Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-06-29T08:45:28.229408Z
Paper Citation Record · LEDGER
Midpoint Generative Models
As of 10 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 1 inbound Pith citation observation for arXiv:2605.29920.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-01T07:15:37.584074Z
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
60 of 60 outbound references displayed
- verified exact10
- verified fuzzy0
- unresolved47
- parse uncertain0
- malformed identifier1
- metadata mismatch2
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 491e2f98-74f9-4c27-9a30-01442ff34d98 · outbound
This paper cites Diffusion models beat gans on image synthesis.
Midpoint Generative Models Diffusion models beat gans on image synthesis
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7db2bfed-a947-4788-ab81-69c8b80635c6 · outbound
This paper cites High- resolution image synthesis with latent diffusion models.
Midpoint Generative Models High- resolution image synthesis with latent diffusion models
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d0a944eb-88b7-4ce4-a373-9575521e6423 · outbound
This paper cites Video diffusion models.Advances in neural information processing systems, 35: 8633–8646, 2022.
Midpoint Generative Models Video diffusion models.Advances in neural information processing systems, 35: 8633–8646, 2022
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c72b04e2-2e85-47cd-a57a-84cd41b91efe · outbound
This paper cites Diffwave: A versatile diffusion model for audio synthesis.
Midpoint Generative Models Diffwave: A versatile diffusion model for audio synthesis
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0e64ebd7-f8fa-4449-8d3b-016e73adf6d6 · outbound
This paper cites an unresolved cited work.
Midpoint Generative Models Unresolved cited work
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2efe54ff-dc75-4168-bc9e-07ea757b5dd8 · outbound
This paper cites SDEdit: Guided image synthesis and editing with stochastic differential equations.
Midpoint Generative Models SDEdit: Guided image synthesis and editing with stochastic differential equations
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f41a29aa-240c-4ccf-9450-74328a3648f8 · outbound
This paper cites Palette: Image-to-image diffusion models.
Midpoint Generative Models Palette: Image-to-image diffusion models
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1d619016-24e4-4c61-9f54-a049bd540238 · outbound
This paper cites Denoising Diffusion Implicit Models.
Midpoint Generative Models Denoising Diffusion Implicit Models
Reference 8
- Resolution
- verified exact
- local_arxiv, observed 2026-06-29T08:53:16.233725Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 6f9e35bd-7c58-47c3-8a52-ed7b2fa4add2 · outbound
This paper cites Elucidating the design space of diffusion-based generative models.Advances in neural information processing systems, 35: 26565–26577, 2022.
Midpoint Generative Models Elucidating the design space of diffusion-based generative models.Advances in neural information processing systems, 35: 26565–26577, 2022
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dd74ecef-87f3-4208-a51e-c5e3bb377b44 · outbound
This paper cites Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps.Advances in neural information processing systems, 35:5775–5787, 2022.
Midpoint Generative Models Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps.Advances in neural information processing systems, 35:5775–5787, 2022
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 69fdb54b-b733-4ba9-abe5-2b3f81a58937 · outbound
This paper cites Progressive distillation for fast sampling of diffusion models.
Midpoint Generative Models Progressive distillation for fast sampling of diffusion models
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4aedfc96-fcea-4787-acfd-27e9070c3181 · outbound
This paper cites Consistency models.
Midpoint Generative Models Consistency models
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2f5738e9-9715-4b37-b18d-45ab4df73fa9 · outbound
This paper cites One-step diffusion with distribution matching distillation.
Midpoint Generative Models One-step diffusion with distribution matching distillation
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7868f1d4-a20a-4460-97d8-3a02f4b85bf0 · outbound
This paper cites Adversarial diffusion distillation.
Midpoint Generative Models Adversarial diffusion distillation
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7e4afb7b-2f2d-4fbe-8102-402642d3ef4a · outbound
This paper cites Susskind.
Midpoint Generative Models Susskind
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 56960a62-7298-48ef-8558-10da87a1e0a4 · outbound
This paper cites Improved techniques for training consistency models.
