Paper Citation Record · LEDGER

Reconciling modern machine learning practice and the bias-variance trade-off

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:1812.11118.

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

pith.paper-citation-record.v1
1812.11118 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:11:14.417501Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

83
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 17184399-20c3-4f07-8c96-57a5ee8d615a · inbound

Scaling Laws for Neural Language Models cites this paper.

Scaling Laws for Neural Language Models Reconciling modern machine learning practice and the bias-variance trade-off

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-05-09T04:51:47.663465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 88a457bf-6530-4488-bd3d-5d2c8613f392 · inbound

Scaling Laws for Transfer cites this paper.

Scaling Laws for Transfer Reconciling modern machine learning practice and the bias-variance trade-off

Reference 73

Resolution: verified exact; arxiv_id, observed 2026-05-18T00:58:13.703855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d0f25eac-2443-4e3a-9d6b-bfcc1eb05d8d · inbound

A General Language Assistant as a Laboratory for Alignment cites this paper.

A General Language Assistant as a Laboratory for Alignment Reconciling modern machine learning practice and the bias-variance trade-off

Reference 103

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:22:59.358443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4801269a-ba1d-4b8f-ae9d-47d82abb0c53 · inbound

Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets cites this paper.

Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets Reconciling modern machine learning practice and the bias-variance trade-off

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T19:28:53.376725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f0090cdd-8f71-48f5-a7af-96f50244e778 · inbound

Scaling Laws and Interpretability of Learning from Repeated Data cites this paper.

Scaling Laws and Interpretability of Learning from Repeated Data Reconciling modern machine learning practice and the bias-variance trade-off

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-17T15:52:40.485189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 12d3bb43-752c-4337-b347-6aa78aa33c85 · inbound

Language Models (Mostly) Know What They Know cites this paper.

Language Models (Mostly) Know What They Know Reconciling modern machine learning practice and the bias-variance trade-off

Reference 161

Resolution: verified exact; arxiv_id, observed 2026-05-10T15:42:47.818474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 56a14a50-a29e-4cd4-b46a-e78039d12e58 · inbound

Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics cites this paper.

Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics Reconciling modern machine learning practice and the bias-variance trade-off

Reference 138

Resolution: verified exact; arxiv_id, observed 2026-05-24T10:24:20.408040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2707b8b1-d55b-4502-af26-264980cb1888 · inbound

PhishingHook: Catching Phishing Ethereum Smart Contracts leveraging EVM Opcodes cites this paper.

PhishingHook: Catching Phishing Ethereum Smart Contracts leveraging EVM Opcodes Reconciling modern machine learning practice and the bias-variance trade-off

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T23:11:14.417501Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68224ca9-cb79-4d0e-b541-51fdd99974f1 · inbound

Asymptotic Behavior of Multi--Task Learning: Implicit Regularization and Double Descent Effects cites this paper.

Asymptotic Behavior of Multi--Task Learning: Implicit Regularization and Double Descent Effects Reconciling modern machine learning practice and the bias-variance trade-off

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-15T14:50:37.075540Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2c2e499-d462-4573-bbeb-18027f0e3b7d · inbound

Lecture Notes on Statistical Physics and Neural Networks cites this paper.

Lecture Notes on Statistical Physics and Neural Networks Reconciling modern machine learning practice and the bias-variance trade-off

Reference 48

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T22:06:24.967085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fb6177ef-6b51-4491-b8a5-bd96a76069e0 · inbound

Asymmetric Scaling Laws from Sparse Features cites this paper.

Asymmetric Scaling Laws from Sparse Features Reconciling modern machine learning practice and the bias-variance trade-off

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-25T03:20:16.920911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8af65c36-940b-467d-a613-6d2c251d3e0c · inbound

Benign Overfitting Does Not Occur in Diffusion Models cites this paper.

Benign Overfitting Does Not Occur in Diffusion Models Reconciling modern machine learning practice and the bias-variance trade-off

Reference 88

Resolution: unresolved; no resolver link, observed 2026-07-12T07:49:39.894643Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c3585c4-f9df-4a6e-bc6f-8017158e6006 · inbound

Semantic Space Search Trajectory Networks cites this paper.

Semantic Space Search Trajectory Networks Reconciling modern machine learning practice and the bias-variance trade-off

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-31T00:44:59.555731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.