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Paper Citation Record · LEDGER

Generative Lines Matching Models

As of 20 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 0 inbound Pith citation observations for arXiv:2412.06403.

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

pith.paper-citation-record.v1
2412.06403 v1

Coverage vector

measured 61 of 61 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T19:49:14.808110Z

measured 61 of 61 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

61 of 61 outbound references displayed

  • verified exact1
  • verified fuzzy34
  • unresolved25
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b961f2b4-c954-428c-a1b1-ecb3a4157aae · outbound

This paper cites Albergo and Eric Vanden - Eijnden.

Generative Lines Matching Models Albergo and Eric Vanden - Eijnden

Reference 1

Resolution
verified fuzzy
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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.

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Observation d28a68da-b8da-44ca-80c3-cbfbd5a6fb3a · outbound

This paper cites Albergo and Eric Vanden - Eijnden.

Generative Lines Matching Models Albergo and Eric Vanden - Eijnden

Reference 2

Resolution
verified fuzzy
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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.

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Observation 44654307-b949-4eee-bc50-db2f3f13ce97 · outbound

This paper cites Near-linear time approximation algorithms for optimal transport via sinkhorn iteration.

Generative Lines Matching Models Near-linear time approximation algorithms for optimal transport via sinkhorn iteration

Reference 3

Resolution
verified fuzzy
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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.

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Observation 51b91a4e-4a27-48ed-a8fe-ce46a2034d2b · outbound

This paper cites Beyer, Jonathan Goldstein, Raghu Ramakrishnan, and Uri Shaft.

Generative Lines Matching Models Beyer, Jonathan Goldstein, Raghu Ramakrishnan, and Uri Shaft

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:49:15.548850Z

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.

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Observation fe435b6f-00a1-46f8-88d7-5ad31723b8b8 · outbound

This paper cites Large scale GAN training for high fidelity natural image synthesis.

Generative Lines Matching Models Large scale GAN training for high fidelity natural image synthesis

Reference 5

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation b3d700ea-81c7-4752-9e1a-68134a6ba7a8 · outbound

This paper cites Weiss, Mohammad Norouzi, and William Chan.

Generative Lines Matching Models Weiss, Mohammad Norouzi, and William Chan

Reference 6

Resolution
verified fuzzy
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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.

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Observation 157246ed-f7fc-42ce-81e5-16a9f5dc10d3 · outbound

This paper cites an unresolved cited work.

Generative Lines Matching Models Unresolved cited work

Reference 7

Resolution
unresolved
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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.

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Observation 18ad9da3-d1dd-4ea9-9fa3-b74f506d6061 · outbound

This paper cites Clarke and Stephen Van Gorder.

Generative Lines Matching Models Clarke and Stephen Van Gorder

Reference 8

Resolution
verified exact
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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.

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Observation 7b74ef26-933b-477e-b3d6-d9478c3049f3 · outbound

This paper cites Diffusion models beat gans on image synthesis.

Generative Lines Matching Models Diffusion models beat gans on image synthesis

Reference 9

Resolution
verified fuzzy
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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.

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Observation eb2f1d53-242e-4838-b3bd-d7f1c7f648bf · outbound

This paper cites Taming transformers for high-resolution image synthesis.

Generative Lines Matching Models Taming transformers for high-resolution image synthesis

Reference 10

Resolution
verified fuzzy
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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.

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Observation 1df9cfe3-e8da-4ef6-88a7-df51d973150e · outbound

This paper cites Alaya, Aur\' e lie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, L\' e o Gautheron, Nathalie T.H.

Generative Lines Matching Models Alaya, Aur\' e lie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, L\' e o Gautheron, Nathalie T.H

Reference 11

Resolution
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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.

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Observation fe1aeff1-1f77-4d66-bf8e-60f28e627b67 · outbound

This paper cites Generative adversarial nets.

Generative Lines Matching Models Generative adversarial nets

Reference 12

Resolution
verified fuzzy
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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.

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Observation 1b79cce9-febf-4057-a1a8-7f44bfe69642 · outbound

This paper cites Susskind.

Generative Lines Matching Models Susskind

Reference 13

Resolution
verified fuzzy
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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.

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Observation 8bbafd84-5ea9-4a6f-abde-424217cbe143 · outbound

This paper cites Denoising diffusion probabilistic models.

Generative Lines Matching Models Denoising diffusion probabilistic models

Reference 14

Resolution
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no resolver link, observed 2026-08-11T19:49:14.648203Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation b480437f-ce5b-4912-9bb8-4b26162a2aca · outbound

This paper cites Video Diffusion Models.