Midpoint Generative Models Improved techniques for training consistency models
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d98b05e7-5d00-4115-970e-ec490a122dc2 · outbound
This paper cites One step diffusion via shortcut models.
Midpoint Generative Models One step diffusion via shortcut models
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2587f0c8-736a-47a9-aad9-5e164b468082 · outbound
This paper cites Mean Flows for One-step Generative Modeling.
Midpoint Generative Models Mean Flows for One-step Generative Modeling
Reference 18
- Resolution
- verified exact
- local_arxiv, observed 2026-06-29T08:53:16.239050Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 9bb31806-2c26-4d66-8bb4-6c3633a0eeb4 · outbound
This paper cites Flow Matching for Generative Modeling.
Midpoint Generative Models Flow Matching for Generative Modeling
Reference 19
- Resolution
- verified exact
- local_arxiv, observed 2026-06-29T08:53:16.216417Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 63c77211-8406-4ebb-a72b-479ededea06a · outbound
This paper cites Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow.
Midpoint Generative Models Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
Reference 20
- Resolution
- verified exact
- local_arxiv, observed 2026-06-29T08:53:16.226417Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 5496c8a0-f795-40d1-a65e-2c8bce7b63b5 · outbound
This paper cites Building normalizing flows with stochastic interpolants.
Midpoint Generative Models Building normalizing flows with stochastic interpolants
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 65b0dcb7-18c1-4b09-a3c1-9cf7cb8dcfbc · outbound
This paper cites Stochastic interpolants: A unifying framework for flows and diffusions.Journal of Machine Learning Research, 26(209): 1–80, 2025.
Midpoint Generative Models Stochastic interpolants: A unifying framework for flows and diffusions.Journal of Machine Learning Research, 26(209): 1–80, 2025
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c7c1aebd-fc72-4880-bc34-5f66b9037a72 · outbound
This paper cites Simplifying, stabilizing and scaling continuous-time consistency models.
Midpoint Generative Models Simplifying, stabilizing and scaling continuous-time consistency models
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0124ecd4-867a-4e16-b958-60be93889a1d · outbound
This paper cites Consistency trajectory models: Learning probability flow ODE trajectory of diffusion.
Midpoint Generative Models Consistency trajectory models: Learning probability flow ODE trajectory of diffusion
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 77730290-436a-49d5-85c7-c7ce40fd5531 · outbound
This paper cites TRACT: Denoising Diffusion Models with Transitive Closure Time-Distillation.
Midpoint Generative Models TRACT: Denoising Diffusion Models with Transitive Closure Time-Distillation
Reference 25
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-29T08:53:16.224022Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 6ad0c9b7-95ab-48c2-b740-0c4f01c044d5 · outbound
This paper cites Consistency models made easy.
Midpoint Generative Models Consistency models made easy
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bb71e4e2-a329-4cf6-9efc-5def050b1f45 · outbound
This paper cites an unresolved cited work.
Midpoint Generative Models Unresolved cited work
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4dfb6b5d-e01d-45e9-8e3a-eba2cf90f07c · outbound
This paper cites Truncated consistency models.
Midpoint Generative Models Truncated consistency models
Reference 28
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 589ab56d-65bf-4d41-9dc2-f4914299bc9a · outbound
This paper cites Diff- instruct: A universal approach for transferring knowledge from pre-trained diffusion models.
Midpoint Generative Models Diff- instruct: A universal approach for transferring knowledge from pre-trained diffusion models
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 01aa5a3f-4b1a-4a79-b03f-28607dc6fe9d · outbound
This paper cites Improved distribution matching distillation for fast image synthesis.Advances in neural information processing systems, 37:47455–47487, 2024.
Midpoint Generative Models Improved distribution matching distillation for fast image synthesis.Advances in neural information processing systems, 37:47455–47487, 2024
Reference 30
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d94a99da-cfff-4161-b160-0cee537a49d1 · outbound
This paper cites One-step Diffusion Models with $f$-Divergence Distribution Matching.
