Paper Citation Record · LEDGER

Normalizing Flows are Capable Generative Models

As of 14 August 2026, this Paper Citation Record lists 71 of 71 outbound references and 28 inbound Pith citation observations for arXiv:2412.06329.

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

pith.paper-citation-record.v1
2412.06329 v3

Coverage vector

measured 71 of 71 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T19:52:21.687984Z

measured 99 of 99 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 28 of 28 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T14:48:00.534830Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

71 of 71 outbound references displayed

verified exact1
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unresolved39
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External citation measurements

4
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 1fdc9151-b169-4b59-9372-2666f821eec8 · outbound

This paper cites write newline.

Normalizing Flows are Capable Generative Models write newline

Reference 1

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Observation 483cb4ca-89e2-4e22-a25e-3c0fd6484f6b · outbound

This paper cites Neural Flow Diffusion Models: Learnable Forward Process for Improved Diffusion Modelling.

Normalizing Flows are Capable Generative Models Neural Flow Diffusion Models: Learnable Forward Process for Improved Diffusion Modelling

Reference 2

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Observation bd676eef-b985-43d8-8607-ac91b97841ba · outbound

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

Normalizing Flows are Capable Generative Models Large scale GAN training for high fidelity natural image synthesis

Reference 3

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Normalizing Flows are Capable Generative Models Unresolved cited work

Reference 4

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Observation 260fb794-262e-47db-b8c9-262ec66485ca · outbound

This paper cites D., Aziz, W., and Titov, I.

Normalizing Flows are Capable Generative Models D., Aziz, W., and Titov, I

Reference 5

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Observation 58e6feb8-7b69-43d5-b6e3-bc8afd5a4a7a · outbound

This paper cites Instance-conditioned GAN.

Normalizing Flows are Capable Generative Models Instance-conditioned GAN

Reference 6

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Observation b1f56d19-b7b6-409f-ac44-2cea60e8356d · outbound

This paper cites Go with the flow: Adaptive control for neural odes.

Normalizing Flows are Capable Generative Models Go with the flow: Adaptive control for neural odes

Reference 7

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Observation f4aef972-96d2-4345-99b2-4541eae8be0d · outbound

This paper cites Generative pretraining from pixels.

Normalizing Flows are Capable Generative Models Generative pretraining from pixels

Reference 8

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Observation cfd4146f-4d93-4025-a1d4-96775a815ec9 · outbound

This paper cites M., and Zhai, S.

Normalizing Flows are Capable Generative Models M., and Zhai, S

Reference 9

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Observation fa73093d-cf56-4518-9b87-9ea54170f76e · outbound

This paper cites Q., Rubanova, Y., Bettencourt, J., and Duvenaud, D.

Normalizing Flows are Capable Generative Models Q., Rubanova, Y., Bettencourt, J., and Duvenaud, D

Reference 10

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Observation b79161a9-18ae-4937-a2f2-e8493569a9da · outbound

This paper cites Very deep vaes generalize autoregressive models and can outperform them on images.

Normalizing Flows are Capable Generative Models Very deep vaes generalize autoregressive models and can outperform them on images

Reference 11

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Observation 1c9a96f9-2bf9-4a69-a31f-151983a0461b · outbound

This paper cites Generating Long Sequences with Sparse Transformers.

Normalizing Flows are Capable Generative Models Generating Long Sequences with Sparse Transformers

Reference 12

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Observation 746819fa-a8df-40bc-938e-127c08889eb9 · outbound

This paper cites Stargan v2: Diverse image synthesis for multiple domains.

Normalizing Flows are Capable Generative Models Stargan v2: Diverse image synthesis for multiple domains

Reference 13

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This paper cites Imagenet: A large-scale hierarchical image database.

Normalizing Flows are Capable Generative Models Imagenet: A large-scale hierarchical image database

Reference 14

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This paper cites and Nichol, A.

Normalizing Flows are Capable Generative Models and Nichol, A

Reference 15

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Observation 110814d3-f7be-4e29-98fb-855a7b865c47 · outbound

This paper cites Nice: Non-linear independent components estimation.

Normalizing Flows are Capable Generative Models Nice: Non-linear independent components estimation

Reference 16

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Observation 4f9f611e-cb03-480d-b91a-e60c4cd79141 · outbound

This paper cites Density estimation using real NVP.

