Paper Citation Record · LEDGER

NVAE: A Deep Hierarchical Variational Autoencoder

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2007.03898.

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

pith.paper-citation-record.v1
2007.03898 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:59:20.474339Z

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

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

378
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation f248eadc-2fc7-4586-8779-526fcc5c135c · inbound

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

Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed NVAE: A Deep Hierarchical Variational Autoencoder

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-17T03:56:19.935875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3a18e48f-b014-4fc6-843b-b7e257d94ab1 · inbound

Improved Denoising Diffusion Probabilistic Models cites this paper.

Improved Denoising Diffusion Probabilistic Models NVAE: A Deep Hierarchical Variational Autoencoder

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-16T19:19:14.988645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6dce3904-8bc5-48bb-9385-a46650fd13e1 · inbound

VideoGPT: Video Generation using VQ-VAE and Transformers cites this paper.

VideoGPT: Video Generation using VQ-VAE and Transformers NVAE: A Deep Hierarchical Variational Autoencoder

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-13T17:24:33.843374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b5983aa4-3801-450c-ab26-5eb9e1a17712 · inbound

Diffusion Models Beat GANs on Image Synthesis cites this paper.

Diffusion Models Beat GANs on Image Synthesis NVAE: A Deep Hierarchical Variational Autoencoder

Reference 63

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T11:16:28.484382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 14cfbb91-6066-4b03-8266-fbef3d3f86dd · inbound

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

Hierarchical Text-Conditional Image Generation with CLIP Latents NVAE: A Deep Hierarchical Variational Autoencoder

Reference 51

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T16:55:57.905008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 46122b67-636e-42cf-9df6-26d5edfc1780 · inbound

Continuous Semi-Implicit Models cites this paper.

Continuous Semi-Implicit Models NVAE: A Deep Hierarchical Variational Autoencoder

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T05:59:20.474339Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50288d00-1870-4b58-8bd7-51e9f94d1325 · inbound

Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection cites this paper.

Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection NVAE: A Deep Hierarchical Variational Autoencoder

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T04:42:37.657013Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2de779ae-92ad-4f11-b743-ec0b20e16d9a · inbound

Case Studies of Generative Machine Learning Models for Dynamical Systems cites this paper.

Case Studies of Generative Machine Learning Models for Dynamical Systems NVAE: A Deep Hierarchical Variational Autoencoder

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T00:01:46.848770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8493a22b-1895-46d9-9d3c-8b83d679aea3 · inbound

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models cites this paper.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models NVAE: A Deep Hierarchical Variational Autoencoder

Reference 24

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T21:38:18.467402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b9ae83cf-dbc0-47f1-af1d-cd921f31ebd3 · inbound

Learning to Theorize the World from Observation cites this paper.

Learning to Theorize the World from Observation NVAE: A Deep Hierarchical Variational Autoencoder

Reference 141

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T23:21:29.259353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 20888804-65e4-43f0-b150-2f9bc4b2e91b · inbound

eXact-Prior Variational Autoencoder (X-VAE): Learning Data-Adaptive Gaussian Mixture Priors for Latent Distributions cites this paper.

eXact-Prior Variational Autoencoder (X-VAE): Learning Data-Adaptive Gaussian Mixture Priors for Latent Distributions NVAE: A Deep Hierarchical Variational Autoencoder

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-07-03T21:48:58.964827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.