Domain-Generalization to Improve Learning in Meta-Learning Algorithms | Paper Citation Record

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This paper cites In: International Conference on Machine Learning, pp.

Domain-Generalization to Improve Learning in Meta-Learning Algorithms In: International Conference on Machine Learning, pp

Reference 1

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This paper cites SIAM review60(2), 223–311 (2018).

Domain-Generalization to Improve Learning in Meta-Learning Algorithms SIAM review60(2), 223–311 (2018)

Reference 2

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This paper cites In: International Conference on Machine Learning, pp.

Domain-Generalization to Improve Learning in Meta-Learning Algorithms In: International Conference on Machine Learning, pp

Reference 3

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This paper cites Sharpness-Aware Minimization for Efficiently Improving Generalization.

Domain-Generalization to Improve Learning in Meta-Learning Algorithms Sharpness-Aware Minimization for Efficiently Improving Generalization

Reference 4

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This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

Domain-Generalization to Improve Learning in Meta-Learning Algorithms In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 5

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Observation af9b296c-3b89-4e9a-8992-0048341a7b78 · outbound

This paper cites Surrogate Gap Minimization Improves Sharpness-Aware Training.

Domain-Generalization to Improve Learning in Meta-Learning Algorithms Surrogate Gap Minimization Improves Sharpness-Aware Training

Reference 6

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This paper cites In: International Conference on Machine Learning, pp.

Domain-Generalization to Improve Learning in Meta-Learning Algorithms In: International Conference on Machine Learning, pp

Reference 7

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This paper cites Advances in neural information processing systems32(2019).

Domain-Generalization to Improve Learning in Meta-Learning Algorithms Advances in neural information processing systems32(2019)

Reference 8

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This paper cites Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML.

Domain-Generalization to Improve Learning in Meta-Learning Algorithms Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML

Reference 9

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This paper cites Advances in neural information processing systems29(2016).

Domain-Generalization to Improve Learning in Meta-Learning Algorithms Advances in neural information processing systems29(2016)

Reference 10

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This paper cites On First-Order Meta-Learning Algorithms.

Domain-Generalization to Improve Learning in Meta-Learning Algorithms On First-Order Meta-Learning Algorithms

Reference 11

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This paper cites Advances in neural information processing systems31(2018).

Domain-Generalization to Improve Learning in Meta-Learning Algorithms Advances in neural information processing systems31(2018)

Reference 12

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This paper cites Advances in neural information processing systems31(2018).

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

This paper cites Recasting Gradient-Based Meta-Learning as Hierarchical Bayes.

Domain-Generalization to Improve Learning in Meta-Learning Algorithms Recasting Gradient-Based Meta-Learning as Hierarchical Bayes

Reference 14

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This paper cites 1733–1774 (2022).

Domain-Generalization to Improve Learning in Meta-Learning Algorithms 1733–1774 (2022)

Reference 15

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This paper cites Advances in Neural Information Processing Systems31(2018).

Domain-Generalization to Improve Learning in Meta-Learning Algorithms Advances in Neural Information Processing Systems31(2018)

Reference 16

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This paper cites Advances in Neural Information Processing Systems34, 2173–2186 (2021).

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

This paper cites In: Uncertainty in Artificial Intel- ligence, pp.

Domain-Generalization to Improve Learning in Meta-Learning Algorithms In: Uncertainty in Artificial Intel- ligence, pp

Reference 18

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This paper cites Neural Computing and Applications, 1–28 (2025) 21.

Domain-Generalization to Improve Learning in Meta-Learning Algorithms Neural Computing and Applications, 1–28 (2025) 21

Reference 19

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This paper cites On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning.

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

This paper cites Advances in Neural Information Processing Systems33, 17886–17895 (2020).

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

This paper cites In: Proceedings of the 2021 SIAM International Conference on Data Mining (SDM), pp.

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

This paper cites Advances in Neural Information Processing Systems34, 3096–3107 (2021).

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

This paper cites Advances in Neural Information Processing Systems33, 18860–18871 (2020).

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

This paper cites Advances in Neural Information Processing Systems33, 3557–3568 (2020).

Domain-Generalization to Improve Learning in Meta-Learning Algorithms Advances in Neural Information Processing Systems33, 3557–3568 (2020)

Reference 25

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This paper cites In: International Conference on Artificial Intelligence and Statistics, pp.

Domain-Generalization to Improve Learning in Meta-Learning Algorithms In: International Conference on Artificial Intelligence and Statistics, pp

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:10:41.296217Z

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This paper cites Sustainability 16(15), 6705 (2024).

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

This paper cites Computers & industrial engineering100, 34–51 (2016).

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

This paper cites Scientia Iranica26(5), 3007–3031 (2019).

Domain-Generalization to Improve Learning in Meta-Learning Algorithms Scientia Iranica26(5), 3007–3031 (2019)

Reference 29

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This paper cites Interna- tional journal of artificial intelligence16(1), 88–112 (2018).

