Paper Citation Record · LEDGER

Asymptotically unbiased estimation of physical observables with neural samplers

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:1910.13496.

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pith.paper-citation-record.v1
1910.13496 v2

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measured 0 of 0 reference resolution

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measured 8 of 8 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T12:56:00.639379Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T03:22:21.033656Z

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Outbound references

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Pith citing papers

Observation e352c404-1891-4045-83b3-1cd2b973e88f · inbound

Diffusion models and stochastic quantisation in lattice field theory cites this paper.

Diffusion models and stochastic quantisation in lattice field theory Asymptotically unbiased estimation of physical observables with neural samplers

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T12:56:00.639379Z

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Unavailable: canonical work link unavailable.

Observation ded9d538-946f-4df0-bd3e-5205718c55d5 · inbound

Simulating the Hubbard Model with Equivariant Normalizing Flows cites this paper.

Simulating the Hubbard Model with Equivariant Normalizing Flows Asymptotically unbiased estimation of physical observables with neural samplers

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T20:48:33.065066Z

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Observation c4103eac-99bc-4c93-b0af-da9ec7ab07c0 · inbound

Exploring Generative Networks for Manifolds with Non-Trivial Topology cites this paper.

Exploring Generative Networks for Manifolds with Non-Trivial Topology Asymptotically unbiased estimation of physical observables with neural samplers

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-09T13:19:35.152979Z

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Unavailable: canonical work link unavailable.

Observation 52b565aa-d26c-42e5-b920-2789ce14232e · inbound

Machine-learning approaches to accelerating lattice simulations cites this paper.

Machine-learning approaches to accelerating lattice simulations Asymptotically unbiased estimation of physical observables with neural samplers

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:41.588111Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7618073-e8e5-4cdd-8d51-5ba9c380ea20 · inbound

Studying Effective String Theory using deep generative models cites this paper.

Studying Effective String Theory using deep generative models Asymptotically unbiased estimation of physical observables with neural samplers

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-05T15:02:49.484812Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f78dd0e0-27c7-4f6b-8fa5-b56888cb95c4 · inbound

Scaling flow-based approaches for topology sampling in $\mathrm{SU}(3)$ gauge theory cites this paper.

Scaling flow-based approaches for topology sampling in $\mathrm{SU}(3)$ gauge theory Asymptotically unbiased estimation of physical observables with neural samplers

Reference 62

Resolution: verified exact; arxiv_id, observed 2026-05-18T03:22:21.036704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f3addb20-98e4-46d7-a76a-ed81b02349ad · inbound

Sampling two-dimensional spin systems with transformers cites this paper.

Sampling two-dimensional spin systems with transformers Asymptotically unbiased estimation of physical observables with neural samplers

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-12T10:31:28.448044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d0393ff3-8d1e-4e71-9eab-b904d21b4539 · inbound

Leveraging generative models to assist Monte Carlo sampling cites this paper.

Leveraging generative models to assist Monte Carlo sampling Asymptotically unbiased estimation of physical observables with neural samplers

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T00:34:15.952466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.