Paper Citation Record · LEDGER

Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking

As of 19 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2504.20900.

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

pith.paper-citation-record.v1
2504.20900 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:21:56.358477Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

26 of 26 outbound references displayed

verified exact1
verified fuzzy9
unresolved14
parse uncertain0
malformed identifier1
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d172625e-3833-4dee-9161-1ddd8cece393 · outbound

This paper cites Improved techniques for training GANs,.

Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking Improved techniques for training GANs,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:56.993238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f08b420f-37eb-44f6-94e2-e50b67c0565f · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium,.

Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking Gans trained by a two time-scale update rule converge to a local nash equilibrium,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-16T05:21:56.212076Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e643cdd0-23ab-4827-81c4-8c826d3ecee4 · outbound

This paper cites Pros and cons of GAN evaluation measures,.

Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking Pros and cons of GAN evaluation measures,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:56.964909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 513cdda2-e1ab-46f0-ac63-39dbe0c98e26 · outbound

This paper cites Tab-distillation: Impacts of dataset distillation on tabular data for outlier detection,.

Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking Tab-distillation: Impacts of dataset distillation on tabular data for outlier detection,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:56.947056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation aacb28b5-2ea9-4e6c-864b-60e8be6938db · outbound

This paper cites Fin-Fed-OD: Federated Outlier Detection on Financial Tabular Data.

Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking Fin-Fed-OD: Federated Outlier Detection on Financial Tabular Data

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T05:21:56.234900Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d49f3c5-236f-4f28-a17e-5f685631bc9d · outbound

This paper cites Recol: Reconstruction error columns for outlier detection,.

Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking Recol: Reconstruction error columns for outlier detection,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:56.928305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 443670ba-cad4-47f9-a1e3-64a4fdfa77fe · outbound

This paper cites Data synthesis based on generative adversarial networks,.

Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking Data synthesis based on generative adversarial networks,

Reference 8

Resolution: malformed identifier; no resolver link, observed 2026-08-16T05:21:56.246688Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ba81d98-047f-4a5a-9b44-ab803a4806b8 · outbound

This paper cites Modeling Tabular data using Conditional GAN.

Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking Modeling Tabular data using Conditional GAN

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T05:21:56.264279Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44838532-b975-4af1-b8fd-c3f6cae6052c · outbound

This paper cites The synthetic data vault,.

Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking The synthetic data vault,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:56.907594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b6ddb43c-96d5-4a6a-8df6-90c826644640 · outbound

This paper cites VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data.

Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-16T05:21:56.285186Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f656b72-1006-4ad6-b4bc-fea7b8d62e62 · outbound

This paper cites Generating Multi-label Discrete Patient Records using Generative Adversarial Networks.

Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking Generating Multi-label Discrete Patient Records using Generative Adversarial Networks

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-16T05:21:56.280837Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9fb2fb49-c761-41cf-9cf2-090d6b1cb336 · outbound

This paper cites Tabddpm: Modelling tabular data with diffusion models,.

Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking Tabddpm: Modelling tabular data with diffusion models,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:56.889607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0fa06111-62d0-4bc6-895d-7d2af11a7a82 · outbound

This paper cites Denoising Diffusion Probabilistic Models.

Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking Denoising Diffusion Probabilistic Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T05:21:56.309103Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5bc555a-354e-4e34-8466-2549fe90838a · outbound

This paper cites Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding.

Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-16T05:21:56.314405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c76585ed-c843-4337-b3da-57f95497c83a · outbound

This paper cites Progressive Growing of GANs for Improved Quality, Stability, and Variation.

Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking Progressive Growing of GANs for Improved Quality, Stability, and Variation

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-16T05:21:56.304200Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b50b317-6fb7-4143-9586-c6435953948f · outbound

This paper cites High-Resolution Image Synthesis with Latent Diffusion Models.

Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking High-Resolution Image Synthesis with Latent Diffusion Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-16T05:21:56.324642Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82050296-72d2-4c1f-be08-fb34c853412e · outbound

This paper cites Time-series generative adversarial networks,.

Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking Time-series generative adversarial networks,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:56.872661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5d8f5446-7f60-42d0-96d1-51f424c09f85 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-16T05:21:56.319449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a9f22e4-4dd2-4b6c-afd2-ab7f78957c0c · outbound

This paper cites Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting.

Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-16T05:21:56.343881Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71031320-09b3-445a-8165-3d8b1acfb032 · outbound

This paper cites Cross-domain transformation for outlier detection on tabular datasets,.

Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking Cross-domain transformation for outlier detection on tabular datasets,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T05:21:56.348868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 327e46cc-75fb-42e0-8ed4-04a60185cc67 · outbound

This paper cites Explaining Anomalies using Denoising Autoencoders for Financial Tabular Data.

Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking Explaining Anomalies using Denoising Autoencoders for Financial Tabular Data

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-16T05:21:56.404354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f1282a12-55c5-4f9b-a19b-ecff6258a076 · outbound

This paper cites Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs.

Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-16T05:21:56.339410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5d2ee21-a6d7-4adb-ad0f-5a8a039c4a84 · outbound

This paper cites [Online].

Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking [Online]

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:56.840554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6a0c075c-96e3-4321-b5d4-86fd2b1aac72 · outbound

This paper cites Synthesizing Tabular Data using Generative Adversarial Networks.

Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking Synthesizing Tabular Data using Generative Adversarial Networks

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-16T05:21:56.259111Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c58ded22-93ab-4081-9d36-3d027561a000 · outbound

This paper cites Available: https://proceedings.neurips.cc/paper files/ paper/2019/file/c9efe5f26cd17ba6216bbe2a7d26d490-Paper.pdf.

Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking Available: https://proceedings.neurips.cc/paper files/ paper/2019/file/c9efe5f26cd17ba6216bbe2a7d26d490-Paper.pdf

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:21:56.856585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c3406278-2787-4dce-adc0-f85ef64d0f26 · outbound

This paper cites FinDiff: Diffusion Models for Financial Tabular Data Generation.

Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking FinDiff: Diffusion Models for Financial Tabular Data Generation

Reference 2023

Resolution: metadata mismatch; local_arxiv, observed 2026-08-16T05:21:56.652619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

No inbound Pith citation observations are available.