Normalizing Flows are Capable Generative Models Density estimation using real NVP

Reference 17

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Observation 5b3ea817-d9f8-4126-acb6-5ac8108d6319 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Normalizing Flows are Capable Generative Models An image is worth 16x16 words: Transformers for image recognition at scale

Reference 18

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Normalizing Flows are Capable Generative Models Unresolved cited work

Reference 19

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Observation c2e8d4d5-a143-4026-b9aa-da0f27e3771f · outbound

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

Normalizing Flows are Capable Generative Models Taming transformers for high-resolution image synthesis

Reference 20

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Observation eb38ded7-c87c-46f3-9994-c78874a4056a · outbound

This paper cites MADE: masked autoencoder for distribution estimation.

Normalizing Flows are Capable Generative Models MADE: masked autoencoder for distribution estimation

Reference 21

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This paper cites J., Pouget - Abadie, J., Mirza, M., Xu, B., Warde - Farley, D., Ozair, S., Courville, A.

Normalizing Flows are Capable Generative Models J., Pouget - Abadie, J., Mirza, M., Xu, B., Warde - Farley, D., Ozair, S., Courville, A

Reference 22

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Reference 23

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Observation b04677bd-4611-486a-84f8-88544bb27a9a · outbound

This paper cites DART: Denoising Autoregressive Transformer for Scalable Text-to-Image Generation.

Normalizing Flows are Capable Generative Models DART: Denoising Autoregressive Transformer for Scalable Text-to-Image Generation

Reference 24

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Observation c8953564-cce3-4a55-b07b-91bd33e037c5 · outbound

This paper cites Classifier-Free Diffusion Guidance.

Normalizing Flows are Capable Generative Models Classifier-Free Diffusion Guidance

Reference 25

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This paper cites Flow++: Improving flow-based generative models with variational dequantization and architecture design.

Normalizing Flows are Capable Generative Models Flow++: Improving flow-based generative models with variational dequantization and architecture design

Reference 26

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Normalizing Flows are Capable Generative Models Denoising diffusion probabilistic models

Reference 27

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Observation 7ed04050-f71e-4588-9cc5-e5ecc3e296b7 · outbound

This paper cites J., Norouzi, M., and Salimans, T.

Normalizing Flows are Capable Generative Models J., Norouzi, M., and Salimans, T

Reference 28

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This paper cites simple diffusion: End-to-end diffusion for high resolution images.

Normalizing Flows are Capable Generative Models simple diffusion: End-to-end diffusion for high resolution images

Reference 29

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Reference 30

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Reference 31

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This paper cites J., and Chen, T.

Normalizing Flows are Capable Generative Models J., and Chen, T

Reference 32

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Normalizing Flows are Capable Generative Models Scaling up gans for text-to-image synthesis

Reference 33

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Normalizing Flows are Capable Generative Models A style-based generator architecture for generative adversarial networks

Reference 34

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

Normalizing Flows are Capable Generative Models Elucidating the design space of diffusion-based generative models

Reference 35

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Normalizing Flows are Capable Generative Models Variational diffusion models

Reference 36

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Reference 37

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Normalizing Flows are Capable Generative Models Auto-Encoding Variational Bayes

Reference 38

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Normalizing Flows are Capable Generative Models P., Salimans, T., Jozefowicz, R., Chen, X., Sutskever, I., and Welling, M

Reference 39

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Normalizing Flows are Capable Generative Models Autoregressive Image Generation without Vector Quantization

Reference 40

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Reference 41

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Reference 43

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Normalizing Flows are Capable Generative Models and Kalchbrenner, N

Reference 44

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Reference 45

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Normalizing Flows are Capable Generative Models Self-labeled Conditional GANs

Reference 46

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Normalizing Flows are Capable Generative Models Masked autoregressive flow for density estimation

Reference 47

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Normalizing Flows are Capable Generative Models Transformer Neural Autoregressive Flows

Reference 48

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Normalizing Flows are Capable Generative Models Sdxl: Improving latent diffusion models for high-resolution image synthesis

Reference 49

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Reference 50

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Normalizing Flows are Capable Generative Models Generating diverse high-fidelity images with VQ-VAE-2

Reference 51

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Reference 52

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Normalizing Flows are Capable Generative Models Efficient content-based sparse attention with routing transformers

Reference 53

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Normalizing Flows are Capable Generative Models Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network

Reference 54

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Normalizing Flows are Capable Generative Models A., Maheswaranathan, N., and Ganguli, S

Reference 55

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Normalizing Flows are Capable Generative Models and Dhariwal, P

Reference 56

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 86415c2d-4c71-4811-b31d-630722c1022c · outbound

This paper cites P., Kumar, A., Ermon, S., and Poole, B.

Normalizing Flows are Capable Generative Models P., Kumar, A., Ermon, S., and Poole, B

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:52:22.567747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3deed6b5-5914-4cdc-bf5b-f74bfb65d031 · outbound

This paper cites Consistency models.

