Paper Citation Record · LEDGER

Joint-stochastic-approximation Autoencoders with Application to Semi-supervised Learning

As of 13 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2505.18558.

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

pith.paper-citation-record.v1
2505.18558 v1

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:35:13.757865Z

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

13 of 13 outbound references displayed

verified exact4
verified fuzzy0
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 13f7e1cd-7b5f-4cd4-b218-dbd0a35b64a7 · outbound

This paper cites Wasserstein GAN.

Joint-stochastic-approximation Autoencoders with Application to Semi-supervised Learning Wasserstein GAN

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T14:35:12.716539Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb34f87e-eeb1-4c46-85a1-80cfd24e4be3 · outbound

This paper cites Adversarially Learned Inference.

Joint-stochastic-approximation Autoencoders with Application to Semi-supervised Learning Adversarially Learned Inference

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T14:35:13.144069Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32f81b5c-ce2f-4850-80c7-b0f1ca9a958f · outbound

This paper cites The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables.

Joint-stochastic-approximation Autoencoders with Application to Semi-supervised Learning The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T14:35:13.378168Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8cf2d3ad-e693-4314-b858-013d4521ee48 · outbound

This paper cites Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning.

Joint-stochastic-approximation Autoencoders with Application to Semi-supervised Learning Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T14:35:13.523130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90adfa10-a6d5-4567-abf8-bcb9351e8937 · outbound

This paper cites Symmetric Variational Autoencoder and Connections to Adversarial Learning.

Joint-stochastic-approximation Autoencoders with Application to Semi-supervised Learning Symmetric Variational Autoencoder and Connections to Adversarial Learning

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:35:14.168839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a8f9e09c-9478-402e-a80f-4e80ff5fd407 · outbound

This paper cites Wasserstein Auto-Encoders.

Joint-stochastic-approximation Autoencoders with Application to Semi-supervised Learning Wasserstein Auto-Encoders

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T14:35:13.683669Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 26222378-9b46-4b93-a7d9-d0d2e5db288b · outbound

This paper cites Joint Stochastic Approximation learning of Helmholtz Machines.

Joint-stochastic-approximation Autoencoders with Application to Semi-supervised Learning Joint Stochastic Approximation learning of Helmholtz Machines

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:35:13.966146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 29179935-4504-4a41-918b-bbbfa2ee3d36 · outbound

This paper cites Adversarial Feature Learning.

Joint-stochastic-approximation Autoencoders with Application to Semi-supervised Learning Adversarial Feature Learning

Reference 1995

Resolution: unresolved; no resolver link, observed 2026-08-07T14:35:13.106095Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba3a3ba8-da9a-4e8d-aa6a-23028c94be7a · outbound

This paper cites Reweighted Wake-Sleep.

Joint-stochastic-approximation Autoencoders with Application to Semi-supervised Learning Reweighted Wake-Sleep

Reference 1998

Resolution: unresolved; no resolver link, observed 2026-08-07T14:35:12.900538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7cc2963b-c71b-4ff8-bda0-0ba837ff7e0a · outbound

This paper cites GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution.

Joint-stochastic-approximation Autoencoders with Application to Semi-supervised Learning GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution

Reference 2014

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:35:14.551598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2c630ddb-9024-4d62-ae53-428a85eb28f0 · outbound

This paper cites Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks.

Joint-stochastic-approximation Autoencoders with Application to Semi-supervised Learning Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks

Reference 2015

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:35:14.341098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 57333b83-efb5-46a9-b49a-0357606ca3e1 · outbound

This paper cites Importance Weighted Autoencoders.

Joint-stochastic-approximation Autoencoders with Application to Semi-supervised Learning Importance Weighted Autoencoders

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-07T14:35:13.004539Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36b59125-ed28-43ac-82d5-a87763694e05 · outbound

This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

Joint-stochastic-approximation Autoencoders with Application to Semi-supervised Learning Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-07T14:35:12.802325Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.