Paper Citation Record · LEDGER

Masked Conditioning for Deep Generative Models

As of 10 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 0 inbound Pith citation observations for arXiv:2505.16725.

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

pith.paper-citation-record.v1
2505.16725 v1

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:58:50.714926Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

49 of 49 outbound references displayed

verified exact10
verified fuzzy9
unresolved29
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e746c292-416e-4eab-8ee8-19d3de6e7d1f · outbound

This paper cites Optuna: A next-generation hyperparameter optimization framework, in: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

Masked Conditioning for Deep Generative Models Optuna: A next-generation hyperparameter optimization framework, in: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:58:56.062619Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 91a466b8-6226-4d1b-9e66-51ef2751072c · outbound

This paper cites From Automation to Augmentation: Redefining Engineering Design and Manufacturing in the Age of NextGen-AI.

Masked Conditioning for Deep Generative Models From Automation to Augmentation: Redefining Engineering Design and Manufacturing in the Age of NextGen-AI

Reference 2

Resolution: verified exact; doi, observed 2026-08-07T14:58:51.272992Z

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Observation 6cff4fb2-f43a-4c80-9b68-1057e20d777e · outbound

This paper cites Curriculum learning, in: Proceedings of the 26th Annual International Conference on Machine Learning, ACM, Montreal Quebec Canada.

Masked Conditioning for Deep Generative Models Curriculum learning, in: Proceedings of the 26th Annual International Conference on Machine Learning, ACM, Montreal Quebec Canada

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T14:58:44.894355Z

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Observation 1d6e2964-c68b-4428-80c0-dcd886796c2e · outbound

This paper cites Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer.

Masked Conditioning for Deep Generative Models Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T14:58:44.997063Z

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Observation 8fa287fd-3d6f-477f-a606-356c74194382 · outbound

This paper cites an unresolved cited work.

Masked Conditioning for Deep Generative Models Unresolved cited work

Reference 5

Resolution: verified exact; doi, observed 2026-08-07T14:58:50.959704Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 926bfae7-dc71-437c-919e-9ccdce788b10 · outbound

This paper cites PaDGAN: Learning to Generate High-Quality Novel Designs.

Masked Conditioning for Deep Generative Models PaDGAN: Learning to Generate High-Quality Novel Designs

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T14:58:45.228094Z

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Observation 03bc651a-1c5a-48eb-b6eb-cf6c095b1c91 · outbound

This paper cites Mo-padgan: Reparameterizing engineering designs for augmented multi-objective optimization.

Masked Conditioning for Deep Generative Models Mo-padgan: Reparameterizing engineering designs for augmented multi-objective optimization

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T14:58:45.379622Z

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Observation 9c6f061b-4abd-4900-8968-29194f2251dd · outbound

This paper cites Image Super-Resolution With Deep Variational Autoencoders.

Masked Conditioning for Deep Generative Models Image Super-Resolution With Deep Variational Autoencoders

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:58:53.552617Z

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Observation f8daba1b-1b8b-4b13-8ede-b726b23fb83d · outbound

This paper cites VAEs in the Presence of Missing Data.

Masked Conditioning for Deep Generative Models VAEs in the Presence of Missing Data

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T14:58:45.682886Z

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Observation f1f96085-13e2-477c-9b08-aa1476b6ba5d · outbound

This paper cites Diffusionmodelsbeatgansonimagesynthesis,in:Proceedingsofthe35thInternationalConferenceonNeural Information Processing Systems, Curran Associates Inc., Red Hook, NY, USA.

Masked Conditioning for Deep Generative Models Diffusionmodelsbeatgansonimagesynthesis,in:Proceedingsofthe35thInternationalConferenceonNeural Information Processing Systems, Curran Associates Inc., Red Hook, NY, USA

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:58:55.871040Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8d371918-fdc8-4777-b342-8c6f95cb040e · outbound

This paper cites Scaling Rectified Flow Transformers for High-Resolution Image Synthesis.

Masked Conditioning for Deep Generative Models Scaling Rectified Flow Transformers for High-Resolution Image Synthesis

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T14:58:45.859696Z

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Observation 4425c055-7320-404d-907b-c76f8cb639c9 · outbound

This paper cites Plantldm: A latent diffusion model for visual synthesis of plant images.

Masked Conditioning for Deep Generative Models Plantldm: A latent diffusion model for visual synthesis of plant images

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:58:55.560168Z

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Observation b2556855-8810-4637-9188-0857d57b743c · outbound

This paper cites CLIPScore: A Reference-free Evaluation Metric for Image Captioning.

Masked Conditioning for Deep Generative Models CLIPScore: A Reference-free Evaluation Metric for Image Captioning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T14:58:46.125311Z

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Observation a058129e-e757-4a52-a1a4-50c2f9fb59c7 · outbound

This paper cites Denoising Diffusion Probabilistic Models.

Masked Conditioning for Deep Generative Models Denoising Diffusion Probabilistic Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T14:58:46.280901Z

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Observation 642d2d9f-57ba-4939-8d6c-bbda7c61a611 · outbound

This paper cites Dvm-car: A large-scale automotive dataset for visual marketing research and applications, in: Proceedings of IEEE International Conference on Big Data, pp.

