Paper Citation Record · LEDGER

ReFormer: Generating Radio Fakes for Data Augmentation

As of 21 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2501.00282.

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

pith.paper-citation-record.v1
2501.00282 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:58:38.528626Z

measured 19 of 19 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 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

19 of 19 outbound references displayed

verified exact2
verified fuzzy11
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 344a58d7-97f2-4ec1-b4ad-03be1c3cbd10 · outbound

This paper cites Mimo channel estimation using score-based generative models,.

ReFormer: Generating Radio Fakes for Data Augmentation Mimo channel estimation using score-based generative models,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:58:39.517904Z

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 e1458431-1ec1-40c6-954a-ee1017be846c · outbound

This paper cites Score-based generative models for robust channel estimation,.

ReFormer: Generating Radio Fakes for Data Augmentation Score-based generative models for robust channel estimation,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:58:39.503498Z

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 d93befe5-79ad-45a4-abd6-a075f0618ce2 · outbound

This paper cites Generative AI-Based Probabilis- tic Constellation Shaping With Diffusion Models,.

ReFormer: Generating Radio Fakes for Data Augmentation Generative AI-Based Probabilis- tic Constellation Shaping With Diffusion Models,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:58:39.378032Z

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 14ca2a6e-0a68-4d4c-af28-8f67160b5846 · outbound

This paper cites Radio Generation Using Generative Adversarial Networks with An Unrolled Design.

ReFormer: Generating Radio Fakes for Data Augmentation Radio Generation Using Generative Adversarial Networks with An Unrolled Design

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:58:38.799762Z

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 bcfe8dcd-5e8b-4342-810c-25fb0669c92b · outbound

This paper cites High dimensional channel estimation using deep generative networks,.

ReFormer: Generating Radio Fakes for Data Augmentation High dimensional channel estimation using deep generative networks,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:58:39.329965Z

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 88fa5225-c733-4fa0-9b8f-38c0dd98e457 · outbound

This paper cites Tire-gan: Task- incentivized generative learning models for radiomap estimation with radio propagation model,.

ReFormer: Generating Radio Fakes for Data Augmentation Tire-gan: Task- incentivized generative learning models for radiomap estimation with radio propagation model,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:58:39.317527Z

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 23de8a84-b7e7-4612-b2e3-1b616fc02b31 · outbound

This paper cites Large Scale Radio Frequency Signal Classification.

ReFormer: Generating Radio Fakes for Data Augmentation Large Scale Radio Frequency Signal Classification

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T22:58:38.310784Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c35b6bac-1c0d-43d4-9f8a-c5238b909fdd · outbound

This paper cites Can We Learn to Compress RF Signals?.

ReFormer: Generating Radio Fakes for Data Augmentation Can We Learn to Compress RF Signals?

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:58:39.304047Z

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 2fc0e2b2-9b61-4ce5-9600-fcc48c5bd146 · outbound

This paper cites Deep-Learned Compression for Radio-Frequency Signal Classification,.

ReFormer: Generating Radio Fakes for Data Augmentation Deep-Learned Compression for Radio-Frequency Signal Classification,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:58:39.258322Z

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 d3b6ddd1-fbd5-46ed-9c88-24eca5844421 · outbound

This paper cites Improving language understanding by generative pre-training,.

ReFormer: Generating Radio Fakes for Data Augmentation Improving language understanding by generative pre-training,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:58:39.161418Z

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 c85f73f5-6c8d-4efc-a907-4af0000e017e · outbound

This paper cites Neural discrete representation learning,.

ReFormer: Generating Radio Fakes for Data Augmentation Neural discrete representation learning,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T22:58:38.324872Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10f65ead-e383-498b-8075-b746b8fb779b · outbound

This paper cites Continuous Relaxation Training of Discrete Latent Variable Image Models,.

ReFormer: Generating Radio Fakes for Data Augmentation Continuous Relaxation Training of Discrete Latent Variable Image Models,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:58:39.140018Z

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 ffb4d790-3b5d-4327-8df2-09d4b8f6dbd5 · outbound

This paper cites Generating diverse high- fidelity images with VQ-V AE-2,.

ReFormer: Generating Radio Fakes for Data Augmentation Generating diverse high- fidelity images with VQ-V AE-2,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:58:39.010703Z

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 be5732ae-d9ca-48f7-b1ea-e7f720a798eb · outbound

This paper cites Straightening out the straight-through estimator: Overcoming optimization challenges in vector quantized networks,.

ReFormer: Generating Radio Fakes for Data Augmentation Straightening out the straight-through estimator: Overcoming optimization challenges in vector quantized networks,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T22:58:38.359915Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40051a06-9d89-4de8-9198-0092dc949310 · outbound

This paper cites SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization.

ReFormer: Generating Radio Fakes for Data Augmentation SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T22:58:38.434664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a65c530-3a71-4309-9ec0-5d4893324cfd · outbound

This paper cites Attention is all you need,.

ReFormer: Generating Radio Fakes for Data Augmentation Attention is all you need,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T22:58:38.486772Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ca50edd-3605-4262-87f7-58bae1cd8d57 · outbound

This paper cites NG Video Lecture,.

ReFormer: Generating Radio Fakes for Data Augmentation NG Video Lecture,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:58:38.904771Z

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 3f49225a-1187-4cd6-a9c9-88daefef9835 · outbound

This paper cites Generative AI for Medical Imaging: extending the MONAI Framework.

ReFormer: Generating Radio Fakes for Data Augmentation Generative AI for Medical Imaging: extending the MONAI Framework

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T22:58:38.522635Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20a32681-0dbb-439f-b33d-240aea6c58cb · outbound

This paper cites TopP&R: Robust Support Estimation Approach for Evaluating Fidelity and Diversity in Generative Models.

ReFormer: Generating Radio Fakes for Data Augmentation TopP&R: Robust Support Estimation Approach for Evaluating Fidelity and Diversity in Generative Models

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:58:38.610461Z

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.

Pith citing papers

No inbound Pith citation observations are available.