Paper Citation Record · LEDGER

Improving the Generation of VAEs with High Dimensional Latent Spaces by the use of Hyperspherical Coordinates

As of 8 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2507.15900.

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

pith.paper-citation-record.v1
2507.15900 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:41:25.745032Z

measured 24 of 24 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 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

24 of 24 outbound references displayed

verified exact3
verified fuzzy14
unresolved6
parse uncertain0
malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3214f19f-a404-40c1-ae73-4bbfb3040320 · outbound

This paper cites Auto-Encoding Variational Bayes.

Improving the Generation of VAEs with High Dimensional Latent Spaces by the use of Hyperspherical Coordinates Auto-Encoding Variational Bayes

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T15:41:23.770022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11736fde-ca35-43db-9974-853523e2ebe3 · outbound

This paper cites An Introduction to Variational Autoencoders,.

Improving the Generation of VAEs with High Dimensional Latent Spaces by the use of Hyperspherical Coordinates An Introduction to Variational Autoencoders,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:41:28.993043Z

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 b1054ac2-dcb4-420a-87da-4d871376fcbc · outbound

This paper cites Variational Autoencoders Pursue PCA Directions (by Accident),.

Improving the Generation of VAEs with High Dimensional Latent Spaces by the use of Hyperspherical Coordinates Variational Autoencoders Pursue PCA Directions (by Accident),

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:41:28.802898Z

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 8b1ea34f-56eb-4e58-b2c6-edd140a88bd8 · outbound

This paper cites Why do Variational Autoencoders Really Promote Disentanglement?.

Improving the Generation of VAEs with High Dimensional Latent Spaces by the use of Hyperspherical Coordinates Why do Variational Autoencoders Really Promote Disentanglement?

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:41:28.612257Z

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Observation ece534a3-8b1d-4d0a-a999-0001f64db845 · outbound

This paper cites β-V AE: Learning Basic Visual Concepts with a Constrained Variational Framework,.

Improving the Generation of VAEs with High Dimensional Latent Spaces by the use of Hyperspherical Coordinates β-V AE: Learning Basic Visual Concepts with a Constrained Variational Framework,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:41:28.361312Z

Source-reported events for the cited work

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Observation 6d48b33e-de4a-4f0c-aef8-904887fc8b95 · outbound

This paper cites Vershynin, High-Dimensional Probability.

Improving the Generation of VAEs with High Dimensional Latent Spaces by the use of Hyperspherical Coordinates Vershynin, High-Dimensional Probability

Reference 6

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Source-reported events for the cited work

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Observation 1e2ea8c3-a1c0-4445-a361-393148e447b4 · outbound

This paper cites Concentration of measure,.

Improving the Generation of VAEs with High Dimensional Latent Spaces by the use of Hyperspherical Coordinates Concentration of measure,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:41:28.030267Z

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 d0a7e253-49d8-4a79-9e53-2140a6172636 · outbound

This paper cites an unresolved cited work.

Improving the Generation of VAEs with High Dimensional Latent Spaces by the use of Hyperspherical Coordinates Unresolved cited work

Reference 8

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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 1074b148-894a-44b2-b10b-747ae270032b · outbound

This paper cites Denoising Diffusion Probabilistic Models.

Improving the Generation of VAEs with High Dimensional Latent Spaces by the use of Hyperspherical Coordinates Denoising Diffusion Probabilistic Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T15:41:24.592779Z

Source-reported events for the cited work

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Observation afbccda2-e0ae-4ea2-bd5d-1becc3b56ff4 · outbound

This paper cites Interpreting the Curse of Dimensionality from Distance Concentration and Manifold Effect,.

Improving the Generation of VAEs with High Dimensional Latent Spaces by the use of Hyperspherical Coordinates Interpreting the Curse of Dimensionality from Distance Concentration and Manifold Effect,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:41:27.668350Z

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 efc62288-d8e8-4f34-87f3-072df8315926 · outbound

This paper cites Hyperspherical Variational Auto-Encoders,.

Improving the Generation of VAEs with High Dimensional Latent Spaces by the use of Hyperspherical Coordinates Hyperspherical Variational Auto-Encoders,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:41:27.475863Z

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 4577bb7e-3051-4cac-aa78-564d7000d0e9 · outbound

This paper cites Spherical Sliced-Wasserstein.

