Paper Citation Record · LEDGER

Machine learning the Ising transition: A comparison between discriminative and generative approaches

As of 13 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2411.19370.

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

pith.paper-citation-record.v1
2411.19370 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T10:18:05.817532Z

measured 23 of 23 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

23 of 23 outbound references displayed

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verified fuzzy18
unresolved5
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 746a255a-a5bc-4000-9324-7c214d83e71e · outbound

This paper cites Sachdev, Quantum Phase Transitions.

Machine learning the Ising transition: A comparison between discriminative and generative approaches Sachdev, Quantum Phase Transitions

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:18:06.363564Z

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 55bead18-14a7-4c0d-8e9f-a4de57d5f3b1 · outbound

This paper cites Goldenfeld, Lectures On Phase Transitions And The Renormalization Group.

Machine learning the Ising transition: A comparison between discriminative and generative approaches Goldenfeld, Lectures On Phase Transitions And The Renormalization Group

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:18:06.349457Z

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 426b3d6d-3c4b-4433-90a2-8c2167cd45a5 · outbound

This paper cites Machine learning and the physical sciences,.

Machine learning the Ising transition: A comparison between discriminative and generative approaches Machine learning and the physical sciences,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:18:06.334522Z

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 f102fa35-96b2-4aa4-a6d1-fb4990ee91e2 · outbound

This paper cites How To Use Neural Networks To Investigate Quantum Many-Body Physics,.

Machine learning the Ising transition: A comparison between discriminative and generative approaches How To Use Neural Networks To Investigate Quantum Many-Body Physics,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:18:06.319003Z

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 3b1d94f5-b7d3-4534-ba67-c328b4c4a7ae · outbound

This paper cites Modern applications of machine learning in quantum sciences,.

Machine learning the Ising transition: A comparison between discriminative and generative approaches Modern applications of machine learning in quantum sciences,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T10:18:05.728758Z

Source-reported events for the cited work

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Observation 60bc89d1-84b3-435e-97df-2ab4102ce5d4 · outbound

This paper cites Replacing neural networks by optimal analytical predictors for the detection of phase transitions,.

Machine learning the Ising transition: A comparison between discriminative and generative approaches Replacing neural networks by optimal analytical predictors for the detection of phase transitions,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:18:06.303997Z

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 707fd0aa-0bf7-4469-b8ac-6638b763e9ba · outbound

This paper cites Mapping out phase diagrams with generative classifiers,.

Machine learning the Ising transition: A comparison between discriminative and generative approaches Mapping out phase diagrams with generative classifiers,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:18:06.287339Z

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 1720b9f2-3c0b-45e2-b799-c6e6869a0778 · outbound

This paper cites Machine learning phase transitions: Connections to the Fisher information.

Machine learning the Ising transition: A comparison between discriminative and generative approaches Machine learning phase transitions: Connections to the Fisher information

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-12T10:18:05.743533Z

Source-reported events for the cited work

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Observation 22e4b417-bc4e-4b35-8bed-209838a890d5 · outbound

This paper cites Machine learning phases of matter,.

Machine learning the Ising transition: A comparison between discriminative and generative approaches Machine learning phases of matter,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:18:06.272225Z

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 19a72da6-fc50-4016-ae79-99f49c4e6d4f · outbound

This paper cites Learning phase transitions by confusion,.

Machine learning the Ising transition: A comparison between discriminative and generative approaches Learning phase transitions by confusion,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:18:06.255623Z

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 9ac3dcf2-aeba-481f-b49f-96d43f409b09 · outbound

This paper cites Vector field divergence of predictive model output as indication of phase transitions,.

Machine learning the Ising transition: A comparison between discriminative and generative approaches Vector field divergence of predictive model output as indication of phase transitions,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:18:06.238004Z

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 67d05311-fe07-4cd2-bd38-d4c4961ffec0 · outbound

This paper cites Interpretable and unsupervised phase classification,.

Machine learning the Ising transition: A comparison between discriminative and generative approaches Interpretable and unsupervised phase classification,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:18:06.222610Z

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 0f09bade-0cf8-4fe7-b7b8-e8b3d2b2f53c · outbound

This paper cites Crystal Statistics. i. A Two-Dimensional Model with an Order-Disorder Transition,.

Machine learning the Ising transition: A comparison between discriminative and generative approaches Crystal Statistics. i. A Two-Dimensional Model with an Order-Disorder Transition,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:18:06.207272Z

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 d28829b8-6072-4ceb-b24a-cc65fd2bc8a2 · outbound

This paper cites The physics of energy-based models,.

Machine learning the Ising transition: A comparison between discriminative and generative approaches The physics of energy-based models,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:18:06.191536Z

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 0d768659-1295-42c1-904c-438e65da5c87 · outbound

This paper cites Introduction to latent variable energy-based models: a path toward autonomous machine intelligence,.

Machine learning the Ising transition: A comparison between discriminative and generative approaches Introduction to latent variable energy-based models: a path toward autonomous machine intelligence,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:18:06.175662Z

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 1cce9dbc-8d83-4d9a-a1d0-6cdb2dd64439 · outbound

This paper cites Pixel recurrent neural networks,.

Machine learning the Ising transition: A comparison between discriminative and generative approaches Pixel recurrent neural networks,

Reference 16

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Source-reported events for the cited work

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Observation 7834202e-7699-4532-b658-b330e7cdf342 · outbound

This paper cites Solving statistical mechanics using variational autoregressive networks,.

Machine learning the Ising transition: A comparison between discriminative and generative approaches Solving statistical mechanics using variational autoregressive networks,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:18:06.147799Z

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 e51bd8b1-fc63-46c7-916f-cd286a80d63c · outbound

This paper cites Asymptotically unbiased estimation of physical observables with neural samplers,.

Machine learning the Ising transition: A comparison between discriminative and generative approaches Asymptotically unbiased estimation of physical observables with neural samplers,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:18:06.131869Z

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 31bdcc3f-cdfe-4e85-91b5-febd9a786cae · outbound

This paper cites Unbiased Monte Carlo cluster updates with autoregressive neural networks,.

Machine learning the Ising transition: A comparison between discriminative and generative approaches Unbiased Monte Carlo cluster updates with autoregressive neural networks,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:18:06.115570Z

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 9d1749ca-0a19-4c4a-a9e2-07fb458feb53 · outbound

This paper cites Fast Detection of Phase Transitions with Multi-Task Learning-by-Confusion.

Machine learning the Ising transition: A comparison between discriminative and generative approaches Fast Detection of Phase Transitions with Multi-Task Learning-by-Confusion

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T10:18:05.801749Z

Source-reported events for the cited work

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Observation 3881421f-5fcf-4184-881c-f22e475634c3 · outbound

This paper cites Fine-tuning Neural Network Quantum States.

Machine learning the Ising transition: A comparison between discriminative and generative approaches Fine-tuning Neural Network Quantum States

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T10:18:05.807906Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e1cab6a-5ab0-469a-8ebd-472458a71a25 · outbound

This paper cites Enhancing variational Monte Carlo simulations using a programmable quantum simulator,.

Machine learning the Ising transition: A comparison between discriminative and generative approaches Enhancing variational Monte Carlo simulations using a programmable quantum simulator,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:18:06.100051Z

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 b5fef211-dcd7-4adb-adbf-1ae68c18e553 · outbound

This paper cites Github repository.

Machine learning the Ising transition: A comparison between discriminative and generative approaches Github repository

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:18:06.083372Z

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.

Pith citing papers

No inbound Pith citation observations are available.