Generative Lines Matching Models Video Diffusion Models

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-11T19:49:14.652002Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 45c6f078-1792-45be-be11-66b99403d8e6 · outbound

This paper cites Fleet, and Ting Chen.

Generative Lines Matching Models Fleet, and Ting Chen

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:49:15.452368Z

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.

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Observation 30569300-9770-406c-8c54-e5bdab3df4e7 · outbound

This paper cites Perceptual losses for real-time style transfer and super-resolution.

Generative Lines Matching Models Perceptual losses for real-time style transfer and super-resolution

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:49:15.442128Z

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.

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Observation 21a3c0e6-8b0b-4671-af65-7b66284eafb0 · outbound

This paper cites Rebooting acgan: auxiliary classifier gans with stable training.

Generative Lines Matching Models Rebooting acgan: auxiliary classifier gans with stable training

Reference 18

Resolution
verified fuzzy
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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.

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Observation ef2984fd-21fd-49e8-a7a0-b77cfafc13ab · outbound

This paper cites Training generative adversarial networks with limited data.

Generative Lines Matching Models Training generative adversarial networks with limited data

Reference 19

Resolution
verified fuzzy
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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.

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Observation 5ed3c913-502e-4ad6-a507-cd33245f09e3 · outbound

This paper cites Elucidating the design space of diffusion-based generative models.

Generative Lines Matching Models Elucidating the design space of diffusion-based generative models

Reference 20

Resolution
verified fuzzy
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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.

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Observation 4b16472a-a246-4e3c-b347-0f6bf374feb3 · outbound

This paper cites Kendall and A.

Generative Lines Matching Models Kendall and A

Reference 21

Resolution
verified fuzzy
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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.

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Observation b1307b3f-35de-4667-b590-26f9f239eb64 · outbound

This paper cites Consistency trajectory models: Learning probability flow ODE trajectory of diffusion.

Generative Lines Matching Models Consistency trajectory models: Learning probability flow ODE trajectory of diffusion

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-11T19:49:14.674557Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 918793ce-50ec-49ad-b1c1-6dba3203e951 · outbound

This paper cites Guided-tts: A diffusion model for text-to-speech via classifier guidance.

Generative Lines Matching Models Guided-tts: A diffusion model for text-to-speech via classifier guidance

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:49:15.385089Z

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.

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Observation 3433b7a7-09a0-4a57-b8ec-e9d7f770ca5c · outbound

This paper cites Auto-Encoding Variational Bayes.

Generative Lines Matching Models Auto-Encoding Variational Bayes

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-11T19:49:14.681908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T19:49:14.681908Z digest=sha256:f664e92806d8601ff46a943dc3505e719ebbfb714924e8e187265ae79d70efe1

Observation 1ecafc23-d1f1-4c28-990f-6c33cd05b663 · outbound

This paper cites Diffwave: A versatile diffusion model for audio synthesis, 2021.

Generative Lines Matching Models Diffwave: A versatile diffusion model for audio synthesis, 2021

Reference 25

Resolution
verified fuzzy
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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.

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Observation e2c44ab1-0450-49c5-a062-a44df2f44291 · outbound

This paper cites Minimizing trajectory curvature of ODE -based generative models.

Generative Lines Matching Models Minimizing trajectory curvature of ODE -based generative models

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:49:15.365232Z

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.

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Observation 34b3dd03-4dea-418a-ad8b-f9a9ab26c5a5 · outbound

This paper cites an unresolved cited work.

Generative Lines Matching Models Unresolved cited work

Reference 27

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 1af44f84-b581-4c0b-a916-20cb67554d33 · outbound

This paper cites Flow straight and fast: Learning to generate and transfer data with rectified flow.

Generative Lines Matching Models Flow straight and fast: Learning to generate and transfer data with rectified flow

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:49:15.348254Z

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.

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Observation 5d54f623-539e-4ab4-8143-a1e924c81e2d · outbound

This paper cites Instaflow: One step is enough for high-quality diffusion-based text-to-image generation.

Generative Lines Matching Models Instaflow: One step is enough for high-quality diffusion-based text-to-image generation

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:49:15.337725Z

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.

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Observation 6ed02848-1d8d-4ea7-9467-884d0c7b9b5f · outbound

This paper cites Sora: A review on background, technology, limitations, and opportunities of large vision models, 2024 b.

Generative Lines Matching Models Sora: A review on background, technology, limitations, and opportunities of large vision models, 2024 b

Reference 30

Resolution
verified fuzzy
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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.

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Observation 99be0db6-55f3-43af-9764-b4f78d621b37 · outbound

This paper cites CM - GAN : Stabilizing GAN training with consistency models.

Generative Lines Matching Models CM - GAN : Stabilizing GAN training with consistency models

Reference 31

Resolution
verified fuzzy
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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.