Normalizing Flows are Capable Generative Models Consistency models

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:52:22.553114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a52d9b45-877e-430e-b2e0-fc19beb489e7 · outbound

This paper cites Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation.

Normalizing Flows are Capable Generative Models Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-11T19:52:21.633145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b71091e-1c90-4951-8cbb-ab1d75104b1f · outbound

This paper cites an unresolved cited work.

Normalizing Flows are Capable Generative Models Unresolved cited work

Reference 60

Resolution: unresolved; raw_fallback, observed 2026-08-11T19:52:22.537448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bbd711f2-d116-43d2-86ed-24dc4cb2cbc6 · outbound

This paper cites Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction.

Normalizing Flows are Capable Generative Models Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-11T19:52:21.641195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4a26a46-64d9-4f9e-80c3-7e00cd0dd05c · outbound

This paper cites JetFormer: An Autoregressive Generative Model of Raw Images and Text.

Normalizing Flows are Capable Generative Models JetFormer: An Autoregressive Generative Model of Raw Images and Text

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-11T19:52:21.645430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64c79b9b-dd7e-4b82-a4a9-2c91d091b964 · outbound

This paper cites Givt: Generative infinite-vocabulary transformers.

Normalizing Flows are Capable Generative Models Givt: Generative infinite-vocabulary transformers

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-11T19:52:21.649909Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05c937e3-cddd-4b09-a491-9e1691c5b1e9 · outbound

This paper cites Conditional image generation with pixelcnn decoders.

Normalizing Flows are Capable Generative Models Conditional image generation with pixelcnn decoders

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:52:22.512079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0fdba52f-3173-44fa-967e-089f3080b639 · outbound

This paper cites Pixel recurrent neural networks.

Normalizing Flows are Capable Generative Models Pixel recurrent neural networks

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:52:22.495347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e3c7ea80-a203-464c-aaa6-fdf519e91c5c · outbound

This paper cites Neural discrete representation learning.

Normalizing Flows are Capable Generative Models Neural discrete representation learning

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:52:22.476791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ce3e7c74-35d0-4bab-99f6-71bc1e3518de · outbound

This paper cites N., Kaiser, L., and Polosukhin, I.

Normalizing Flows are Capable Generative Models N., Kaiser, L., and Polosukhin, I

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:52:22.460140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation dbfce2f7-0179-4f4e-9fbb-250ef36ad5a1 · outbound

This paper cites Stabilizing Generative Adversarial Networks: A Survey.

Normalizing Flows are Capable Generative Models Stabilizing Generative Adversarial Networks: A Survey

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-11T19:52:21.673416Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49b25988-31f3-44b8-a1da-e7133b8ea43a · outbound

This paper cites Y., Luong, T., Baid, G., Wang, Z., Vasudevan, V., Ku, A., Yang, Y., Ayan, B.

Normalizing Flows are Capable Generative Models Y., Luong, T., Baid, G., Wang, Z., Vasudevan, V., Ku, A., Yang, Y., Ayan, B

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:52:22.441044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 20a22063-b4e7-4b07-b8e9-87b40d040fb5 · outbound

This paper cites Open-sora: Democratizing efficient video production for all, 2024.

Normalizing Flows are Capable Generative Models Open-sora: Democratizing efficient video production for all, 2024

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:52:22.424242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0046518d-4f4b-4adb-a8c6-0478b647895f · outbound

This paper cites C., Tatikonda, S., and Duncan, J.

Normalizing Flows are Capable Generative Models C., Tatikonda, S., and Duncan, J

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:52:22.407100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

Observation 4476835d-7cce-4b86-b7bf-498ba301157c · inbound

Jet: A Modern Transformer-Based Normalizing Flow cites this paper.

Jet: A Modern Transformer-Based Normalizing Flow Normalizing Flows are Capable Generative Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T11:40:14.708030Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 442afa5c-a670-49cd-b5cb-f6b23e66be5f · inbound

Normalizing Flows are Capable Models for Continuous Control cites this paper.

Normalizing Flows are Capable Models for Continuous Control Normalizing Flows are Capable Generative Models

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-07T12:50:03.733713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0726e273-d6b4-41ed-817f-b74826e0e18c · inbound

STARFlow: Scaling Latent Normalizing Flows for High-resolution Image Synthesis cites this paper.

STARFlow: Scaling Latent Normalizing Flows for High-resolution Image Synthesis Normalizing Flows are Capable Generative Models

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:59.728304Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 933732f5-60c7-45a1-9d3c-d45a2b192332 · inbound

Inherited or produced? Inferring protein production kinetics when protein counts are shaped by a cell's division history cites this paper.