Masked Conditioning for Deep Generative Models Dvm-car: A large-scale automotive dataset for visual marketing research and applications, in: Proceedings of IEEE International Conference on Big Data, pp

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:58:55.382709Z

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Observation 78f16d0f-ba8b-4f0a-a226-8a3666bf8868 · outbound

This paper cites Variational autoencoder with arbitrary conditioning, in: International Conference on Learning Representations.

Masked Conditioning for Deep Generative Models Variational autoencoder with arbitrary conditioning, in: International Conference on Learning Representations

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:58:55.097720Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 229bba62-ad94-4f3c-a5c5-d22b79ce98b0 · outbound

This paper cites Alias-Free Generative Adversarial Networks.

Masked Conditioning for Deep Generative Models Alias-Free Generative Adversarial Networks

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T14:58:46.619292Z

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Observation a49c1f88-55f2-459c-b69f-3ee2fd4a2b98 · outbound

This paper cites Analyzing and Improving the Image Quality of StyleGAN.

Masked Conditioning for Deep Generative Models Analyzing and Improving the Image Quality of StyleGAN

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T14:58:46.750525Z

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Observation 387a9380-3504-4796-8872-733b8f5d0aa6 · outbound

This paper cites Auto-Encoding Variational Bayes.

Masked Conditioning for Deep Generative Models Auto-Encoding Variational Bayes

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T14:58:46.830550Z

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Observation 2b5e9c0f-d009-497c-be7b-8057c432888d · outbound

This paper cites An introduction to variational autoencoders.

Masked Conditioning for Deep Generative Models An introduction to variational autoencoders

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T14:58:46.971553Z

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Observation f1f4e7f8-4223-4b14-aed9-4111f7fcfbb6 · outbound

This paper cites Flux.https://github.com/black-forest-labs/flux.

Masked Conditioning for Deep Generative Models Flux.https://github.com/black-forest-labs/flux

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:58:54.817985Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b7185c67-3836-4d6c-84cd-243c4529115b · outbound

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

Masked Conditioning for Deep Generative Models VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T14:58:47.220219Z

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Observation a5f0459f-d5a9-480f-8c0e-dff71486d16e · outbound

This paper cites Exploring the Potentials and Challenges of Deep Generative Models in Product Design Conception.

Masked Conditioning for Deep Generative Models Exploring the Potentials and Challenges of Deep Generative Models in Product Design Conception

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:58:53.304089Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3a3c0e6a-d523-4977-ae39-5ce5378d3b2b · outbound

This paper cites GeoBiked: A Dataset with Geometric Features and Automated Labeling Techniques to Enable Deep Generative Models in Engineering Design.

Masked Conditioning for Deep Generative Models GeoBiked: A Dataset with Geometric Features and Automated Labeling Techniques to Enable Deep Generative Models in Engineering Design

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:58:53.085899Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8810cb6c-aeca-4cd0-bde8-f463da28f2e2 · outbound

This paper cites InsertDiffusion: Identity Preserving Visualization of Objects through a Training-Free Diffusion Architecture.

Masked Conditioning for Deep Generative Models InsertDiffusion: Identity Preserving Visualization of Objects through a Training-Free Diffusion Architecture

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:58:52.802384Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 16389661-c646-41c9-b82e-426188c09c34 · outbound

This paper cites Handling incomplete heterogeneous data using vaes.

Masked Conditioning for Deep Generative Models Handling incomplete heterogeneous data using vaes

Reference 26

Resolution: malformed identifier; no resolver link, observed 2026-08-07T14:58:47.678146Z

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Observation 647f3f90-deb6-4752-b969-8cde2e91193d · outbound

This paper cites PcDGAN: A Continuous Conditional Diverse Generative Adversarial Network For Inverse Design.

Masked Conditioning for Deep Generative Models PcDGAN: A Continuous Conditional Diverse Generative Adversarial Network For Inverse Design

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:58:52.591664Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b74e434b-364a-4c6e-b177-9a2341c914f5 · outbound

This paper cites CreativeGAN: Editing Generative Adversarial Networks for Creative Design Synthesis.

Masked Conditioning for Deep Generative Models CreativeGAN: Editing Generative Adversarial Networks for Creative Design Synthesis

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:58:52.253816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 08934fbd-c742-4feb-a860-fdb79a8a9018 · outbound

This paper cites Semi-Supervised Learning with Generative Adversarial Networks.

Masked Conditioning for Deep Generative Models Semi-Supervised Learning with Generative Adversarial Networks

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T14:58:48.015421Z

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Observation 954fa6ff-d585-4acf-beaa-3468cda08df9 · outbound

This paper cites Conditional Image Generation with PixelCNN Decoders.

Masked Conditioning for Deep Generative Models Conditional Image Generation with PixelCNN Decoders

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T14:58:48.153714Z

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Observation eb2b8fd4-1569-4e1b-b9ed-11daa343e422 · outbound

This paper cites Scalable Diffusion Models with Transformers.

