Paper Citation Record · LEDGER

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps

As of 22 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2501.06999.

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pith.paper-citation-record.v1
2501.06999 v1

Coverage vector

measured 39 of 39 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-10T20:56:10.361807Z

measured 39 of 39 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

39 of 39 outbound references displayed

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External citation measurements

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Outbound references

Observation 3977dbc2-9c8d-4110-8563-89b62c980667 · outbound

This paper cites According to (Ho et al., 2020), the likelihood bound Eq.

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps According to (Ho et al., 2020), the likelihood bound Eq

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:56:10.914690Z

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Observation 7e5d9191-4e82-4178-a689-d4d8419b394b · outbound

This paper cites Hierarchical Autoregressive Image Models with Auxiliary Decoders.

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps Hierarchical Autoregressive Image Models with Auxiliary Decoders

Reference 5

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Observation 61dbabec-01bb-4826-af2e-45cf56df1cff · outbound

This paper cites NICE: Non-linear Independent Components Estimation.

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps NICE: Non-linear Independent Components Estimation

Reference 6

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Observation 741e5ecd-8a4a-43f0-94fe-4a7985943407 · outbound

This paper cites Learning Energy-Based Models by Diffusion Recovery Likelihood.

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps Learning Energy-Based Models by Diffusion Recovery Likelihood

Reference 8

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Observation 8877d213-6e07-4e8a-bb1f-7f33bf32ce1c · outbound

This paper cites Wavelet Score-Based Generative Modeling.

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps Wavelet Score-Based Generative Modeling

Reference 10

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Observation 74dc36b2-a5aa-4f1d-8ad4-887d44df4a21 · outbound

This paper cites Deep Anomaly Detection with Outlier Exposure.

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps Deep Anomaly Detection with Outlier Exposure

Reference 12

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Observation 14157d43-c0d6-4a74-bf1c-e1bcf539d06b · outbound

This paper cites Autoregressive Diffusion Models.

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps Autoregressive Diffusion Models

Reference 13

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Observation 4e57d041-0cb8-405b-9bee-90746f120f6a · outbound

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

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps Progressive Growing of GANs for Improved Quality, Stability, and Variation

Reference 14

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Observation a4657e19-bf90-4aa1-9471-e979bcdcd20c · outbound

This paper cites Auto-Encoding Variational Bayes.

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps Auto-Encoding Variational Bayes

Reference 16

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Observation 6c11c711-26ae-4406-af2d-c6795ebc4851 · outbound

This paper cites Flow Matching for Generative Modeling.

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps Flow Matching for Generative Modeling

Reference 18

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Observation ae5a1486-5723-4da2-89f6-97199ef06d00 · outbound

This paper cites Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional Upscaling.

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional Upscaling

Reference 19

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Observation a2d211a8-b4d5-4733-b5ea-5bbc3f706c1b · outbound

This paper cites Do Deep Generative Models Know What They Don't Know?.

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps Do Deep Generative Models Know What They Don't Know?

Reference 20

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Observation 2c536664-1631-4c6e-b0a4-e6457c12589c · outbound

This paper cites Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality.

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality

Reference 21

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Observation 72fbd232-5801-4145-8db9-3b8485cd4e3d · outbound

This paper cites Parallel multiscale autoregressive density estimation.

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps Parallel multiscale autoregressive density estimation

Reference 23

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Observation 2b15eeec-c5b9-43cc-9ef8-fe1c0dee9444 · outbound

This paper cites A Less Biased Evaluation of Out-of-distribution Sample Detectors.

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps A Less Biased Evaluation of Out-of-distribution Sample Detectors

Reference 25

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Observation 8c827286-925c-41b4-9172-aae2dbeecd46 · outbound

This paper cites Approximate earth mover’s distance in linear time.

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps Approximate earth mover’s distance in linear time

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:56:10.976379Z

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Observation d5bc830b-b858-49ae-a3a9-ed8bce7684ec · outbound

This paper cites Flif: Free lossless image format based on maniac compression.

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps Flif: Free lossless image format based on maniac compression

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:56:10.961685Z

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Observation 8a44b488-df70-4d64-b471-ba4d6cc93e20 · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps Score-Based Generative Modeling through Stochastic Differential Equations

Reference 28

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Observation eb6c6b9d-1b38-4073-9718-36e90d263735 · outbound

This paper cites Pixel recurrent neural networks.

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps Pixel recurrent neural networks

Reference 30

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Observation 3a0652bc-92bb-468e-ae4e-b367a0d3d2b8 · outbound

This paper cites Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model.

