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Paper Citation Record · LEDGER

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

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 16 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 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 16 of 16 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T14:20:23.177818Z

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 4a79a638-af2f-43a4-8b98-05ea01fa4bde · inbound

Behaviour Suite for Reinforcement Learning cites this paper.

Behaviour Suite for Reinforcement Learning Reconciling modern machine learning practice and the bias-variance trade-off

Reference 2016

Resolution
unresolved
no resolver link, observed 2026-08-14T14:20:23.177818Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T14:20:23.177818Z digest=sha256:130e1a74908c70bb93cbc37535f670ccb3fd03fc5453b4987ffb9e49d903058b

Observation 24aa0934-0d5f-4ca7-8d18-9abb51508f49 · inbound

On the Multiple Descent of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels cites this paper.

On the Multiple Descent of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels Reconciling modern machine learning practice and the bias-variance trade-off

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-14T10:56:16.564669Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T10:56:16.564669Z digest=sha256:0298dca9bd62328e008016c3eedd3a3e1a1e78e4920db071d5355767873b7960

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-24T15:31:29.677449Z digest=sha256:e21eb0fe9a6bacce1487e9444350a324fa06684497c0fb048a78611c93eb3695

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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-05-18T00:58:13.116663Z digest=sha256:f2b85dce39d89ec3263da2c1cea0ee0d57ab28684323d873059f0c097d69321a

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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-05-11T14:22:57.925354Z digest=sha256:1a41c825b765c72c60a0da0ac253931dfc439defee16a3afe0ec7505e58d3fbf

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-11T19:28:53.301344Z digest=sha256:2cba3551418f7e4b4d2d568243ecd6f0532c5a489332014bf0fc7f85efce8ee2

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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-05-17T15:52:40.335080Z digest=sha256:c1ac39c723f797f9a1cdd32bd159067bd8cd7c20803877d1af086999b7b37e35

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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-05-10T15:42:47.274448Z digest=sha256:343cc269ede61666e84408798dede2e0060a40fc23dd085beaa63c1c7de1e324

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-24T10:22:00.419523Z digest=sha256:b05367a5725cf07964978d25e0d67d38690509e1cdc6c143048132f3952add85

Observation 96440fc7-b3d5-4e70-a75b-56942e9f659b · inbound

A Comprehensive Review of Adversarial Attacks on Machine Learning cites this paper.

A Comprehensive Review of Adversarial Attacks on Machine Learning Reconciling modern machine learning practice and the bias-variance trade-off

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-11T15:01:54.393563Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T15:01:54.393563Z digest=sha256:93c86c1b60589bd145257bf84c376165e93daa6e0bfc8a227f07252bc7f83f39

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.

source=pdf_text observed=2026-08-06T23:11:14.417501Z digest=sha256:19e415c86fb8034bacb6c5b48a2f30685a14aca857196d89756e5e710d80327a

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.

source=pdf_text observed=2026-07-15T14:50:37.075540Z digest=sha256:efecc3f7d23a610a334bc8ba4d19784efdcefebfeb7ab694ff4c5b26a4e1150c

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-08T03:24:06.282053Z digest=sha256:0ef73e42586e10bf370ec28018b36201e5366395c1bd5f0f30d02e0b0077ea6f

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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-05-25T03:16:34.488732Z digest=sha256:884340ca1e511c5e9011380634ff88b41b76e982fa84562c432ef9071cb4186b

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.

source=arxiv_source observed=2026-07-12T07:49:39.894643Z digest=sha256:21f78d8b43c05f799b3ba7a421714dd111610cdfc4a28501e6460301a604ab3f

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.

source=pdf_text observed=2026-07-31T00:44:59.555731Z digest=sha256:857ce7570c3b34c433a33ab37369045e71e2ecba539a11e75626cff5aed41496