Inherited or produced? Inferring protein production kinetics when protein counts are shaped by a cell's division history Normalizing Flows are Capable Generative Models

Reference 57

Resolution: verified exact; arxiv_id, observed 2026-05-19T10:37:15.150087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f037e215-73a4-4023-96bd-0aa1bd80e113 · inbound

Flexible Language Modeling in Continuous Space with Transformer-based Autoregressive Flows cites this paper.

Flexible Language Modeling in Continuous Space with Transformer-based Autoregressive Flows Normalizing Flows are Capable Generative Models

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-06T21:24:27.887508Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ceac2da8-ba61-4022-8a15-4c303a0d24d4 · inbound

Guided Unconditional and Conditional Generative Models for Super-Resolution and Inference of Quasi-Geostrophic Turbulence cites this paper.

Guided Unconditional and Conditional Generative Models for Super-Resolution and Inference of Quasi-Geostrophic Turbulence Normalizing Flows are Capable Generative Models

Reference 375

Resolution: unresolved; no resolver link, observed 2026-08-06T21:12:37.056608Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8763492-f806-483f-9796-7a542edcbd4b · inbound

PixNerd: Pixel Neural Field Diffusion cites this paper.

PixNerd: Pixel Neural Field Diffusion Normalizing Flows are Capable Generative Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T10:59:53.635782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7e3f0e1-3c6a-4be4-978c-471f90c93974 · inbound

DeCo: Frequency-Decoupled Pixel Diffusion for End-to-End Image Generation cites this paper.

DeCo: Frequency-Decoupled Pixel Diffusion for End-to-End Image Generation Normalizing Flows are Capable Generative Models

Reference 70

Resolution: verified exact; arxiv_id, observed 2026-05-17T05:49:08.276051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 45103f62-a4c8-4c4b-b497-d38447a8448b · inbound

PixelGen: Improving Pixel Diffusion with Perceptual Supervision cites this paper.

PixelGen: Improving Pixel Diffusion with Perceptual Supervision Normalizing Flows are Capable Generative Models

Reference 27

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T07:57:33.161231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 004e005a-2115-4a6d-9485-980bb93dc995 · inbound

MPDiT: Multi-Patch Global-to-Local Transformer Architecture For Efficient Flow Matching and Diffusion Model cites this paper.

MPDiT: Multi-Patch Global-to-Local Transformer Architecture For Efficient Flow Matching and Diffusion Model Normalizing Flows are Capable Generative Models

Reference 80

Resolution: verified exact; arxiv_id, observed 2026-05-14T23:48:19.239685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4b6e4714-d553-4e35-ac2d-7dc727ab4796 · inbound

Optimal Stability of KL Divergence under Gaussian Perturbations cites this paper.

Optimal Stability of KL Divergence under Gaussian Perturbations Normalizing Flows are Capable Generative Models

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:56:05.060415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 045233db-61e4-4284-9ee2-30b570c99281 · inbound

Coevolving Representations in Joint Image-Feature Diffusion cites this paper.

Coevolving Representations in Joint Image-Feature Diffusion Normalizing Flows are Capable Generative Models

Reference 51

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T06:31:30.475307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1648b62d-7988-4fd9-ad67-7c4c1e0ecbff · inbound

Normalizing Flows with Iterative Denoising cites this paper.

Normalizing Flows with Iterative Denoising Normalizing Flows are Capable Generative Models

Reference 22

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T13:11:18.675867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f2177943-1463-4054-a167-988d1f052548 · inbound

SRC-Flow: Compact Semantic Representations Enable Normalizing Flows for Image Generation cites this paper.

SRC-Flow: Compact Semantic Representations Enable Normalizing Flows for Image Generation Normalizing Flows are Capable Generative Models

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-20T12:03:15.342384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 50f7d88b-7bd1-4054-a49f-64941a2d3262 · inbound

SRC-Flow: Compact Semantic Representations Enable Normalizing Flows for Image Generation cites this paper.

SRC-Flow: Compact Semantic Representations Enable Normalizing Flows for Image Generation Normalizing Flows are Capable Generative Models

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-06-30T19:15:01.303687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 423baf26-7a34-49e0-95fb-da112e6107a5 · inbound

SRC-Flow: Compact Semantic Representations Enable Normalizing Flows for Image Generation cites this paper.

SRC-Flow: Compact Semantic Representations Enable Normalizing Flows for Image Generation Normalizing Flows are Capable Generative Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-02T13:49:18.466475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 005df705-46a5-4519-af3e-ae2234d98c48 · inbound

WavTTS: Towards High-Quality Zero-Shot TTS via Direct Raw Waveform Modeling cites this paper.

