Paper Citation Record · LEDGER

Variational Encoder-Decoders for Learning Latent Representations of Physical Systems

As of 20 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 1 inbound Pith citation observation for arXiv:2412.05175.

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

pith.paper-citation-record.v1
2412.05175 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:58:01.253901Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-09T03:51:36.049656Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-09T03:55:54.990212Z

Reference resolution

35 of 35 outbound references displayed

verified exact3
verified fuzzy19
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 972c457b-dacc-4a57-99b7-dc812daa65c9 · outbound

This paper cites Geometric disentanglement for generative latent shape models.

Variational Encoder-Decoders for Learning Latent Representations of Physical Systems Geometric disentanglement for generative latent shape models

Reference 1

Resolution: verified exact; raw_fallback, observed 2026-08-11T20:58:01.668519Z

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Observation 954f50d9-17a3-4973-bcf7-5b8c13ff7daf · outbound

This paper cites Non-linear dimensionality reduction with a varia- tional encoder decoder to understand convective processes in climate models.

Variational Encoder-Decoders for Learning Latent Representations of Physical Systems Non-linear dimensionality reduction with a varia- tional encoder decoder to understand convective processes in climate models

Reference 2

Resolution: verified exact; doi, observed 2026-08-11T20:58:01.384891Z

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Observation df13bbe1-3aeb-496d-aa72-8938d24cd82e · outbound

This paper cites Representation Learning: A Review and New Perspectives.

Variational Encoder-Decoders for Learning Latent Representations of Physical Systems Representation Learning: A Review and New Perspectives

Reference 3

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Observation 8f0f3c42-8808-4fa7-8100-8319c56acfeb · outbound

This paper cites Isolating sources of disentanglement in variational autoencoders.

Variational Encoder-Decoders for Learning Latent Representations of Physical Systems Isolating sources of disentanglement in variational autoencoders

Reference 4

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Observation 92114251-5cd3-48cc-b0b0-d61376fea1f4 · outbound

This paper cites Transient inverse calibration of Hanford site-wide ground- water model to Hanford operational impacts – 1943 to 1996.

Variational Encoder-Decoders for Learning Latent Representations of Physical Systems Transient inverse calibration of Hanford site-wide ground- water model to Hanford operational impacts – 1943 to 1996

Reference 5

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Observation c11e4acb-8f4c-4bf3-91f2-199012e2a5d7 · outbound

This paper cites Tutorial on Variational Autoencoders.

Variational Encoder-Decoders for Learning Latent Representations of Physical Systems Tutorial on Variational Autoencoders

Reference 6

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Observation d7171d17-8c4d-4ec0-81e1-f4ee80cce3a8 · outbound

This paper cites Structured dis- entangled representations.

Variational Encoder-Decoders for Learning Latent Representations of Physical Systems Structured dis- entangled representations

Reference 7

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Observation 01092def-c1af-4ef1-a2d7-5c8a0b1b9689 · outbound

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Variational Encoder-Decoders for Learning Latent Representations of Physical Systems Unresolved cited work

Reference 8

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Observation 5dcf285e-726b-4cae-bddf-dbc1fd8a5486 · outbound

This paper cites Deep residual learning for image recognition.

Variational Encoder-Decoders for Learning Latent Representations of Physical Systems Deep residual learning for image recognition

Reference 9

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Observation c9e83bc5-cf94-431d-bc7d-5fd5a7c759fb · outbound

This paper cites beta-V AE: Learning basic vi- sual concepts with a constrained variational framework.

Variational Encoder-Decoders for Learning Latent Representations of Physical Systems beta-V AE: Learning basic vi- sual concepts with a constrained variational framework

Reference 10

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Observation 11ab9bcf-fed3-4375-8c12-ca51533543ec · outbound

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Variational Encoder-Decoders for Learning Latent Representations of Physical Systems Unresolved cited work

Reference 11

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Observation 9444c0e3-0588-461c-901c-f30f3600cef6 · outbound

This paper cites Batch normalization: accelerating deep network training by reducing internal covariate shift.

Variational Encoder-Decoders for Learning Latent Representations of Physical Systems Batch normalization: accelerating deep network training by reducing internal covariate shift

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:58:01.980333Z

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Observation 03ea933a-d9ee-4714-9c28-ce9af499b2fe · outbound

This paper cites Disentangling generative factors of phys- ical fields using variational autoencoders.

Variational Encoder-Decoders for Learning Latent Representations of Physical Systems Disentangling generative factors of phys- ical fields using variational autoencoders

Reference 13

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Observation db85edfb-aa06-479c-a577-3f0c60a23ccf · outbound

This paper cites Deep encoder, shallow decoder: Reevaluating non-autoregressive machine translation.

Variational Encoder-Decoders for Learning Latent Representations of Physical Systems Deep encoder, shallow decoder: Reevaluating non-autoregressive machine translation

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:58:01.945414Z

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Observation 7cf710d0-07ef-4179-943d-ef4f8ac24ab5 · outbound

This paper cites Disentangling by factorising.

Variational Encoder-Decoders for Learning Latent Representations of Physical Systems Disentangling by factorising

Reference 15

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Observation d603d38b-e9fa-442d-82b5-5ca45f391262 · outbound

This paper cites $\Gamma$-VAE: Curvature regularized variational autoencoders for uncovering emergent low dimensional geometric structure in high dimensional data.

Variational Encoder-Decoders for Learning Latent Representations of Physical Systems $\Gamma$-VAE: Curvature regularized variational autoencoders for uncovering emergent low dimensional geometric structure in high dimensional data

Reference 16

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Observation 5e6730a2-f2c4-465b-abd5-ed78c7b2a5d4 · outbound

This paper cites Kingma and Jimmy Ba.