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model

Reference 31

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Observation ea78fbdb-62ce-4e49-88fd-b928bdbd1936 · outbound

This paper cites MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance.

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance

Reference 32

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Observation 8de2fe94-4ae9-4b3a-bd67-5b26fc06b0b6 · outbound

This paper cites Improved Techniques for Maximum Likelihood Estimation for Diffusion ODEs.

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps Improved Techniques for Maximum Likelihood Estimation for Diffusion ODEs

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-08-10T20:56:10.404254Z

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Observation 1114dc03-8d04-41a4-a1e5-e458ff8ad4aa · outbound

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Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps Unresolved cited work

Reference 36

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Observation 0079fbdb-2474-4e0c-9f31-5fd7a305bdfe · outbound

This paper cites This is why we get differing behaviors between the two hierarchical maps.

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps This is why we get differing behaviors between the two hierarchical maps

Reference 38

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Observation 27849bb2-1674-4136-9de9-576a838e63dc · outbound

This paper cites All model architecture and hyper- parameters are retained from W-PCDM and LP-PCDM, except for the choice of z(1),.

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps All model architecture and hyper- parameters are retained from W-PCDM and LP-PCDM, except for the choice of z(1),

Reference 39

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Observation 3edfc101-ffe2-497d-9fc6-3b038da89bc6 · outbound

This paper cites Efficiently Modeling Long Sequences with Structured State Spaces.

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps Efficiently Modeling Long Sequences with Structured State Spaces

Reference 1995

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Observation 65a6c68f-a227-450f-835d-21f24ad73ab0 · outbound

This paper cites PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications.

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications

Reference 2000

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Observation 8818800f-e2bb-4652-b68a-667c68839efe · outbound

This paper cites Practical Lossless Compression with Latent Variables using Bits Back Coding.

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps Practical Lossless Compression with Latent Variables using Bits Back Coding

Reference 2003

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Observation de381e70-1ead-46c3-a714-06fb8852b62a · outbound

This paper cites Lemma A.1 (From Theorem 2 in (Shirdhonkar & Jacobs, 2008)).

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps Lemma A.1 (From Theorem 2 in (Shirdhonkar & Jacobs, 2008))

Reference 2008

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:56:10.884185Z

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Observation d63afaab-226d-4fe9-bcd0-f65334f36d6d · outbound

This paper cites Score-based Generative Modeling Secretly Minimizes the Wasserstein Distance.

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps Score-based Generative Modeling Secretly Minimizes the Wasserstein Distance

Reference 2009

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Observation 21ae3f99-7ab1-486f-b819-b0095109a3c1 · outbound

This paper cites WaveNet: A Generative Model for Raw Audio.

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps WaveNet: A Generative Model for Raw Audio

Reference 2011

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This paper cites Implicit Generation and Generalization in Energy-Based Models.

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps Implicit Generation and Generalization in Energy-Based Models

Reference 2014

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Observation aacc5bb6-3ad2-4a38-aa4f-5f33948c746b · outbound

This paper cites Elucidating the Design Space of Diffusion-Based Generative Models.

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps Elucidating the Design Space of Diffusion-Based Generative Models

Reference 2017

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Observation 813a0701-f176-41e5-8306-9def49578130 · outbound

This paper cites Likelihood Training of Schr\"odinger Bridge using Forward-Backward SDEs Theory.

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps Likelihood Training of Schr\"odinger Bridge using Forward-Backward SDEs Theory

Reference 2018

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Observation 60144a12-5a0f-4018-8c0a-5507123612b9 · outbound

This paper cites WAIC, but Why? Generative Ensembles for Robust Anomaly Detection.

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps WAIC, but Why? Generative Ensembles for Robust Anomaly Detection

Reference 2019

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Observation 139a9d4b-ac1e-469e-a3bf-d80faa0d1cdc · outbound

This paper cites Generating Long Sequences with Sparse Transformers.

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps Generating Long Sequences with Sparse Transformers

Reference 2020

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Observation 0419342b-7edb-4769-b2aa-7262a9c80de9 · outbound

This paper cites Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images.

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images

Reference 2021

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Observation 20c29356-3ef6-46c2-a6f6-d21a754e5ec5 · outbound

This paper cites Laplacian pyramid-like autoencoder.

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps Laplacian pyramid-like autoencoder

Reference 2022

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Observation 53eaad3e-9b80-4cb7-9cf8-49252b8c9435 · outbound

This paper cites Let h be a hierarchi- cal volume-preserving map such that h(x) = (z(1), z(2),.

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps Let h be a hierarchi- cal volume-preserving map such that h(x) = (z(1), z(2),

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:56:10.931723Z

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