Midpoint Generative Models One-step Diffusion Models with $f$-Divergence Distribution Matching
Reference 31
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T08:53:16.236577Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 65dc54b8-f36c-4bf8-b89d-3bc779b2dea5 · outbound
This paper cites Score identity distillation: Exponentially fast distillation of pretrained diffusion models for one-step generation.
Midpoint Generative Models Score identity distillation: Exponentially fast distillation of pretrained diffusion models for one-step generation
Reference 32
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 13e5e073-fa8c-44e9-8b1c-fbd441daffb6 · outbound
This paper cites One-step diffusion distillation through score implicit matching.Advances in Neural Information Processing Systems, 37:115377–115408, 2024.
Midpoint Generative Models One-step diffusion distillation through score implicit matching.Advances in Neural Information Processing Systems, 37:115377–115408, 2024
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cae9c635-6903-4b04-ad30-f5ed83d579c2 · outbound
This paper cites Inverse bridge matching distillation.
Midpoint Generative Models Inverse bridge matching distillation
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 927da933-3074-46e8-bef6-27ead4861bb8 · outbound
This paper cites Flow Generator Matching.
Reference 35
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T08:53:16.241581Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 60afbcba-3d25-410a-afa1-a1e5b85bb2a5 · outbound
This paper cites Overclocking electrostatic generative models.
Midpoint Generative Models Overclocking electrostatic generative models
Reference 36
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cbb93253-35a6-428b-97db-d6a9f49a77c5 · outbound
This paper cites Universal inverse distillation for matching models with real-data supervision (no GANs).
Midpoint Generative Models Universal inverse distillation for matching models with real-data supervision (no GANs)
Reference 37
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0e0b54b2-1856-49a9-8757-b48fc62ed7cb · outbound
This paper cites Improved Techniques for Training Consistency Models.
Midpoint Generative Models Improved Techniques for Training Consistency Models
Reference 38
- Resolution
- verified exact
- local_arxiv, observed 2026-06-29T08:53:16.231394Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation e1acce3e-b6bd-406d-ab66-d6bd80da7ce9 · outbound
This paper cites Flow map matching with stochastic interpolants: A mathematical framework for consistency models.
Midpoint Generative Models Flow map matching with stochastic interpolants: A mathematical framework for consistency models
Reference 39
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 60dec9b4-8169-452d-a4f9-11cdb18f163c · outbound
This paper cites Generative adversarial nets.Advances in neural information processing systems, 27, 2014.
Midpoint Generative Models Generative adversarial nets.Advances in neural information processing systems, 27, 2014
Reference 40
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8769abc4-f5d9-47e6-b508-fc6d1e5aaa62 · outbound
This paper cites f-gan: Training generative neural samplers using variational divergence minimization.Advances in neural information processing systems, 29, 2016.
Midpoint Generative Models f-gan: Training generative neural samplers using variational divergence minimization.Advances in neural information processing systems, 29, 2016
Reference 41
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 930f6c3f-c7aa-4ada-b7da-6bd43163b044 · outbound
This paper cites Wasserstein generative adversarial networks.
Midpoint Generative Models Wasserstein generative adversarial networks
Reference 42
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8211d452-ebfe-449f-a0cf-16d1c23e39ca · outbound
This paper cites Tackling the generative learning trilemma with denoising diffusion GANs.
Midpoint Generative Models Tackling the generative learning trilemma with denoising diffusion GANs
Reference 43
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9fa635cf-73f5-45fa-9106-a568976d5486 · outbound
This paper cites an unresolved cited work.
Midpoint Generative Models Unresolved cited work
Reference 44
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f367d822-859d-41bd-953b-8855100e7814 · outbound
This paper cites Diffusion-GAN: Training GANs with diffusion.
Midpoint Generative Models Diffusion-GAN: Training GANs with diffusion
Reference 45
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6701a48e-b255-46d0-b484-fd1045bc83b3 · outbound
This paper cites Learning multiple layers of features from tiny images.
Midpoint Generative Models Learning multiple layers of features from tiny images
Reference 46
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cf0c029b-9681-450d-b6eb-bb8912370f36 · outbound
This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium.Advances in neural information processing systems, 30, 2017.