Variational Encoder-Decoders for Learning Latent Representations of Physical Systems Kingma and Jimmy Ba

Reference 17

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Observation 5b74d5b3-1aff-4d85-bb24-19c690b9a240 · outbound

This paper cites Auto-Encoding Variational Bayes.

Variational Encoder-Decoders for Learning Latent Representations of Physical Systems Auto-Encoding Variational Bayes

Reference 18

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Observation 31eba760-ad13-4a78-9e07-75aac84bd133 · outbound

This paper cites Kipf and Max Welling.

Variational Encoder-Decoders for Learning Latent Representations of Physical Systems Kipf and Max Welling

Reference 19

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Variational Encoder-Decoders for Learning Latent Representations of Physical Systems Unresolved cited work

Reference 20

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Observation 056576a2-95a3-41b2-af3f-533cbcc8d307 · outbound

This paper cites Variational infer- ence of disentangled latent concepts from unlabeled observations.

Variational Encoder-Decoders for Learning Latent Representations of Physical Systems Variational infer- ence of disentangled latent concepts from unlabeled observations

Reference 21

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Observation d5fb589a-0d7c-462e-98d5-83285f4e5d56 · outbound

This paper cites Lecun, L.

Variational Encoder-Decoders for Learning Latent Representations of Physical Systems Lecun, L

Reference 22

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Observation c18edbb7-b140-4018-9287-22f682c93ad0 · outbound

This paper cites Information con- straints on auto-encoding variational bayes.Advances in neural information processing systems, 31, 2018.

Variational Encoder-Decoders for Learning Latent Representations of Physical Systems Information con- straints on auto-encoding variational bayes.Advances in neural information processing systems, 31, 2018

Reference 23

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Observation b9038986-7a4d-4384-b1a8-1579e9e96a9f · outbound

This paper cites Stacked convolu- tional auto-encoders for hierarchical feature extraction.

Variational Encoder-Decoders for Learning Latent Representations of Physical Systems Stacked convolu- tional auto-encoders for hierarchical feature extraction

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:58:01.831644Z

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Observation 517149a1-9301-4cb2-85c5-fd521bb6d1fa · outbound

This paper cites Disentangling disentanglement in variational autoencoders.

Variational Encoder-Decoders for Learning Latent Representations of Physical Systems Disentangling disentanglement in variational autoencoders

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:58:01.814633Z

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Observation 430266c3-8a5b-4b3e-aa09-3191af5aa0df · outbound

This paper cites Linear and nonlinear dimensionality reduction from fluid me- chanics to machine learning.

Variational Encoder-Decoders for Learning Latent Representations of Physical Systems Linear and nonlinear dimensionality reduction from fluid me- chanics to machine learning

Reference 26

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Observation d0158cec-289d-4526-8a4a-08392a347f9d · outbound

This paper cites On the difficulty of train- ing recurrent neural networks.

Variational Encoder-Decoders for Learning Latent Representations of Physical Systems On the difficulty of train- ing recurrent neural networks

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:58:01.797335Z

Source-reported events for the cited work

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Observation 3a5e89c4-8dad-44dc-bf3f-6386f8f42f95 · outbound

This paper cites Simple and effective vae training with calibrated decoders.

Variational Encoder-Decoders for Learning Latent Representations of Physical Systems Simple and effective vae training with calibrated decoders

Reference 28

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Observation 6ff3afd2-9bce-4e00-8543-11d918cb37d1 · outbound

This paper cites Assessing the interplay of shape and physical parameters by nonlinear dimensionality reduction methods.

Variational Encoder-Decoders for Learning Latent Representations of Physical Systems Assessing the interplay of shape and physical parameters by nonlinear dimensionality reduction methods

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:58:01.760482Z

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Observation 179168fb-eb9d-42b7-9121-6f3eb29f2646 · outbound

This paper cites Learning structured out- put representation using deep conditional generative models.

Variational Encoder-Decoders for Learning Latent Representations of Physical Systems Learning structured out- put representation using deep conditional generative models

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:58:01.741899Z

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Observation bb9d4f83-b5a5-4f88-86a7-f3ee8b90d166 · outbound

This paper cites Unsupervised geomet- ric disentanglement via CF AN-V AE.

Variational Encoder-Decoders for Learning Latent Representations of Physical Systems Unsupervised geomet- ric disentanglement via CF AN-V AE

Reference 31

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Observation 0d7a128a-b0db-43b5-83e3-36b1745f609e · outbound

This paper cites Recent Advances in Autoencoder-Based Representation Learning.

Variational Encoder-Decoders for Learning Latent Representations of Physical Systems Recent Advances in Autoencoder-Based Representation Learning

Reference 32

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This paper cites Barajas-Solano, and Alexandre M.

Variational Encoder-Decoders for Learning Latent Representations of Physical Systems Barajas-Solano, and Alexandre M

Reference 33

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Observation 9a5310d2-4b5f-42bf-b860-838bf956161a · outbound

This paper cites Zeiler and Rob Fergus.

Variational Encoder-Decoders for Learning Latent Representations of Physical Systems Zeiler and Rob Fergus

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:58:01.686102Z

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Observation b0c75417-d76c-4f07-8a5f-1958e4d1f9b1 · outbound

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Variational Encoder-Decoders for Learning Latent Representations of Physical Systems Unresolved cited work

Reference 2015

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Pith citing papers

Observation f880c389-0c33-42b0-aff4-089be827300d · inbound

Pic2Spec: Generative Modeling Reconstructs Single Cell Raman Fingerprints from Brightfield Images cites this paper.

Pic2Spec: Generative Modeling Reconstructs Single Cell Raman Fingerprints from Brightfield Images Variational Encoder-Decoders for Learning Latent Representations of Physical Systems

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-07-09T03:55:54.992024Z

Source-reported events for the cited work

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