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Paper Citation Record · LEDGER

Stochastic Gradient MCMC with Repulsive Forces

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:1812.00071.

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

pith.paper-citation-record.v1
1812.00071 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

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

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T11:08:47.084130Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T02:46:28.720088Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation a76ef65f-336b-4df0-81f3-cbe5f9479051 · inbound

Variationally Inferred Sampling Through a Refined Bound for Probabilistic Programs cites this paper.

Variationally Inferred Sampling Through a Refined Bound for Probabilistic Programs Stochastic Gradient MCMC with Repulsive Forces

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-14T11:08:47.084130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T11:08:47.084130Z digest=sha256:f388bd205da69c096b5e483bf89e328d19286b5d5a83c70f484dd92907a763fd

Observation eecf4bfc-bb03-4df9-976a-2d735f5edd0a · inbound

The Stein-log-Sobolev inequality and the exponential rate of convergence for the continuous Stein variational gradient descent method cites this paper.

The Stein-log-Sobolev inequality and the exponential rate of convergence for the continuous Stein variational gradient descent method Stochastic Gradient MCMC with Repulsive Forces

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-11T16:15:47.968691Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T16:15:47.968691Z digest=sha256:5ce3184cb9e76c7c7548e6cd4916aef7ad8f72346bfa89f4e6457dce2b1ab0ba

Observation 7afbbd08-57d0-44bf-9ef2-6ef1e52a797b · inbound

Functional-prior-based approaches to Bayesian PDE-constrained inversion using physics-informed neural networks cites this paper.

Functional-prior-based approaches to Bayesian PDE-constrained inversion using physics-informed neural networks Stochastic Gradient MCMC with Repulsive Forces

Reference 3

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T03:45:56.904658Z

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.

source=pdf_text observed=2026-05-11T02:19:16.737920Z digest=sha256:63eff45537ac98ea18b7cc17c40d763c3c23efd89e5d57b827743dd26a857c41

Observation 21616192-31e3-4614-948d-cc64127ac58e · inbound

Functional-prior-based approaches to Bayesian PDE-constrained inversion using physics-informed neural networks cites this paper.

Functional-prior-based approaches to Bayesian PDE-constrained inversion using physics-informed neural networks Stochastic Gradient MCMC with Repulsive Forces

Reference 3

Resolution
metadata mismatch
arxiv_id, observed 2026-05-15T06:29:49.753668Z

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.

source=pdf_text observed=2026-05-15T06:26:43.277631Z digest=sha256:11e60ef9375edfe6ea2f8c49c70572f9d764aaea74e105d9a729f663af36dfae

Observation 096eaf80-143c-4e9b-ba4c-15c7f57b25b5 · inbound

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials cites this paper.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Stochastic Gradient MCMC with Repulsive Forces

Reference 38

Resolution
verified exact
arxiv_id, observed 2026-07-02T02:46:28.722663Z

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.

source=pdf_text observed=2026-06-28T10:39:56.302357Z digest=sha256:f08413a07d9f3d7cad7fff594ae5a58078ae3163d9cca4c23b95914a4c337f16

Observation 6f650c72-0cc2-47f5-ba57-16c5178a80e4 · inbound

Mean-Field Stochastic PDEs: Well-posedness and Quantitative Dimension-Free Propagation of Chaos cites this paper.

Mean-Field Stochastic PDEs: Well-posedness and Quantitative Dimension-Free Propagation of Chaos Stochastic Gradient MCMC with Repulsive Forces

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-01T18:51:11.094831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T18:51:11.094831Z digest=sha256:559d2d1acd9c91aab9b828d83e24c5bea4e8bab9d8033c2a20fae56b11d35c4b