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Paper Citation Record · LEDGER

SETOL: A Semi-Empirical Theory of (Deep) Learning

As of 20 August 2026, this Paper Citation Record lists 100 of 148 outbound references and 6 inbound Pith citation observations for arXiv:2507.17912.

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

pith.paper-citation-record.v1
2507.17912 v2

Coverage vector

measured 100 of 148 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:45:20.169177Z

measured 106 of 106 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-03T23:41:26.610335Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T02:59:25.684114Z

Reference resolution

100 of 148 outbound references displayed

  • verified exact4
  • verified fuzzy13
  • unresolved83
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bfc5347a-f962-486b-9929-d55c13e25846 · outbound

This paper cites Highly accurate protein structure prediction with alphafold.Nature, 596(7873):583–589, 2021.

SETOL: A Semi-Empirical Theory of (Deep) Learning Highly accurate protein structure prediction with alphafold.Nature, 596(7873):583–589, 2021

Reference 1

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source=pdf_text observed=2026-08-06T14:45:19.907324Z digest=sha256:cda7007b4ff68355123d7b01c06dec8094649b4f01817749c9ef5b2e89773430

Observation e5ebd3ff-4d54-4f83-a452-db83718f8024 · outbound

This paper cites The nobel prize in physics 2024, 2024.

SETOL: A Semi-Empirical Theory of (Deep) Learning The nobel prize in physics 2024, 2024

Reference 2

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source=pdf_text observed=2026-08-06T14:45:19.911197Z digest=sha256:790b4e556eb8313be0725093d3b90f14920947395700696ce7e7c0aa5b103645

Observation f44f0e32-7e10-4586-a091-eef3ac6bf203 · outbound

This paper cites The nobel prize in chemistry 2024, 2024.

SETOL: A Semi-Empirical Theory of (Deep) Learning The nobel prize in chemistry 2024, 2024

Reference 3

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source=pdf_text observed=2026-08-06T14:45:19.914058Z digest=sha256:96af9ccbfeffcd58ca4ef67639e8f9666bf7b51e29238457a37aa8f9e9668d52

Observation b0385796-8160-412b-9f41-650a8c2ba462 · outbound

This paper cites an unresolved cited work.

SETOL: A Semi-Empirical Theory of (Deep) Learning Unresolved cited work

Reference 4

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source=pdf_text observed=2026-08-06T14:45:19.916772Z digest=sha256:69f4d7a89e9f4d9e9c3af06c02416d2c02ede8484ae81af929754ab436a1e6d5

Observation 37dfd05a-3116-462d-b517-fbe62d179bdf · outbound

This paper cites Engel and C.

SETOL: A Semi-Empirical Theory of (Deep) Learning Engel and C

Reference 5

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source=pdf_text observed=2026-08-06T14:45:19.919382Z digest=sha256:988ed23abfc49b7d1f3180398c544a9779d3c91676b3cf887ab3af7b27bfad11

Observation 8b8cd7c5-dc2a-45b9-a43c-1ee168c2d0df · outbound

This paper cites The space of interactions in neural network models.Journal of Physics A: Mathematical and General, 21(1):257, jan 1988.

SETOL: A Semi-Empirical Theory of (Deep) Learning The space of interactions in neural network models.Journal of Physics A: Mathematical and General, 21(1):257, jan 1988

Reference 6

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source=pdf_text observed=2026-08-06T14:45:19.921813Z digest=sha256:49ef3ab6f9daf0ec6d276ab8e1e5951befcde659c39b057e42ec0a4c63aad67e

Observation 2b1e4701-bcbe-494a-9299-4f165017c1b6 · outbound

This paper cites Sompolinsky, N.

SETOL: A Semi-Empirical Theory of (Deep) Learning Sompolinsky, N

Reference 7

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source=pdf_text observed=2026-08-06T14:45:19.924516Z digest=sha256:d1076d6302b0646ee4606e4c53d7aa52bbd0863bb20331423d47200cf209abb1

Observation a8c0c886-5670-454a-9ef8-61b9b736c54b · outbound

This paper cites an unresolved cited work.