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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This paper cites Journal of 22 industrial and systems engineering10(3), 140–160 (2017).

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

This paper cites In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp.

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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This paper cites Journal of industrial and systems engineering11(2), 134–150 (2018).

Domain-Generalization to Improve Learning in Meta-Learning Algorithms Journal of industrial and systems engineering11(2), 134–150 (2018)

Reference 33

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Observation 36501e5a-a0d4-459c-8c43-bbf4ba05254a · outbound

This paper cites Journal of machine learning research12(7) (2011).

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

This paper cites Adam: A Method for Stochastic Optimization.

Domain-Generalization to Improve Learning in Meta-Learning Algorithms Adam: A Method for Stochastic Optimization

Reference 35

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Domain-Generalization to Improve Learning in Meta-Learning Algorithms On the Convergence of Adam and Beyond

Reference 36

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This paper cites mSAM: Micro-Batch-Averaged Sharpness-Aware Minimization.

Domain-Generalization to Improve Learning in Meta-Learning Algorithms mSAM: Micro-Batch-Averaged Sharpness-Aware Minimization

Reference 37

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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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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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Domain-Generalization to Improve Learning in Meta-Learning Algorithms Unresolved cited work

Reference 40

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This paper cites In: International Conference on Machine Learning, pp.

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

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

Domain-Generalization to Improve Learning in Meta-Learning Algorithms In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 42

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Observation f38782cf-e253-46b6-bcde-1a69ed655f85 · outbound

This paper cites Dataset Condensation with Gradient Matching.

Domain-Generalization to Improve Learning in Meta-Learning Algorithms Dataset Condensation with Gradient Matching

Reference 43

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This paper cites In: International Conference on Machine Learning, pp.

Domain-Generalization to Improve Learning in Meta-Learning Algorithms In: International Conference on Machine Learning, pp

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:10:38.684739Z

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Observation 6ffce50a-5a2a-4f92-b762-9a4b74cba3a7 · outbound

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Domain-Generalization to Improve Learning in Meta-Learning Algorithms Gradient Matching for Domain Generalization

Reference 45

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This paper cites In: 2023 IEEE International Conference on Omni-layer Intelligent Systems (COINS), pp.

Domain-Generalization to Improve Learning in Meta-Learning Algorithms In: 2023 IEEE International Conference on Omni-layer Intelligent Systems (COINS), pp

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:10:38.465699Z

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Observation d753d5dd-cb49-4605-b4a4-d984ba7a4263 · outbound

This paper cites Gradient-Matching Coresets for Rehearsal-Based Continual Learning.

Domain-Generalization to Improve Learning in Meta-Learning Algorithms Gradient-Matching Coresets for Rehearsal-Based Continual Learning

Reference 47

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This paper cites IEEE Transactions on Neural Networks and Learning Systems (2022).

Domain-Generalization to Improve Learning in Meta-Learning Algorithms IEEE Transactions on Neural Networks and Learning Systems (2022)

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:10:38.275909Z

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Observation 37a65574-27fe-442c-ad41-e7d14673a5dd · outbound

This paper cites In: International Conference on Learning Representations (2016).

Domain-Generalization to Improve Learning in Meta-Learning Algorithms In: International Conference on Learning Representations (2016)

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:10:38.094559Z

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This paper cites https://github.

Domain-Generalization to Improve Learning in Meta-Learning Algorithms https://github

Reference 50

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Observation 2bc491c5-d3e0-408c-b03a-c93949fa762d · outbound

This paper cites In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol.

Domain-Generalization to Improve Learning in Meta-Learning Algorithms In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:10:37.519854Z

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Observation 61b4b606-297d-4e4c-8b3f-84727fcfb58c · outbound

This paper cites Torchmeta: A Meta-Learning library for PyTorch.

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

This paper cites Advances in neural information processing systems30(2017).

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

This paper cites In: Hughes, A.L., McNeill, F., Zobel, C.W.

Domain-Generalization to Improve Learning in Meta-Learning Algorithms In: Hughes, A.L., McNeill, F., Zobel, C.W

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:10:37.291871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 310a49ff-6795-467b-9c0e-1af1e0d09350 · outbound

This paper cites In: Adrot, A., Grace, R., Moore, K.A., Zobel, C.W.

Domain-Generalization to Improve Learning in Meta-Learning Algorithms In: Adrot, A., Grace, R., Moore, K.A., Zobel, C.W

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:10:37.038660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dfaa595f-63e4-4efa-bc8d-1a8735af826a · outbound

This paper cites Online Soc.

