Paper Citation Record · LEDGER

Variational Learning is Effective for Large Deep Networks

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2402.17641.

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

pith.paper-citation-record.v1
2402.17641 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T16:15:07.332674Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T20:40:08.506330Z

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 94265aaf-654f-43e5-9518-debed1819096 · inbound

Spectral-factorized Positive-definite Curvature Learning for NN Training cites this paper.

Spectral-factorized Positive-definite Curvature Learning for NN Training Variational Learning is Effective for Large Deep Networks

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-08T16:15:07.332674Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d189caa-36d8-40a1-b892-3bf352f1fcca · inbound

Optimization Guarantees for Square-Root Natural-Gradient Variational Inference cites this paper.

Optimization Guarantees for Square-Root Natural-Gradient Variational Inference Variational Learning is Effective for Large Deep Networks

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-06T18:39:18.391365Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e298c473-0caa-4aa3-9d62-4bc69c10f1c1 · inbound

Bayesian E(3)-Equivariant Interatomic Potential with Iterative Restratification of Many-body Message Passing cites this paper.

Bayesian E(3)-Equivariant Interatomic Potential with Iterative Restratification of Many-body Message Passing Variational Learning is Effective for Large Deep Networks

Reference 27

Resolution: malformed identifier; arxiv_id, observed 2026-05-18T10:02:31.767495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation de62a576-759e-4e53-928a-17820d8b22d2 · inbound

Gaussian Mean Field Variational Inference can Overestimate Predictive Variance cites this paper.

Gaussian Mean Field Variational Inference can Overestimate Predictive Variance Variational Learning is Effective for Large Deep Networks

Reference 30

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T20:40:08.507744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.