Improving the Generation of VAEs with High Dimensional Latent Spaces by the use of Hyperspherical Coordinates Spherical Sliced-Wasserstein

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:41:26.230972Z

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 5f55c3d4-ce08-4194-96e8-2171484c9018 · outbound

This paper cites Sliced-Wasserstein Autoencoder: An Embarrassingly Simple Generative Model.

Improving the Generation of VAEs with High Dimensional Latent Spaces by the use of Hyperspherical Coordinates Sliced-Wasserstein Autoencoder: An Embarrassingly Simple Generative Model

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:41:26.051804Z

Source-reported events for the cited work

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Observation 2ed6b80d-74c7-4a41-9912-958b89f67a66 · outbound

This paper cites Rotating Features for Object Discovery.

Improving the Generation of VAEs with High Dimensional Latent Spaces by the use of Hyperspherical Coordinates Rotating Features for Object Discovery

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:41:25.906826Z

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 bdfe0b27-df14-48e8-a748-051dbc8cb2ac · outbound

This paper cites VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning.

Improving the Generation of VAEs with High Dimensional Latent Spaces by the use of Hyperspherical Coordinates VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T15:41:25.148410Z

Source-reported events for the cited work

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Observation 6d35184d-9573-4211-8527-c66b0edde8a6 · outbound

This paper cites Deep Residual Learning for Image Recognition,.

Improving the Generation of VAEs with High Dimensional Latent Spaces by the use of Hyperspherical Coordinates Deep Residual Learning for Image Recognition,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:41:27.157554Z

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 1a5ce8bd-a2b0-492d-800b-219195c36ad0 · outbound

This paper cites Cyclical Annealing Schedule: A Simple Approach to Mitigating KL Vanishing.

Improving the Generation of VAEs with High Dimensional Latent Spaces by the use of Hyperspherical Coordinates Cyclical Annealing Schedule: A Simple Approach to Mitigating KL Vanishing

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T15:41:25.296821Z

Source-reported events for the cited work

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Observation 40791ccb-36fe-4dbf-8245-c1aa247b35c3 · outbound

This paper cites Gradient-based learning applied to document recogni- tion.

Improving the Generation of VAEs with High Dimensional Latent Spaces by the use of Hyperspherical Coordinates Gradient-based learning applied to document recogni- tion

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:41:27.021998Z

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 b708adbf-fbe9-4429-8c04-0ec4bb31e52c · outbound

This paper cites GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium.

Improving the Generation of VAEs with High Dimensional Latent Spaces by the use of Hyperspherical Coordinates GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T15:41:25.445041Z

Source-reported events for the cited work

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Observation 515f811e-5003-45dd-80f8-31c2e509fc6f · outbound

This paper cites Learning Multiple Layers of Features from Tiny Im- ages,.

Improving the Generation of VAEs with High Dimensional Latent Spaces by the use of Hyperspherical Coordinates Learning Multiple Layers of Features from Tiny Im- ages,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:41:26.866961Z

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 bf09ddc4-284e-4973-b8b9-4109277d9b0c · outbound

This paper cites Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models,.

Improving the Generation of VAEs with High Dimensional Latent Spaces by the use of Hyperspherical Coordinates Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:41:26.719000Z

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 33183f70-cdb7-416e-b4e3-23e9f5d462a5 · outbound

This paper cites Deep Learning Face Attributes in the Wild,.

Improving the Generation of VAEs with High Dimensional Latent Spaces by the use of Hyperspherical Coordinates Deep Learning Face Attributes in the Wild,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:41:26.376863Z

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 eda74425-ed90-40c0-922b-0ab206d17031 · outbound

This paper cites Available: https://github.com/nicola-decao/s-vae.

Improving the Generation of VAEs with High Dimensional Latent Spaces by the use of Hyperspherical Coordinates Available: https://github.com/nicola-decao/s-vae

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:41:27.290841Z

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 53ca6074-52e7-4ca2-bcce-6f69f522d855 · outbound

This paper cites Available: https://github.com/layer6ai-labs/dgm-eval.

Improving the Generation of VAEs with High Dimensional Latent Spaces by the use of Hyperspherical Coordinates Available: https://github.com/layer6ai-labs/dgm-eval

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:41:26.570080Z

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.

Pith citing papers

No inbound Pith citation observations are available.