Paper Citation Record · LEDGER

Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction

As of 7 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2507.06538.

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

pith.paper-citation-record.v1
2507.06538 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:08:28.521321Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

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

30 of 30 outbound references displayed

verified exact0
verified fuzzy20
unresolved10
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation dd15d230-0374-4cee-bb92-8cfd857e1ce3 · outbound

This paper cites Generalizing from a few examples: A survey on few-shot learning,.

Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction Generalizing from a few examples: A survey on few-shot learning,

Reference 1

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Source-reported events for the cited work

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Observation 4dce347e-07b6-4b7f-87ba-a6a0346dab9f · outbound

This paper cites Pretraining graph neural networks for few-shot analog circuit modeling and design,.

Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction Pretraining graph neural networks for few-shot analog circuit modeling and design,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:08:34.121492Z

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Observation 565a88e5-3c16-420f-b789-29be30f3ef48 · outbound

This paper cites Yu and X.

Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction Yu and X

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:08:33.822100Z

Source-reported events for the cited work

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Observation e28377cf-ad84-41c3-b40d-a44e191b2b44 · outbound

This paper cites Variational capacitance extraction of on- chip interconnects based on continuous surface model,.

Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction Variational capacitance extraction of on- chip interconnects based on continuous surface model,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:08:33.519730Z

Source-reported events for the cited work

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Observation ab34f8ee-3826-436d-bdae-dc4bd8ca0cc0 · outbound

This paper cites Link prediction based on graph neural networks,.

Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction Link prediction based on graph neural networks,

Reference 5

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Observation fd072b60-650e-48dc-9847-850cfb700989 · outbound

This paper cites Recipe for a General, Powerful, Scalable Graph Trans- former,.

Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction Recipe for a General, Powerful, Scalable Graph Trans- former,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:08:33.155048Z

Source-reported events for the cited work

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Observation df5b2e37-68cc-46cb-910b-27f0fbfe9585 · outbound

This paper cites Optimization as a model for few-shot learning,.

Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction Optimization as a model for few-shot learning,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:08:32.903619Z

Source-reported events for the cited work

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Observation f566a85e-0453-43a5-ab74-af96ec683f08 · outbound

This paper cites Weisfeiler-lehman neural machine for link prediction,.

Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction Weisfeiler-lehman neural machine for link prediction,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:08:32.577227Z

Source-reported events for the cited work

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Observation 52c013e3-e086-48dc-8197-3ef04dbfb0e5 · outbound

This paper cites Link prediction in complex networks: A survey,.

Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction Link prediction in complex networks: A survey,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:08:32.302912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3b4f26f5-2fc9-4b3f-babb-3cb84fa055d9 · outbound

This paper cites AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processing.

Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processing

Reference 10

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Source-reported events for the cited work

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Observation bfc8332f-30ed-4919-ae56-67b4439eac56 · outbound

This paper cites A survey on vision transformer,.

Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction A survey on vision transformer,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:08:32.004526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8e76adc8-234c-406d-8e58-fd0d8959a99b · outbound

This paper cites A Generalization of Transformer Networks to Graphs.

Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction A Generalization of Transformer Networks to Graphs

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T19:08:26.880163Z

Source-reported events for the cited work

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Observation 201603e2-5ad2-4235-955f-54ae67431db5 · outbound

This paper cites Rethinking graph transformers with spectral attention,.

Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction Rethinking graph transformers with spectral attention,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:08:31.688399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5b728b75-0305-420d-9622-0512dfa77b67 · outbound

This paper cites Do transformers really perform badly for graph representation?.

Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction Do transformers really perform badly for graph representation?

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:08:31.344755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bd342c28-d06a-4633-a941-af6ae5921273 · outbound

This paper cites Graph neural networks with learnable structural and positional repre- sentations,.

Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction Graph neural networks with learnable structural and positional repre- sentations,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:08:31.006859Z

Source-reported events for the cited work

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Observation b724473f-5bb7-4def-9a46-495c9aee56ca · outbound

This paper cites Directional graph networks,.

Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction Directional graph networks,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:08:30.743767Z

Source-reported events for the cited work

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Observation d48748c6-65ed-48f2-9920-e98d64d301bc · outbound

This paper cites Understanding over-squashing and bottlenecks on graphs via curvature.

Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction Understanding over-squashing and bottlenecks on graphs via curvature

Reference 17

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Source-reported events for the cited work

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Observation 00240ab6-b7f2-4144-b8f9-698118bd09eb · outbound

This paper cites ParaGraph: Layout parasitics and device parameter prediction using graph neural networks,.

Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction ParaGraph: Layout parasitics and device parameter prediction using graph neural networks,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:08:30.469065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation adca4073-814e-4baf-8869-b064ff1cc553 · outbound

This paper cites Deep-learning- based pre-layout parasitic capacitance prediction on sram designs,.

Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction Deep-learning- based pre-layout parasitic capacitance prediction on sram designs,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:08:30.227509Z

Source-reported events for the cited work

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Observation 4a11af24-e02e-48cb-9ecd-596154e48acb · outbound

This paper cites Rethinking attention with performers,.

Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction Rethinking attention with performers,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:08:29.972548Z

Source-reported events for the cited work

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Observation 4538745d-77c0-48ba-aa57-2972ecc5c03c · outbound

This paper cites Benchmarking Graph Neural Networks.

Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction Benchmarking Graph Neural Networks

Reference 21

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Source-reported events for the cited work

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Observation 6c12d182-7311-4ef4-ac41-e8afaaad211e · outbound

This paper cites Comparing Graph Transformers via Positional Encodings.

Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction Comparing Graph Transformers via Positional Encodings

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T19:08:27.974394Z

Source-reported events for the cited work

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Observation 2f453e90-8cff-4b7c-8b00-8f289bdabda7 · outbound

This paper cites TS cache: A fast cache with timing-speculation mechanism under low supply voltages,.

Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction TS cache: A fast cache with timing-speculation mechanism under low supply voltages,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:08:29.697164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8e25c259-e5ca-4092-bc11-7c7eeda6198a · outbound

This paper cites Structural information enhanced graph representation for link prediction,.

Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction Structural information enhanced graph representation for link prediction,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:08:29.437275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation aacc6892-2845-49af-ad96-e9aa4717a3a2 · outbound

This paper cites Residual Gated Graph ConvNets.

Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction Residual Gated Graph ConvNets

Reference 25

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Source-reported events for the cited work

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Observation 7e830c07-6ab4-4b01-a888-c793b861daef · outbound

This paper cites Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification.

Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification

Reference 26

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Observation f4f366a1-cc61-413b-9a37-9ebd466bc22c · outbound

This paper cites Fast graph representation learning with PyTorch Geometric,.

Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction Fast graph representation learning with PyTorch Geometric,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T19:08:28.310234Z

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Observation 6e7ed161-9ec5-4db5-b4b3-c83f5653cd1c · outbound

This paper cites Design space for graph neural networks,.

Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction Design space for graph neural networks,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:08:29.145681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4f35143f-8955-4fb9-b3d3-7695e99120c4 · outbound

This paper cites Ultra8t: A sub-threshold 8t sram with leakage detection,.

Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction Ultra8t: A sub-threshold 8t sram with leakage detection,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:08:28.846352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 55bc8552-9bfb-4cb6-863c-669211bf1dd5 · outbound

This paper cites 24.4 sandwich-RAM: An energy-efficient in-memory BWN architecture with pulse-width modulation,.

Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction 24.4 sandwich-RAM: An energy-efficient in-memory BWN architecture with pulse-width modulation,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:08:28.707354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

No inbound Pith citation observations are available.