Paper Citation Record · LEDGER

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset

As of 19 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2504.18696.

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

pith.paper-citation-record.v1
2504.18696 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:19:33.991382Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

44 of 44 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bac72b20-4a46-4c0e-895c-075f56ba127a · outbound

This paper cites One-shot learning of object categories,.

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset One-shot learning of object categories,

Reference 1

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Observation 4d7291cb-9086-4a20-bfc5-b78a3b6b6d0f · outbound

This paper cites Siamese neural networks for one-shot image recognition,.

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Siamese neural networks for one-shot image recognition,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:19:34.928034Z

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Observation 8d71ffb0-b099-479e-8941-2bd44e081760 · outbound

This paper cites Relative and absolute location embedding for few-shot node classification on graph,.

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Relative and absolute location embedding for few-shot node classification on graph,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:19:34.906360Z

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Observation ae8cc2f2-5f50-40ec-b1bd-413ea123d6e1 · outbound

This paper cites Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks.

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

Reference 4

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Observation e2f05289-fe2b-40c6-8c34-b4c8e9a8d917 · outbound

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Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Active learning for networked data,

Reference 5

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Observation 0ab398fd-8f4e-4a35-ad96-873d71fa3011 · outbound

This paper cites Few-shot learning with graph neural networks,.

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Few-shot learning with graph neural networks,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:19:34.858373Z

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Observation 32262746-6a40-4a57-a832-70441b029aee · outbound

This paper cites Prototypical networks for few- shot learning,.

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Prototypical networks for few- shot learning,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:19:34.838770Z

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Observation 60e66fbb-f528-478d-93fc-359619cc0b4a · outbound

This paper cites Semi-supervised classification with graph convolutional networks,.

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Semi-supervised classification with graph convolutional networks,

Reference 8

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This paper cites Graph attention networks,.

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Graph attention networks,

Reference 9

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Observation ef1694c2-578c-407a-b684-ae4f38282936 · outbound

This paper cites Inductive representation learning on large graphs,.

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Inductive representation learning on large graphs,

Reference 10

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Observation 0abb9f52-d5e4-49b2-bb1e-7e70d7f95ada · outbound

This paper cites Importance of semantic representation: Dataless classification.

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Importance of semantic representation: Dataless classification

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:19:34.781705Z

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Observation abc20fc2-09c0-4faa-84fb-af73694fd7cf · outbound

This paper cites Graph prototypical networks for few-shot learning on attributed networks,.

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Graph prototypical networks for few-shot learning on attributed networks,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:19:34.762576Z

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Observation 9899ac2b-df9a-4a2f-b28d-9a533077f643 · outbound

This paper cites Few-shot medical image segmentation using a global corre- lation network with discriminative embedding,.

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Few-shot medical image segmentation using a global corre- lation network with discriminative embedding,

Reference 13

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Observation 6001d2a4-9a29-4c33-908a-697bb2a7eb6d · outbound

This paper cites Learning to estimate 6dof pose from limited data: A few-shot, generalizable approach using rgb images,.

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Learning to estimate 6dof pose from limited data: A few-shot, generalizable approach using rgb images,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:19:34.728125Z

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Observation 6cc2790b-79b1-47e9-9d88-c62644a78919 · outbound

This paper cites Learning loss for active learning,.

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Learning loss for active learning,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:19:34.710110Z

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Observation 3ddfe365-50e0-4326-8182-23e86215a0c6 · outbound

This paper cites Human-in-the-loop machine learning: a state of the art,.

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Human-in-the-loop machine learning: a state of the art,

Reference 16

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Observation 5e80d296-0ff4-4af8-9a0d-d471d0fe7987 · outbound

This paper cites Active learning literature survey,.

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Active learning literature survey,

Reference 17

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Observation 48e741c3-5574-448a-8572-6cd42d0c34ef · outbound

This paper cites Unsupervised learning via meta- learning,.

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Unsupervised learning via meta- learning,

Reference 18

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Observation 1f58f743-4e15-483c-ab16-d72bc4992e5c · outbound

This paper cites Diversity helps: Unsupervised few- shot learning via distribution shift-based data augmentation,.

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Diversity helps: Unsupervised few- shot learning via distribution shift-based data augmentation,

Reference 19

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Observation 84250558-c53d-4852-8dc3-12412db9c1a7 · outbound

This paper cites Meal: Stable and active learning for few-shot prompting,.