SETOL: A Semi-Empirical Theory of (Deep) Learning Unresolved cited work

Reference 8

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source=pdf_text observed=2026-08-06T14:45:19.926959Z digest=sha256:6d6d9c17bec12b8045c3e7ba99de0e51441b34a99ca9d2fcbd967dd4422af958

Observation a0279efb-2827-4499-9abe-e36241e25194 · outbound

This paper cites Levin, N.

SETOL: A Semi-Empirical Theory of (Deep) Learning Levin, N

Reference 9

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source=pdf_text observed=2026-08-06T14:45:19.929330Z digest=sha256:9bc6131755c3a928a5afd7cc0f701196d8667c5c0c1ec51b64a401bb56521905

Observation b27c188e-4b0e-4923-8982-3a1cd25e8e19 · outbound

This paper cites Statistical physics, Bayesian inference and neural information processing.

SETOL: A Semi-Empirical Theory of (Deep) Learning Statistical physics, Bayesian inference and neural information processing

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T14:45:19.931864Z digest=sha256:d1a7959fd5a0486ca47b59b717438c16f07dcf64629b414592c7751e70b0fcb8

Observation 5ad0389b-dd9e-4a88-8fb1-bf9954d1ab6b · outbound

This paper cites Vapnik.Statistical Learning Theory.

SETOL: A Semi-Empirical Theory of (Deep) Learning Vapnik.Statistical Learning Theory

Reference 11

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source=pdf_text observed=2026-08-06T14:45:19.934543Z digest=sha256:bb8a918a9c69b5e51003b62247124fab90be59c4b4617189d5651dff1678636f

Observation 47f21556-749d-48a6-bfc7-7a7a6296aec7 · outbound

This paper cites Neuralnetworksandphysicalsystemswithemergentcollectivecomputationalabilities.

SETOL: A Semi-Empirical Theory of (Deep) Learning Neuralnetworksandphysicalsystemswithemergentcollectivecomputationalabilities

Reference 12

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Observation 44c28e7f-d0b5-4fd1-b1b9-fe9752f7dc40 · outbound

This paper cites an unresolved cited work.

SETOL: A Semi-Empirical Theory of (Deep) Learning Unresolved cited work

Reference 13

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Observation c4076c62-7239-461c-bf4b-58a760a485c5 · outbound

This paper cites an unresolved cited work.

SETOL: A Semi-Empirical Theory of (Deep) Learning Unresolved cited work

Reference 14

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source=pdf_text observed=2026-08-06T14:45:19.941613Z digest=sha256:36a8558d88ac0d5e67c87aab64bb699e9e265df4eacaa3aca065e290bc513c5c

Observation dea89ee6-095d-4bf7-8317-79aec33557e3 · outbound

This paper cites an unresolved cited work.

SETOL: A Semi-Empirical Theory of (Deep) Learning Unresolved cited work

Reference 15

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source=pdf_text observed=2026-08-06T14:45:19.944130Z digest=sha256:1820347d36d40552a63374fd1d6327bd2a2fe4d33b313d5e623052c1971243a6

Observation 4e3f09fb-61d6-49fa-9db8-12446db06ed3 · outbound

This paper cites Belkin, D.

SETOL: A Semi-Empirical Theory of (Deep) Learning Belkin, D

Reference 16

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source=pdf_text observed=2026-08-06T14:45:19.946456Z digest=sha256:a1603100cbfef770580f04dbec0f69b2bf48e5777ebf85aa069ab1ba80bc8ca1

Observation e7775a9b-f454-479f-b689-5d2e4e396b27 · outbound

This paper cites A brief prehistory of double descent.

SETOL: A Semi-Empirical Theory of (Deep) Learning A brief prehistory of double descent

Reference 17

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source=pdf_text observed=2026-08-06T14:45:19.948594Z digest=sha256:83e084cde69db11028a86b6118510b16b01e24971aea97aee16356f195b462eb

Observation d21c80d5-8a53-4ca8-b485-3141223593eb · outbound

This paper cites an unresolved cited work.