Masked Conditioning for Deep Generative Models Scalable Diffusion Models with Transformers

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T14:58:48.299848Z

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Observation 2fb8fe57-7e32-4b21-9bff-38d14533593c · outbound

This paper cites From Concept to Manufacturing: Evaluating Vision-Language Models for Engineering Design.

Masked Conditioning for Deep Generative Models From Concept to Manufacturing: Evaluating Vision-Language Models for Engineering Design

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T14:58:48.437632Z

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Observation ceffeb42-9e3b-4fcf-b8a7-3a64085280d7 · outbound

This paper cites State of the Art on Diffusion Models for Visual Computing.

Masked Conditioning for Deep Generative Models State of the Art on Diffusion Models for Visual Computing

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T14:58:48.569304Z

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Observation 7ef98347-8e22-4420-95a9-9798f083a0ef · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

Masked Conditioning for Deep Generative Models SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T14:58:48.721817Z

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Observation e55394ae-d076-474f-b822-063369ca34df · outbound

This paper cites Understanding Deep Learning.

Masked Conditioning for Deep Generative Models Understanding Deep Learning

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:58:54.604615Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 57bcaaa3-76b2-4597-afec-c28ffdd1e83f · outbound

This paper cites BIKED: A Dataset for Computational Bicycle Design with Machine Learning Benchmarks.

Masked Conditioning for Deep Generative Models BIKED: A Dataset for Computational Bicycle Design with Machine Learning Benchmarks

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:58:52.030763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7a4ab1d7-f089-4bb5-906d-0ee89cd1d3ab · outbound

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

Masked Conditioning for Deep Generative Models High-Resolution Image Synthesis with Latent Diffusion Models

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T14:58:49.111985Z

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Observation 55e06e56-1f6a-4080-802e-42087d184236 · outbound

This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation.

Masked Conditioning for Deep Generative Models U-Net: Convolutional Networks for Biomedical Image Segmentation

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T14:58:49.239746Z

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Observation 674020f2-9468-4f44-abfe-dcebc84caa49 · outbound

This paper cites Palette: Image-to-image diffusion models, in: ACM SIGGRAPH 2022 Conference Proceedings, Association for Computing Machinery, New York, NY, USA.

Masked Conditioning for Deep Generative Models Palette: Image-to-image diffusion models, in: ACM SIGGRAPH 2022 Conference Proceedings, Association for Computing Machinery, New York, NY, USA

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T14:58:49.372709Z

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Observation 9aa01a32-a47b-4061-a067-6fd28f68d857 · outbound

This paper cites LAION-5B: An open large-scale dataset for training next generation image-text models.

Masked Conditioning for Deep Generative Models LAION-5B: An open large-scale dataset for training next generation image-text models

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T14:58:49.484697Z

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Observation ce768d48-b570-4775-99d7-69bee83cfdbc · outbound

This paper cites an unresolved cited work.

Masked Conditioning for Deep Generative Models Unresolved cited work

Reference 41

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:58:54.330908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5d75d285-2496-4112-9219-6e57f6108036 · outbound

This paper cites Denoising Diffusion Implicit Models.

Masked Conditioning for Deep Generative Models Denoising Diffusion Implicit Models

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T14:58:49.769749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42b861df-ee9e-47a5-9148-7c5e1476874b · outbound

This paper cites Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks.

Masked Conditioning for Deep Generative Models Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T14:58:49.911618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76ee9450-3868-4b54-aff9-3278e34c9d47 · outbound

This paper cites Attention is all you need, in: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R.

Masked Conditioning for Deep Generative Models Attention is all you need, in: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:58:54.096905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6b77fe0f-645a-42f8-a65f-1937380b7ad1 · outbound

This paper cites Diffusers: State-of-the-art diffusion models.

Masked Conditioning for Deep Generative Models Diffusers: State-of-the-art diffusion models

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:58:53.925928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3d6eab05-c699-4c94-a719-fb5b8e2110ed · outbound

This paper cites Image quality assessment: from error visibility to structural similarity.

Masked Conditioning for Deep Generative Models Image quality assessment: from error visibility to structural similarity

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T14:58:50.341677Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation adb76bd6-ae58-466f-8613-5d05b810be94 · outbound

This paper cites Generating various airfoil shapes with required lift coefficient using conditional variational autoencoders.

Masked Conditioning for Deep Generative Models Generating various airfoil shapes with required lift coefficient using conditional variational autoencoders

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:58:51.563334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 756bb27e-36b0-4cd8-92e1-5753177c1d9d · outbound

This paper cites The Unreasonable Effectiveness of Deep Features as a Perceptual Metric.

Masked Conditioning for Deep Generative Models The Unreasonable Effectiveness of Deep Features as a Perceptual Metric

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T14:58:50.610200Z

Source-reported events for the cited work

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Observation 35dfb33a-f03e-48e3-a609-486a229010bc · outbound

This paper cites 3D Shape Synthesis for Conceptual Design and Optimization Using Variational Autoencoders.

Masked Conditioning for Deep Generative Models 3D Shape Synthesis for Conceptual Design and Optimization Using Variational Autoencoders

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T14:58:50.714926Z

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Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.