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Meal: Stable and active learning for few-shot prompting,

Reference 20

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Observation c6fb507c-d87a-44d1-9dbe-6b0beddbc792 · outbound

This paper cites Active learning for graph neural networks via node feature propagation,.

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Active learning for graph neural networks via node feature propagation,

Reference 21

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Observation 1740f011-376b-4ba0-b2f0-d73aa0a4e08c · outbound

This paper cites Dissimilar nodes improve graph active learning,.

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Dissimilar nodes improve graph active learning,

Reference 22

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Observation 8ed16227-f449-4611-81c7-80c3b9beed1f · outbound

This paper cites Improving graph prototypical network using active learning,.

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Improving graph prototypical network using active learning,

Reference 23

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Observation 2226d2a6-1d6e-4c95-a1cd-356aad5282d2 · outbound

This paper cites Cost-effective data labelling for graph neural networks,.

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Cost-effective data labelling for graph neural networks,

Reference 24

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Observation cbda6c96-c6e0-44a2-a98a-c8c767c42442 · outbound

This paper cites Making your first choice: To address cold start problem in vision active learning,.

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Making your first choice: To address cold start problem in vision active learning,

Reference 25

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Observation 3b786772-cfa9-4e2c-bf34-2eedcad06648 · outbound

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Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Cold-start active learning for image classification,

Reference 26

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Observation 768880ac-8a66-4ff7-9974-7dafcff1dae0 · outbound

This paper cites Birch: an efficient data clustering method for very large databases,.

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Birch: an efficient data clustering method for very large databases,

Reference 27

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Observation dc0c372e-3a30-48b9-afb3-b7bde9651677 · outbound

This paper cites Combining label propagation and simple models out-performs graph neural net- works,.

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Combining label propagation and simple models out-performs graph neural net- works,

Reference 28

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Observation d1ba437d-0ac7-482a-bee8-9dfc292076cd · outbound

This paper cites The pagerank citation ranking: bringing order to the web,.

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset The pagerank citation ranking: bringing order to the web,

Reference 29

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Observation ad286be4-3c19-4f97-9cf6-db2b22431e7f · outbound

This paper cites Open-world graph active learning for node classification,.

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Open-world graph active learning for node classification,

Reference 30

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Observation a0d45470-9360-4fbd-8d86-ef74e6e3067d · outbound

This paper cites Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods,.

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods,

Reference 31

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Observation ee8f16f8-dec6-416b-a0e6-f816a40e4f19 · outbound

This paper cites Revisiting semi- supervised learning with graph embeddings,.

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Revisiting semi- supervised learning with graph embeddings,

Reference 32

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Observation 69eebf2b-b55b-4db5-ab31-2fd41ea1f44a · outbound

This paper cites Automating the construction of internet portals with machine learning,.

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Automating the construction of internet portals with machine learning,

Reference 33

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This paper cites Citeseer: An automatic citation indexing system,.

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Citeseer: An automatic citation indexing system,

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Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Collective classification in network data,

Reference 35

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Observation f4dc78f9-caeb-492b-929f-baab2f73bafd · outbound

This paper cites Graph- saint: Graph sampling based inductive learning method,.

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Graph- saint: Graph sampling based inductive learning method,

Reference 36

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Observation 67a38cfc-0b11-49b6-99b6-45b01dd8e92e · outbound

This paper cites Glove: Global vectors for word representation,.

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Glove: Global vectors for word representation,

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Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Open Graph Benchmark: Datasets for Machine Learning on Graphs

Reference 38

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Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Deep Graph Infomax,

Reference 39

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Observation 86e83e56-8759-4c91-8976-98ae45720c85 · outbound

This paper cites Who belongs in the family?.

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Who belongs in the family?

Reference 40

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This paper cites Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time,.

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:19:34.393351Z

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Observation c2d334c1-e60b-4f5a-9231-189e98ae08ab · outbound

This paper cites Adaptive Universal Generalized PageRank Graph Neural Network.

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Adaptive Universal Generalized PageRank Graph Neural Network

Reference 42

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Observation a351259a-bc06-4c64-918c-8b46d1f5840a · outbound

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Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Improving Graph Neural Networks with Simple Architecture Design

Reference 43

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Observation e2e5284c-bb7d-478d-8cf1-523cb8c668d4 · outbound

This paper cites Beyond homophily in graph neural networks: current limitations and effective designs,.

Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset Beyond homophily in graph neural networks: current limitations and effective designs,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:19:34.373205Z

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Pith citing papers

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