Domain-Generalization to Improve Learning in Meta-Learning Algorithms Online Soc

Reference 56

Resolution: metadata mismatch; raw_fallback, observed 2026-08-05T21:10:33.416163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e9f0d823-89ca-44af-9ecd-e019c2a37542 · outbound

This paper cites In: The Fifteenth International Conference on Information, Process, and Knowledge Management, eKNOW23 (2023).

Domain-Generalization to Improve Learning in Meta-Learning Algorithms In: The Fifteenth International Conference on Information, Process, and Knowledge Management, eKNOW23 (2023)

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:10:36.818764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0fce312f-c1d6-427f-8eaa-4f4f82f793c1 · outbound

This paper cites In: Proceedings of the International ISCRAM Conference (2025).

Domain-Generalization to Improve Learning in Meta-Learning Algorithms In: Proceedings of the International ISCRAM Conference (2025)

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:10:36.587866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 389ed263-2bb7-413c-85cd-9a3dec2528a7 · outbound

This paper cites In: Khazanchi, J.R.I.D.N.L.D.

Domain-Generalization to Improve Learning in Meta-Learning Algorithms In: Khazanchi, J.R.I.D.N.L.D

Reference 59

Resolution: verified exact; doi, observed 2026-08-05T21:10:32.569956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a26df94f-4b24-4185-bbc4-27fc729a29c7 · outbound

This paper cites In: Tsumoto, S., Ohsawa, Y., Chen, L., Poel, D.V., Hu, X., Motomura, Y., Takagi, T., Wu, L., Xie, Y., Abe, A., Raghavan, V.

Domain-Generalization to Improve Learning in Meta-Learning Algorithms In: Tsumoto, S., Ohsawa, Y., Chen, L., Poel, D.V., Hu, X., Motomura, Y., Takagi, T., Wu, L., Xie, Y., Abe, A., Raghavan, V

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-05T21:10:31.568697Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6df001ce-c0ff-4c50-abcd-89cbef3bf030 · outbound

This paper cites Briefings in Bioinformatics25(Supplement 1), 232 (2024).

Domain-Generalization to Improve Learning in Meta-Learning Algorithms Briefings in Bioinformatics25(Supplement 1), 232 (2024)

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:10:36.307846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cf0433b3-3e44-403d-8b7a-ed43e65f55ed · outbound

This paper cites Electronics13(19), 3825 (2024).

Domain-Generalization to Improve Learning in Meta-Learning Algorithms Electronics13(19), 3825 (2024)

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:10:36.121177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2c6d153f-e015-4d2b-9ac6-c8cb3bd102d9 · outbound

This paper cites A Novel Approach To Implementing Knowledge Distillation In Tsetlin Machines.

Domain-Generalization to Improve Learning in Meta-Learning Algorithms A Novel Approach To Implementing Knowledge Distillation In Tsetlin Machines

Reference 63

Resolution: metadata mismatch; local_arxiv, observed 2026-08-05T21:10:32.977689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c5d9ab48-7c83-49d3-91a0-faec7fd334cb · outbound

This paper cites Adaptive Temperature Based on Logits Correlation in Knowledge Distillation.

Domain-Generalization to Improve Learning in Meta-Learning Algorithms Adaptive Temperature Based on Logits Correlation in Knowledge Distillation

Reference 64

Resolution: malformed identifier; local_arxiv, observed 2026-08-05T21:10:32.743748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cc3245f2-ee9b-4afa-8536-a0130d417485 · outbound

This paper cites Lemma 2: BoundingLγ2 2 E[|| e∇LSAGM ||2 | Ft].

Domain-Generalization to Improve Learning in Meta-Learning Algorithms Lemma 2: BoundingLγ2 2 E[|| e∇LSAGM ||2 | Ft]

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:10:35.906732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation baee9475-02e3-4db2-a898-1ebf7fa94ab7 · outbound

This paper cites an unresolved cited work.

Domain-Generalization to Improve Learning in Meta-Learning Algorithms Unresolved cited work

Reference 66

Resolution: unresolved; raw_fallback, observed 2026-08-05T21:10:35.661457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d3f53b2a-f185-4e6b-95c9-e7c7d90f2095 · outbound

This paper cites Lemma 3: Bounding||∇Lp||.

Domain-Generalization to Improve Learning in Meta-Learning Algorithms Lemma 3: Bounding||∇Lp||

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:10:35.429602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f10cae78-77c0-49db-8118-e61bf379f2c2 · outbound

This paper cites an unresolved cited work.

Domain-Generalization to Improve Learning in Meta-Learning Algorithms Unresolved cited work

Reference 68

Resolution: unresolved; raw_fallback, observed 2026-08-05T21:10:35.115588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation be1ce834-65fc-4a06-86f1-d9c3311d9af5 · outbound

This paper cites an unresolved cited work.

Domain-Generalization to Improve Learning in Meta-Learning Algorithms Unresolved cited work

Reference 69

Resolution: unresolved; raw_fallback, observed 2026-08-05T21:10:34.863751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers