Paper Citation Record · LEDGER

ARD-VAE: A Statistical Formulation to Find the Relevant Latent Dimensions of Variational Autoencoders

As of 12 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 2 inbound Pith citation observations for arXiv:2501.10901.

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

pith.paper-citation-record.v1
2501.10901 v2

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T18:58:30.009519Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:06:43.758175Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-10T11:00:18.242744Z

Reference resolution

14 of 14 outbound references displayed

verified exact0
verified fuzzy12
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 744334d9-4670-446f-aa8a-cbe53e64709b · outbound

This paper cites 3d shapes dataset.

ARD-VAE: A Statistical Formulation to Find the Relevant Latent Dimensions of Variational Autoencoders 3d shapes dataset

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:58:30.868326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ae3c4468-8a10-4d26-90b9-6f9064527845 · outbound

This paper cites Dynamic narrowing of vae bottle- necks using geco and l0 regularization.

ARD-VAE: A Statistical Formulation to Find the Relevant Latent Dimensions of Variational Autoencoders Dynamic narrowing of vae bottle- necks using geco and l0 regularization

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:58:30.833980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b557b262-857a-4a9a-a3ee-103d6dd52959 · outbound

This paper cites Imagenet: A large-scale hierar- chical image database.

ARD-VAE: A Statistical Formulation to Find the Relevant Latent Dimensions of Variational Autoencoders Imagenet: A large-scale hierar- chical image database

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:58:30.767601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b611bc54-8cac-4767-be0d-3e44b6bae945 · outbound

This paper cites an unresolved cited work.

ARD-VAE: A Statistical Formulation to Find the Relevant Latent Dimensions of Variational Autoencoders Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-10T18:58:30.709331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 53e2538c-0142-41b1-91bf-8c8721bb6094 · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium.

ARD-VAE: A Statistical Formulation to Find the Relevant Latent Dimensions of Variational Autoencoders Gans trained by a two time-scale update rule converge to a local nash equilibrium

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:58:30.679890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d8dc8493-795d-4e82-96e7-a8a051fcaa13 · outbound

This paper cites Disentangling by factorising.

ARD-VAE: A Statistical Formulation to Find the Relevant Latent Dimensions of Variational Autoencoders Disentangling by factorising

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:58:30.587456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0602c9b6-0142-44a5-aff6-1f39f2fdfed5 · outbound

This paper cites Auto-encoding variational bayes.

ARD-VAE: A Statistical Formulation to Find the Relevant Latent Dimensions of Variational Autoencoders Auto-encoding variational bayes

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:58:30.509251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ebf573a4-b34d-4b8b-bd56-1c595ec30e8e · outbound

This paper cites Variational inference of disentangled latent concepts from unlabeled observations.

ARD-VAE: A Statistical Formulation to Find the Relevant Latent Dimensions of Variational Autoencoders Variational inference of disentangled latent concepts from unlabeled observations

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:58:30.493471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation feb269ee-2d9a-4949-871b-1be8a800a63e · outbound

This paper cites Challenging common assumptions in the unsupervised learning of disentangled representa- tions.

ARD-VAE: A Statistical Formulation to Find the Relevant Latent Dimensions of Variational Autoencoders Challenging common assumptions in the unsupervised learning of disentangled representa- tions

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:58:30.411714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e9869a0b-d16d-4178-a397-1aad2c00dab0 · outbound

This paper cites dsprites: Disentanglement testing sprites dataset.

ARD-VAE: A Statistical Formulation to Find the Relevant Latent Dimensions of Variational Autoencoders dsprites: Disentanglement testing sprites dataset

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:58:30.341559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 25b59778-739e-4dca-b766-a6751deb5935 · outbound

This paper cites Maskaae: Latent space optimization for adversarial auto-encoders.

ARD-VAE: A Statistical Formulation to Find the Relevant Latent Dimensions of Variational Autoencoders Maskaae: Latent space optimization for adversarial auto-encoders

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:58:30.280165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 70c84124-7b54-43f8-be68-259d018e7109 · outbound

This paper cites Stochastic backpropagation and approxi- mate inference in deep generative models.

ARD-VAE: A Statistical Formulation to Find the Relevant Latent Dimensions of Variational Autoencoders Stochastic backpropagation and approxi- mate inference in deep generative models

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:58:30.184026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5e63b425-3ff5-4953-baf8-3b46300c41af · outbound

This paper cites an unresolved cited work.

ARD-VAE: A Statistical Formulation to Find the Relevant Latent Dimensions of Variational Autoencoders Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-08-10T18:58:30.108089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 39871fca-d2a0-4bce-8df1-3ece03753a29 · outbound

This paper cites Wasserstein auto-encoders.

ARD-VAE: A Statistical Formulation to Find the Relevant Latent Dimensions of Variational Autoencoders Wasserstein auto-encoders

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:58:30.063400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

Observation dc2d728f-e3f3-4814-83df-99f0cdc5fa9b · inbound

Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions cites this paper.

Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions ARD-VAE: A Statistical Formulation to Find the Relevant Latent Dimensions of Variational Autoencoders

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T14:06:43.758175Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 175e0f3e-0bb7-4ffe-b572-632bcf49be20 · inbound

AdaSemSeg: An Adaptive Few-shot Semantic Segmentation of Seismic Facies cites this paper.

AdaSemSeg: An Adaptive Few-shot Semantic Segmentation of Seismic Facies ARD-VAE: A Statistical Formulation to Find the Relevant Latent Dimensions of Variational Autoencoders

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-08-10T11:00:18.247278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.