WavTTS: Towards High-Quality Zero-Shot TTS via Direct Raw Waveform Modeling Normalizing Flows are Capable Generative Models

Reference 100

Resolution: verified exact; arxiv_id, observed 2026-07-02T05:16:39.794214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9f71efb7-08b6-4240-abf6-1fc21421e8f7 · inbound

Learning the Universe: Posterior Reliability of Neural Generative Models in High-Dimensional Field-Level Inference of Cosmic Initial Conditions cites this paper.

Learning the Universe: Posterior Reliability of Neural Generative Models in High-Dimensional Field-Level Inference of Cosmic Initial Conditions Normalizing Flows are Capable Generative Models

Reference 130

Resolution: verified exact; arxiv_id, observed 2026-06-27T19:11:10.646189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a1c7b1f4-8ce0-4ccc-87ef-196957a0ed7a · inbound

Unsupervised Disentanglement Without Compromises : How Functional Orthogonality Enforces Identifiability cites this paper.

Unsupervised Disentanglement Without Compromises : How Functional Orthogonality Enforces Identifiability Normalizing Flows are Capable Generative Models

Reference 71

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T05:59:37.844874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 586dd2c2-bf0f-4db5-a1c4-7455d072d1f0 · inbound

MIMFlow: Integrating Masked Image Modeling with Normalizing Flows for End-to-End Image Generation cites this paper.

MIMFlow: Integrating Masked Image Modeling with Normalizing Flows for End-to-End Image Generation Normalizing Flows are Capable Generative Models

Reference 48

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T20:50:11.370238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 26820345-c659-43f1-ae25-6c8c8e89649e · inbound

MIMFlow: Integrating Masked Image Modeling with Normalizing Flows for End-to-End Image Generation cites this paper.

MIMFlow: Integrating Masked Image Modeling with Normalizing Flows for End-to-End Image Generation Normalizing Flows are Capable Generative Models

Reference 52

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T09:35:40.204597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 11fd454b-88ec-41fd-9608-70246f0a7a53 · inbound

MIMFlow: Integrating Masked Image Modeling with Normalizing Flows for End-to-End Image Generation cites this paper.

MIMFlow: Integrating Masked Image Modeling with Normalizing Flows for End-to-End Image Generation Normalizing Flows are Capable Generative Models

Reference 52

Resolution: unresolved; no resolver link, observed 2026-07-12T12:07:02.175855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74b39190-0f92-46d4-b4d7-e2bd668a0407 · inbound

PixelU: A U-Shaped Transformer for Efficient End-to-End Pixel Diffusion cites this paper.

PixelU: A U-Shaped Transformer for Efficient End-to-End Pixel Diffusion Normalizing Flows are Capable Generative Models

Reference 54

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T20:03:57.203701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bd1161e0-df30-4804-bd2b-6149da5ce93e · inbound

Few-Step Boltzmann Generators via Scalable Likelihood Flow Maps cites this paper.

Few-Step Boltzmann Generators via Scalable Likelihood Flow Maps Normalizing Flows are Capable Generative Models

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-06-30T09:24:32.563356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4adc9fe1-f223-48f2-a571-f1b2acb1a82f · inbound

Signal or Noise? Understanding Generative Models for Real-World Sensor Time Series cites this paper.

Signal or Noise? Understanding Generative Models for Real-World Sensor Time Series Normalizing Flows are Capable Generative Models

Reference 44

Resolution: unresolved; no resolver link, observed 2026-07-11T20:41:10.528667Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b89d0f5c-c86d-430f-9e2c-f21b2ed53bbb · inbound

A Generative Model-Free Form Deformation Approach for the Generation of Mesh Motions with Applications to PDE cites this paper.

A Generative Model-Free Form Deformation Approach for the Generation of Mesh Motions with Applications to PDE Normalizing Flows are Capable Generative Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-02T06:01:02.136932Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d589d863-5153-4933-a6e7-7ce5cea92858 · inbound

Amortized Moment Matching for Visual Generation cites this paper.

Amortized Moment Matching for Visual Generation Normalizing Flows are Capable Generative Models

Reference 121

Resolution: unresolved; no resolver link, observed 2026-07-30T18:58:28.307144Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 091e9ce1-5277-4e37-8741-161b8fc26204 · inbound

You Only Flow Once: Calibrated and Real-Time Radar Pose Estimation with Multi-Hypothesis Normalizing Flows cites this paper.

You Only Flow Once: Calibrated and Real-Time Radar Pose Estimation with Multi-Hypothesis Normalizing Flows Normalizing Flows are Capable Generative Models

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-11T14:48:00.534830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.