Paper Citation Record · LEDGER

Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:1903.03096.

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

pith.paper-citation-record.v1
1903.03096 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:47:10.603459Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-17T22:12:05.816125Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation a0f96071-7c24-4e8f-9796-a08351c3fafa · inbound

A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark cites this paper.

A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-17T22:12:05.818527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2f4d9e3d-0414-46e8-8e77-b80310380f6e · inbound

Task-Specific Preconditioner for Cross-Domain Few-Shot Learning cites this paper.

Task-Specific Preconditioner for Cross-Domain Few-Shot Learning Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-11T11:27:13.871093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72aa9c57-0772-4b34-924a-1a9449524869 · inbound

AI for the Open-World: the Learning Principles cites this paper.

AI for the Open-World: the Learning Principles Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples

Reference 176

Resolution: unresolved; no resolver link, observed 2026-08-16T11:47:10.603459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18f29162-7540-4334-bff4-2204242fedb7 · inbound

Prompt Tuning Vision Language Models with Margin Regularizer for Few-Shot Learning under Distribution Shifts cites this paper.

Prompt Tuning Vision Language Models with Margin Regularizer for Few-Shot Learning under Distribution Shifts Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T15:20:29.447891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0159cc3c-f182-40e2-9e6b-337a62544fb2 · inbound

Episode-specific Fine-tuning for Metric-based Few-shot Learners with Optimization-based Training cites this paper.

Episode-specific Fine-tuning for Metric-based Few-shot Learners with Optimization-based Training Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T19:14:23.126864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f201ead1-cb04-43e5-9fa5-d5e1e4782f7f · 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-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T18:06:32.713704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd6d0169-24be-4afa-bfa6-5b3d7d028131 · 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-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T16:57:32.066879Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4701cf0-7edb-4624-847a-a47cf8365a5d · inbound

Meta-Learning and Meta-Reinforcement Learning -- Tracing the Path towards DeepMind's Adaptive Agent cites this paper.

Meta-Learning and Meta-Reinforcement Learning -- Tracing the Path towards DeepMind's Adaptive Agent Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples

Reference 171

Resolution: verified exact; arxiv_id, observed 2026-05-15T20:46:35.644472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5f84cadf-33dd-4fa7-9037-e4b507922c73 · inbound

MAPLE: A Meta-learning Framework for Cross-Prompt Essay Scoring cites this paper.

MAPLE: A Meta-learning Framework for Cross-Prompt Essay Scoring Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples

Reference 67

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T06:01:13.429246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 01ef6343-61ae-46fe-ba82-2fc576fe97c2 · inbound

Position: Stop Reactively Patching Your Model Every Time and Start Proactive Test-Driven AI Development cites this paper.

Position: Stop Reactively Patching Your Model Every Time and Start Proactive Test-Driven AI Development Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-02T07:40:22.094772Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.