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Observation 52e4ef20-d0ce-41a4-bdfa-49395a41b172 · outbound

This paper cites Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed.

Generative Lines Matching Models Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed

Reference 32

Resolution
unresolved
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Source-reported events for the cited work

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Observation 19c22a77-007d-4672-af59-c55e8a52b6f5 · outbound

This paper cites Improved denoising diffusion probabilistic models.

Generative Lines Matching Models Improved denoising diffusion probabilistic models

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-11T19:49:14.713941Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T19:49:14.713941Z digest=sha256:56b6bfcb4f67095969b27ca753f1e2ec3c6b66f182a0bd5690606c7530a6b4c9

Observation 07abab2a-ae3a-433e-bc67-4c873046ad9e · outbound

This paper cites GLIDE: towards photorealistic image generation and editing with text-guided diffusion models.

Generative Lines Matching Models GLIDE: towards photorealistic image generation and editing with text-guided diffusion models

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:49:15.302016Z

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.

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Observation 389c2587-35b0-44d6-8156-c7f14efd74c2 · outbound

This paper cites an unresolved cited work.

Generative Lines Matching Models Unresolved cited work

Reference 35

Resolution
unresolved
raw_fallback, observed 2026-08-11T19:49:15.289452Z

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.

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Observation 99cdcbcf-75b1-4e3a-9a3e-6889c6cacf73 · outbound

This paper cites Grad-tts: A diffusion probabilistic model for text-to-speech, 2021.

Generative Lines Matching Models Grad-tts: A diffusion probabilistic model for text-to-speech, 2021

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:49:15.279241Z

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.

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Observation 746ef58b-39ba-4c0c-bb37-ee293cc33410 · outbound

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

Generative Lines Matching Models Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 37

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 57eadc25-513d-4dd7-83f2-4c88aa426992 · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

Generative Lines Matching Models High-resolution image synthesis with latent diffusion models

Reference 38

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Observation 24727a1c-9e20-4875-9a95-c900f48fa347 · outbound

This paper cites Sara Mahdavi, Raphael Gontijo-Lopes, Tim Salimans, Jonathan Ho, David J Fleet, and Mohammad Norouzi.

Generative Lines Matching Models Sara Mahdavi, Raphael Gontijo-Lopes, Tim Salimans, Jonathan Ho, David J Fleet, and Mohammad Norouzi

Reference 39

Resolution
verified fuzzy
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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.

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Observation f863257e-0cb8-41e6-9a11-736d5d411525 · outbound

This paper cites Progressive distillation for fast sampling of diffusion models.

Generative Lines Matching Models Progressive distillation for fast sampling of diffusion models

Reference 40

Resolution
verified fuzzy
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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.

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Observation 0303f6af-1e44-4f72-8a10-1576b29a7877 · outbound

This paper cites Stylegan-xl: Scaling stylegan to large diverse datasets.

Generative Lines Matching Models Stylegan-xl: Scaling stylegan to large diverse datasets

Reference 41

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Observation 9557a7a4-abad-4b91-bec9-9869fbba3907 · outbound

This paper cites Make-a-video: Text-to-video generation without text-video data.

Generative Lines Matching Models Make-a-video: Text-to-video generation without text-video data

Reference 42

Resolution
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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.

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Observation e381af41-630c-4052-bd59-a72e5be45ed3 · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

Generative Lines Matching Models Deep unsupervised learning using nonequilibrium thermodynamics

Reference 43

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Source-reported events for the cited work

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Observation f0f2c41a-42a7-4dbb-9af4-3e763c8aba7f · outbound

This paper cites Denoising diffusion implicit models.

Generative Lines Matching Models Denoising diffusion implicit models

Reference 44

Resolution
verified fuzzy
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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.

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Observation 620d2da6-2f6f-45ca-bc29-04c44e5e0283 · outbound

This paper cites Improved techniques for training consistency models.

Generative Lines Matching Models Improved techniques for training consistency models

Reference 45

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Source-reported events for the cited work

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Observation 6d869ea9-94b5-4008-ad3d-0209cbf6bfae · outbound

This paper cites Generative modeling by estimating gradients of the data distribution.

Generative Lines Matching Models Generative modeling by estimating gradients of the data distribution

Reference 46

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Observation f96d0289-e33d-4493-9a99-00a7442f2888 · outbound

This paper cites Improved techniques for training score-based generative models.

Generative Lines Matching Models Improved techniques for training score-based generative models

Reference 47

Resolution
verified fuzzy
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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.

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Observation be82ca48-5b56-4bad-be21-ca3ff3b00123 · outbound

This paper cites Sliced score matching: A scalable approach to density and score estimation.

