Paper Citation Record · LEDGER

Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 19 inbound Pith citation observations for arXiv:1707.09835.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1707.09835 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 19 of 19 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:41:50.725814Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-07-04T00:29:16.760084Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 8c27e6ba-1bf0-449b-b33e-086699a458f4 · inbound

Can Gradient Descent Simulate Prompting? cites this paper.

Can Gradient Descent Simulate Prompting? Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T22:41:50.725814Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa49e60e-e945-4450-a901-55ee79c2c1c6 · inbound

An Enhanced Privacy-preserving Federated Few-shot Learning Framework for Respiratory Disease Diagnosis cites this paper.

An Enhanced Privacy-preserving Federated Few-shot Learning Framework for Respiratory Disease Diagnosis Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T18:44:09.630588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 26e65407-fe08-4e1b-bdf9-d475a7b14b59 · inbound

ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation cites this paper.

ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T18:06:32.015482Z

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Observation 1f9d5112-b011-4abc-9af4-185bae7352fc · inbound

Acquiring and Adapting Priors for Novel Tasks via Neural Meta-Architectures cites this paper.

Acquiring and Adapting Priors for Novel Tasks via Neural Meta-Architectures Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-06T19:32:54.294985Z

Source-reported events for the cited work

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Observation a67229c7-db01-4f3e-b706-6c4d1d7a153d · inbound

Few-Shot Learning in Video and 3D Object Detection: A Survey cites this paper.

Few-Shot Learning in Video and 3D Object Detection: A Survey Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T15:00:44.288599Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0948937a-1037-47d2-affd-43a25527753a · inbound

ICM-Fusion: In-Context Meta-Optimized LoRA Fusion for Multi-Task Adaptation cites this paper.

ICM-Fusion: In-Context Meta-Optimized LoRA Fusion for Multi-Task Adaptation Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T00:55:58.180633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60e5af41-e77c-4206-8a4c-fee289dfad3b · inbound

FedMeNF: Privacy-Preserving Federated Meta-Learning for Neural Fields cites this paper.

FedMeNF: Privacy-Preserving Federated Meta-Learning for Neural Fields Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-05T22:57:21.203812Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 069782e2-5df1-4b02-93fa-3c0911c4b090 · inbound

Disentangled Multi-Context Meta-Learning: Unlocking robust and Generalized Task Learning cites this paper.

Disentangled Multi-Context Meta-Learning: Unlocking robust and Generalized Task Learning Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T12:48:05.426552Z

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Unavailable: canonical work link unavailable.

Observation 6bc115ec-1dfa-46b3-974a-4f594601a5f5 · inbound

Learnable Loss Geometries with Mirror Descent for Scalable and Convergent Meta-Learning cites this paper.

Learnable Loss Geometries with Mirror Descent for Scalable and Convergent Meta-Learning Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-05T11:45:39.013187Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b515f31e-9836-4f6c-835f-0c248129ca94 · inbound

CCoMAML: Efficient Cattle Identification Using Cooperative Model-Agnostic Meta-Learning cites this paper.

CCoMAML: Efficient Cattle Identification Using Cooperative Model-Agnostic Meta-Learning Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T16:57:31.978755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25644cbd-11d8-49b2-a8fd-37ddb4ccf36a · inbound

Trust Region Continual Learning as an Implicit Meta-Learner cites this paper.

Trust Region Continual Learning as an Implicit Meta-Learner Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-03T05:28:58.587235Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12e8af38-e15d-45ed-94b0-dca375f802ed · inbound

AHC: Meta-Learned Adaptive Compression for Continual Object Detection on Memory-Constrained Microcontrollers cites this paper.

AHC: Meta-Learned Adaptive Compression for Continual Object Detection on Memory-Constrained Microcontrollers Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-05-15T20:26:35.677870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 70327406-1ccc-47eb-b529-91b9a2e94fbe · inbound

Binomial Gradient-Based Meta-Learning for Enhanced Meta-Gradient Estimation cites this paper.

Binomial Gradient-Based Meta-Learning for Enhanced Meta-Gradient Estimation Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:41:00.332349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b83f287f-70bd-40c1-89eb-a79379f3a72d · inbound

Frequency-Enhanced Dual-Subspace Networks for Few-Shot Fine-Grained Image Classification cites this paper.

Frequency-Enhanced Dual-Subspace Networks for Few-Shot Fine-Grained Image Classification Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-10T11:20:10.833723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e9ae51a2-ee77-434b-b591-2c06548db859 · inbound

Learning to Evaluate: Cost-Effective Model Evaluation on Unlabeled Data with Meta-Learning cites this paper.

Learning to Evaluate: Cost-Effective Model Evaluation on Unlabeled Data with Meta-Learning Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-05-25T04:45:20.161423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a327b0b8-0dbe-4507-a178-5e8a547fbaeb · inbound

Learning to Evaluate: Cost-Effective Model Evaluation on Unlabeled Data with Meta-Learning cites this paper.

Learning to Evaluate: Cost-Effective Model Evaluation on Unlabeled Data with Meta-Learning Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-06-30T15:44:48.335603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e06fe07d-e802-4ac1-b656-b31c2da0500d · inbound

Provable Data Scaling Law for Meta Learning via Complexity Minimization cites this paper.

Provable Data Scaling Law for Meta Learning via Complexity Minimization Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

Reference 60

Resolution: metadata mismatch; local_arxiv, observed 2026-07-02T00:56:25.422603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f408661f-df26-4766-97f4-4a855c96d2a8 · inbound

Self-Improvement Can Self-Regress: The Rise-and-Collapse Failure Mode of LLM Self-Training cites this paper.

Self-Improvement Can Self-Regress: The Rise-and-Collapse Failure Mode of LLM Self-Training Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-07-04T00:29:16.764076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 24bb457f-3b63-4083-be1f-45c1a0a5e97f · inbound

Labeled-Data-Free Meta-Learning: Efficient Task Generation Using Pre-trained Models and Unlabeled Data cites this paper.

Labeled-Data-Free Meta-Learning: Efficient Task Generation Using Pre-trained Models and Unlabeled Data Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-12T06:37:34.581914Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.