5a-a0d4-459c-8c43-bbf4ba05254a · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms Journal of machine learning research12(7) (2011)
Reference 34
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Observation bbba1a35-2dc0-46a6-be63-af025a1d4a64 · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms Adam: A Method for Stochastic Optimization
Reference 35
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Observation 58b78b08-165d-43fd-9d0e-dc4da1679038 · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms On the Convergence of Adam and Beyond
Reference 36
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Observation fe413015-5030-467a-976e-fd451339344b · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms mSAM: Micro-Batch-Averaged Sharpness-Aware Minimization
Reference 37
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Observation d0772292-108b-4800-b413-fbf5014eef51 · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms K-SAM: Sharpness-Aware Minimization at the Speed of SGD
Reference 38
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Observation 7c9ad42d-1f88-4fc8-b6bd-7d22a23fae05 · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms Efficient Sharpness-aware Minimization for Improved Training of Neural Networks
Reference 39
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Observation 87fb0c88-3c9a-4cb9-8d3a-581b3f3ea8a5 · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms Unresolved cited work
Reference 40
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Observation f014face-0d69-4df2-87ca-7c08fce6b1b3 · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms In: International Conference on Machine Learning, pp
Reference 41
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Observation 23c31603-fdc5-4fef-b5c5-e2ea76a0ec20 · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp
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Observation f38782cf-e253-46b6-bcde-1a69ed655f85 · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms Dataset Condensation with Gradient Matching
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Observation 47dc3056-f8bd-48bf-b2bf-ad50b8f68b82 · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms In: International Conference on Machine Learning, pp
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Observation 6ffce50a-5a2a-4f92-b762-9a4b74cba3a7 · outbound
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Reference 45
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Observation 922c61b4-635f-46b4-903a-e8dc79c331b5 · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms In: 2023 IEEE International Conference on Omni-layer Intelligent Systems (COINS), pp
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Observation d753d5dd-cb49-4605-b4a4-d984ba7a4263 · outbound
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Observation 14818877-52da-4826-8587-16c1f75f6c64 · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms IEEE Transactions on Neural Networks and Learning Systems (2022)
Reference 48
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Observation 37a65574-27fe-442c-ad41-e7d14673a5dd · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms In: International Conference on Learning Representations (2016)
Reference 49
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Observation 682397c3-e1ea-4a11-b113-0021fe50707c · outbound
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Reference 50
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Observation 2bc491c5-d3e0-408c-b03a-c93949fa762d · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol
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Observation 61b4b606-297d-4e4c-8b3f-84727fcfb58c · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms Torchmeta: A Meta-Learning library for PyTorch
Reference 52
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Observation 343c1498-1af3-4fc0-81fa-23ddcd33f8cc · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms Advances in neural information processing systems30(2017)
Reference 53
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Observation 567575bb-e529-411b-85e0-eb05cff7c4c1 · outbound
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Reference 54
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Reference 55
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Observation e9f0d823-89ca-44af-9ecd-e019c2a37542 · outbound
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Reference 57
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Observation 0fce312f-c1d6-427f-8eaa-4f4f82f793c1 · outbound
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Reference 58
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Observation 389ed263-2bb7-413c-85cd-9a3dec2528a7 · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms In: Khazanchi, J.R.I.D.N.L.D
Reference 59
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Reference 60
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Observation 6df001ce-c0ff-4c50-abcd-89cbef3bf030 · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms Briefings in Bioinformatics25(Supplement 1), 232 (2024)
Reference 61
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Observation cf0433b3-3e44-403d-8b7a-ed43e65f55ed · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms Electronics13(19), 3825 (2024)
Reference 62
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Observation 2c6d153f-e015-4d2b-9ac6-c8cb3bd102d9 · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms A Novel Approach To Implementing Knowledge Distillation In Tsetlin Machines
Reference 63
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Observation c5d9ab48-7c83-49d3-91a0-faec7fd334cb · outbound
Domain-Generalization to Improve Learning in Meta-Learning Algorithms Adaptive Temperature Based on Logits Correlation in Knowledge Distillation
Reference 64
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Domain-Generalization to Improve Learning in Meta-Learning Algorithms Lemma 2: BoundingLγ2 2 E[|| e∇LSAGM ||2 | Ft]
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Observation baee9475-02e3-4db2-a898-1ebf7fa94ab7 · outbound
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Reference 66
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Reference 67
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Reference 68
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Reference 69
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No inbound Pith citation observations are available.