Generative Lines Matching Models Sliced score matching: A scalable approach to density and score estimation

Reference 48

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verified fuzzy
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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.

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Observation c6eeb4e7-b908-4979-a496-fc6f9cc97501 · outbound

This paper cites Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole.

Generative Lines Matching Models Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole

Reference 49

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verified fuzzy
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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.

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Observation 5b958f10-09d7-4cb2-bb42-099e6fbb414c · outbound

This paper cites Consistency models.

Generative Lines Matching Models Consistency models

Reference 50

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Observation f6ba4d79-9490-4959-a776-97bc02d42aec · outbound

This paper cites Catastrophic forgetting and mode collapse in gans.

Generative Lines Matching Models Catastrophic forgetting and mode collapse in gans

Reference 51

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Observation d66d272e-7a82-42ca-b953-f00bf60d0ae9 · outbound

This paper cites Improving and generalizing flow-based generative models with minibatch optimal transport.

Generative Lines Matching Models Improving and generalizing flow-based generative models with minibatch optimal transport

Reference 52

Resolution
verified fuzzy
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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.

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Observation 49ceea11-e6ef-4b61-a9e9-e2b3ae223930 · outbound

This paper cites A connection between score matching and denoising autoencoders.

Generative Lines Matching Models A connection between score matching and denoising autoencoders

Reference 53

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source=arxiv_source observed=2026-08-11T19:49:14.782658Z digest=sha256:a195d6e3be7b5842b824c7e822b13d69b5e655ac0daf3a09b49ce80bebb38953

Observation bead5f03-1983-4801-bfdb-6c6023d5fb71 · outbound

This paper cites One-step diffusion with distribution matching distillation.

Generative Lines Matching Models One-step diffusion with distribution matching distillation

Reference 54

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source=arxiv_source observed=2026-08-11T19:49:14.785320Z digest=sha256:6e8f36be2642957cdfd8a186ce2dc91683114482288241a819eec17cc4d9f17f

Observation b8efee08-e521-492a-ac68-efbe0a363ffa · outbound

This paper cites The unreasonable effectiveness of deep features as a perceptual metric.

Generative Lines Matching Models The unreasonable effectiveness of deep features as a perceptual metric

Reference 55

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unresolved
no resolver link, observed 2026-08-11T19:49:14.788122Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-11T19:49:14.788122Z digest=sha256:07ccd8b00198819b95302c10328f6c3fc75f081e70f7797940a15b6b88954afb

Observation 29de9c22-54f5-4912-8a19-62eca1478848 · outbound

This paper cites Differentiable augmentation for data-efficient gan training.

Generative Lines Matching Models Differentiable augmentation for data-efficient gan training

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:49:15.133085Z

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.

source=arxiv_source observed=2026-08-11T19:49:14.790921Z digest=sha256:07e2fc88364b9c9875f9860afe51667ceb1584912374c9ea25a2c2a775143046

Observation 3d95bf17-b0fb-41b4-97a3-993a5c8e67fd · outbound

This paper cites Score identity Distillation: Exponentially Fast Distillation of Pretrained Diffusion Models for One-Step Generation.

Generative Lines Matching Models Score identity Distillation: Exponentially Fast Distillation of Pretrained Diffusion Models for One-Step Generation

Reference 57

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Observation 12ac80b3-b9af-41d6-8af2-de5419b305a4 · outbound

This paper cites write newline.

Generative Lines Matching Models write newline

Reference 58

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Observation ca02d9df-6f79-45e1-8107-ccc1b48e2a9c · outbound

This paper cites @esa (Ref.

Generative Lines Matching Models @esa (Ref

Reference 59

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source=arxiv_source observed=2026-08-11T19:49:14.801230Z digest=sha256:ea352472efde665e32b96b928c109481171cb7c2aab5e5c39cb39b9ce218318a

Observation dd208fae-dbb9-434b-836b-1acd1f38a340 · outbound

This paper cites an unresolved cited work.

Generative Lines Matching Models Unresolved cited work

Reference 60

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source=arxiv_source observed=2026-08-11T19:49:14.804998Z digest=sha256:8e9ebc21b29d23d0757a432ebafa5f93cc5a0eaea1fb5a229478d9db080e3ec3

Observation cbb57a79-b130-434b-8b2e-b9993a4bb528 · outbound

This paper cites We used the same network architecture and hyper-parameters as existing models, with all the implementation details provided in Appendix append:impl.

Generative Lines Matching Models We used the same network architecture and hyper-parameters as existing models, with all the implementation details provided in Appendix append:impl

Reference 61

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raw_fallback, observed 2026-08-11T19:49:15.101770Z

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.

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Pith citing papers

No inbound Pith citation observations are available.