Midpoint Generative Models Gans trained by a two time-scale update rule converge to a local nash equilibrium.Advances in neural information processing systems, 30, 2017
Reference 47
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e0ac00ca-e7d5-4381-91ca-9bf73f4111d8 · outbound
This paper cites Let us build bridges: Understanding and extending diffusion generative models.
Midpoint Generative Models Let us build bridges: Understanding and extending diffusion generative models
Reference 48
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a24ea594-7d00-497b-8819-809fd64cb11c · outbound
This paper cites Denoising Diffusion Bridge Models.
Midpoint Generative Models Denoising Diffusion Bridge Models
Reference 49
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T08:53:16.218884Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation cc2653e4-b28c-48a9-a8b6-08e26ce3dc65 · outbound
This paper cites Diffusion Bridge Implicit Models.
Reference 50
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-29T08:53:16.214112Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation f2b3a006-df16-45b2-8da7-5f0d9ee2374a · outbound
This paper cites Consistency diffusion bridge models.Advances in Neural Information Processing Systems, 37:23516–23548, 2024.
Midpoint Generative Models Consistency diffusion bridge models.Advances in Neural Information Processing Systems, 37:23516–23548, 2024
Reference 51
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation eaa1c2d2-69a6-4aa6-91c6-ee9a4b73796d · outbound
This paper cites Implicit image-to-image schrödinger bridge for image restoration.Pattern Recognition, 165:111627, 2025.
Midpoint Generative Models Implicit image-to-image schrödinger bridge for image restoration.Pattern Recognition, 165:111627, 2025
Reference 52
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e4ff61e8-e5c3-4ae5-b30b-ebd011bcd05d · outbound
This paper cites Cmt: Mid-training for efficient learning of consistency, mean flow, and flow map models.
Midpoint Generative Models Cmt: Mid-training for efficient learning of consistency, mean flow, and flow map models
Reference 53
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T08:53:16.229077Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 7774bc07-fbfa-4248-8fa4-53680f77b7f8 · outbound
This paper cites Diffratio: Training one-step diffusion models without teacher supervision.arXiv e-prints, pages arXiv–2502, 2025.
Midpoint Generative Models Diffratio: Training one-step diffusion models without teacher supervision.arXiv e-prints, pages arXiv–2502, 2025
Reference 54
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dd917de1-5091-4786-a91c-4d0a7b7daebb · outbound
This paper cites Stable consistency tuning: Understanding and improving consistency models.
Midpoint Generative Models Stable consistency tuning: Understanding and improving consistency models
Reference 55
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 54916aa0-5625-45f2-ae3d-33b311341cf4 · outbound
This paper cites Fast sampling of diffusion models via operator learning.
Midpoint Generative Models Fast sampling of diffusion models via operator learning
Reference 56
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b4615b2b-8d3c-4a2f-b699-64eb756fac68 · outbound
This paper cites Training generative adversarial networks with limited data.Advances in neural information processing systems, 33:12104–12114, 2020.
Midpoint Generative Models Training generative adversarial networks with limited data.Advances in neural information processing systems, 33:12104–12114, 2020
Reference 57
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6c35e2ec-64c3-488b-9ecc-67ab90f6ff95 · outbound
This paper cites Inductive Moment Matching.
Reference 58
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T08:53:16.221468Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 3db4b472-96e1-4467-af00-59898746c166 · outbound
This paper cites Discussion and Limitations.
Midpoint Generative Models Discussion and Limitations
Reference 59
- Resolution
- malformed identifier
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bfe21545-b224-4b5c-ad72-70cf56a33dd3 · outbound
This paper cites Guidelines: • The answer [N/A] means that the paper does not involve crowdsourcing nor research with human subjects.
Midpoint Generative Models Guidelines: • The answer [N/A] means that the paper does not involve crowdsourcing nor research with human subjects
Reference 60
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:45:28.229408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
Observation fc596ade-ae4b-40fa-baff-34113f8d5353 · inbound
Zero-Flow Two-Sample Tests cites this paper.
Reference 51
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T07:15:37.584074Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.