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Paper Citation Record · LEDGER

Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions

As of 20 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 1 inbound Pith citation observation for arXiv:2501.15705.

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

pith.paper-citation-record.v1
2501.15705 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:06:43.866836Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T11:00:18.185759Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T11:00:18.224003Z

Reference resolution

31 of 31 outbound references displayed

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External citation measurements

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Outbound references

Observation 28bcbd93-182c-4147-8b58-f276608b40a4 · outbound

This paper cites Auto-encoding variational bayes,.

Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions Auto-encoding variational bayes,

Reference 1

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Observation ad8fb2f3-d8d9-410a-8435-d079f16c5880 · outbound

This paper cites Stochastic backprop- agation and approximate inference in deep generative models,.

Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions Stochastic backprop- agation and approximate inference in deep generative models,

Reference 2

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Observation 7c26cd2b-5edb-4db4-ba27-b2377582f0be · outbound

This paper cites Variational autoencoders pursue pca directions (by accident),.

Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions Variational autoencoders pursue pca directions (by accident),

Reference 3

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Observation 934eb5fb-682a-4792-8175-b23ad1276980 · outbound

This paper cites Score-based generative modeling through stochastic differ- ential equations,.

Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions Score-based generative modeling through stochastic differ- ential equations,

Reference 4

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Source-reported events for the cited work

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Observation 89d4bbd1-b202-44c1-a1ee-5161fc10ebc7 · outbound

This paper cites Matching aggregate posteriors in the variational autoencoder,.

Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions Matching aggregate posteriors in the variational autoencoder,

Reference 5

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

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

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

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Observation bb42708b-3348-4dcd-8418-431637e87eff · outbound

This paper cites Representation learning: A review and new perspectives,.

Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions Representation learning: A review and new perspectives,

Reference 7

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Observation 9f5839cc-8562-491c-8a31-98dac29a07cd · outbound

This paper cites Recent advances in autoencoder-based representation learning,.

Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions Recent advances in autoencoder-based representation learning,

Reference 8

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Observation f42c5947-4c9b-41e0-8652-713a2c24b9ff · outbound

This paper cites Few-shot segmentation of mi- croscopy images using gaussian process,.

Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions Few-shot segmentation of mi- croscopy images using gaussian process,

Reference 9

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Observation 4005bba6-790e-4274-8695-02b3c9999f51 · outbound

This paper cites Multitask training as regularization strategy for seismic image segmentation,.

Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions Multitask training as regularization strategy for seismic image segmentation,

Reference 10

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Observation a3423367-99ad-45fc-8b78-ca18bca6fb4b · outbound

This paper cites Real-time idling vehicles detection using combined audio-visual deep learning,.

Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions Real-time idling vehicles detection using combined audio-visual deep learning,

Reference 11

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Observation 9a38b28e-3d21-4c6d-ba10-dd8fb399d8e1 · outbound

This paper cites Joint Audio-Visual Idling Vehicle Detection with Streamlined Input Dependencies.

Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions Joint Audio-Visual Idling Vehicle Detection with Streamlined Input Dependencies

Reference 12

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Source-reported events for the cited work

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Observation 489e8b7e-d3a3-414d-ac3d-9e1ee4e92151 · outbound

This paper cites Optimization as a model for few-shot learning,.

Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions Optimization as a model for few-shot learning,

Reference 13

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Observation f29be3b1-a4a8-4678-87d0-6b109026310b · outbound

This paper cites Model-agnostic meta-learning for fast adaptation of deep networks,.

Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions Model-agnostic meta-learning for fast adaptation of deep networks,

Reference 14

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Unavailable: canonical work link unavailable.

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Observation 60ed41b2-aff2-48e9-a721-1ec4c07d3ab9 · outbound

This paper cites Challenging common assumptions in the unsupervised learning of disentangled representations,.

Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions Challenging common assumptions in the unsupervised learning of disentangled representations,

Reference 15

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Observation d09be10d-9297-4aaa-9a68-ae781f5eee59 · outbound

This paper cites β-vae: Learning basic visual concepts with a constrained variational framework,.

Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions β-vae: Learning basic visual concepts with a constrained variational framework,

Reference 16

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Source-reported events for the cited work

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Observation 903a82f2-ca9b-4508-bb0a-679ab4cc5bb3 · outbound

This paper cites Disentangling by factorising,.

Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions Disentangling by factorising,

Reference 17

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Observation e2a138cc-7a0c-4065-9d1c-a9fbb52e87f7 · outbound

This paper cites Isolating sources of disentanglement in vaes,.

Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions Isolating sources of disentanglement in vaes,

Reference 18

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Source-reported events for the cited work

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Observation 4cbff59b-4d55-416a-b1b9-e64ec81fc834 · outbound

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

Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions Variational inference of disentangled latent concepts from unlabeled observations,

Reference 19

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Observation 91de2284-1032-4413-8002-88fba8b75556 · outbound

This paper cites A framework for the quantitative evaluation of disentangled representations,.

Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions A framework for the quantitative evaluation of disentangled representations,

Reference 20

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Observation fc24c20b-9c13-4979-a1e5-bdd9da9634cf · outbound

This paper cites DCI-ES: An extended disentanglement framework with connections to identifiability,.

Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions DCI-ES: An extended disentanglement framework with connections to identifiability,

Reference 21

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Observation 35b6af02-8630-4044-ad3f-12e116952960 · outbound

This paper cites Elbo surgery: yet another way to carve up the variational evidence lower bound.

Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions Elbo surgery: yet another way to carve up the variational evidence lower bound

Reference 22

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Observation 5250b85c-da38-4159-99dc-0d2acc7d8ec2 · outbound

This paper cites Understanding posterior collapse in generative latent variable models,.

Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions Understanding posterior collapse in generative latent variable models,

Reference 23

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Observation 04377524-cb85-4697-b882-25efe1a1ce51 · outbound

This paper cites Don’t blame the elbo! a linear vae perspective on posterior collapse,.

Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions Don’t blame the elbo! a linear vae perspective on posterior collapse,

Reference 24

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Observation 558b6933-6eac-4d96-b8e3-973379cb25b0 · outbound

This paper cites Distribution Matching in Variational Inference.

Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions Distribution Matching in Variational Inference

Reference 25

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Unavailable: canonical work link unavailable.

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Observation 030e8e8a-e3b3-4eab-a4e0-d7c4261924fb · outbound

This paper cites Adver- sarial autoencoders,.

Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions Adver- sarial autoencoders,

Reference 26

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Source-reported events for the cited work

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Observation c01005fd-5197-4cb8-bfe4-8821085d5e30 · outbound

This paper cites Wasserstein auto-encoders,.

Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions Wasserstein auto-encoders,

Reference 27

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 04de677d-091c-4ede-bea0-1a89024a44a8 · outbound

This paper cites Gens: generative encoding networks,.

Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions Gens: generative encoding networks,

Reference 28

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation cd6b8559-b7de-451b-a4ab-9eead3cc5ec0 · outbound

This paper cites 3d shapes dataset,.

Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions 3d shapes dataset,

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation fa1a5334-b94d-4249-aab2-2a2722feadd1 · outbound

This paper cites From variational to deterministic autoencoders,.

Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions From variational to deterministic autoencoders,

Reference 30

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raw_fallback, observed 2026-08-10T14:06:43.975543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 66c4b09f-ba8d-4372-b0a4-1de2d0d89a21 · outbound

This paper cites dsprites: Disentanglement testing sprites dataset,.

Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions dsprites: Disentanglement testing sprites dataset,

Reference 31

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Pith citing papers

Observation c8e24836-669d-4c10-b84b-a7c77ca75abf · inbound

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

AdaSemSeg: An Adaptive Few-shot Semantic Segmentation of Seismic Facies Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions

Reference 48

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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