SETOL: A Semi-Empirical Theory of (Deep) Learning Unresolved cited work

Reference 18

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Observation 36f74a5d-01ce-4e36-bc71-53861855569b · outbound

This paper cites Roberts, Sho Yaida, and Boris Hanin.

SETOL: A Semi-Empirical Theory of (Deep) Learning Roberts, Sho Yaida, and Boris Hanin

Reference 19

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source=pdf_text observed=2026-08-06T14:45:19.953138Z digest=sha256:1507dc86bdb1ae5589b35014f46ae9a54e8375cecbaff51e21e407cf83471963

Observation 36e99f64-a6f8-4a78-b9af-ed96f5c1dc2c · outbound

This paper cites Vapnik, E.

SETOL: A Semi-Empirical Theory of (Deep) Learning Vapnik, E

Reference 20

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Observation 8712ea51-31b1-436f-a890-edb0efc380fd · outbound

This paper cites an unresolved cited work.

SETOL: A Semi-Empirical Theory of (Deep) Learning Unresolved cited work

Reference 21

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source=pdf_text observed=2026-08-06T14:45:19.957725Z digest=sha256:f8eb2e2288846f631d911f70ca0878597972b8dd63941940d4a212a119db3a9a

Observation ed7d2c52-8bfe-47fc-bc6b-b75babb86018 · outbound

This paper cites Haussler, M.

SETOL: A Semi-Empirical Theory of (Deep) Learning Haussler, M

Reference 22

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Observation 95958104-a5f0-429c-b505-2eff77d4eb27 · outbound

This paper cites Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data.

SETOL: A Semi-Empirical Theory of (Deep) Learning Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data

Reference 23

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Observation ca45a118-8f9f-4294-82eb-3949c7ddac00 · outbound

This paper cites Post-mortem on a deep learning contest: a Simpson's paradox and the complementary roles of scale metrics versus shape metrics.

SETOL: A Semi-Empirical Theory of (Deep) Learning Post-mortem on a deep learning contest: a Simpson's paradox and the complementary roles of scale metrics versus shape metrics

Reference 24

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Observation d5c3de3c-740e-494d-a605-30e6beb52e5c · outbound

This paper cites an unresolved cited work.

SETOL: A Semi-Empirical Theory of (Deep) Learning Unresolved cited work

Reference 25

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Observation c229e3a8-85a3-45f0-a014-0adbce16a01b · outbound

This paper cites an unresolved cited work.

SETOL: A Semi-Empirical Theory of (Deep) Learning Unresolved cited work

Reference 26

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Observation c61174b1-5816-4df9-8c82-524a213a17d7 · outbound

This paper cites Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data.

SETOL: A Semi-Empirical Theory of (Deep) Learning Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data

Reference 27

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Observation 4627ab2a-0e2a-4ac7-bf26-2205255a84c3 · outbound

This paper cites an unresolved cited work.

SETOL: A Semi-Empirical Theory of (Deep) Learning Unresolved cited work

Reference 28

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Observation 5fd388a6-e602-4f00-9767-0b2ea0ffec28 · outbound

This paper cites Temperature balancing, layer-wise weight analysis, and neural network training.

SETOL: A Semi-Empirical Theory of (Deep) Learning Temperature balancing, layer-wise weight analysis, and neural network training

Reference 29

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Observation fd9c7752-120c-4974-b668-b8969041dfd3 · outbound

This paper cites Mahoney, and Yaoqing Yang.

SETOL: A Semi-Empirical Theory of (Deep) Learning Mahoney, and Yaoqing Yang

Reference 30

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Observation ec9c16df-5a33-43c1-8fe2-2926fb765c77 · outbound

This paper cites Hans Bethe and the theory of nuclear matter.Physics Today, 58(10):58, 2005.

SETOL: A Semi-Empirical Theory of (Deep) Learning Hans Bethe and the theory of nuclear matter.Physics Today, 58(10):58, 2005

Reference 31

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Observation a9fe06ec-5587-4827-85a4-c601a8c3fe8a · outbound

This paper cites Ivanenko.

SETOL: A Semi-Empirical Theory of (Deep) Learning Ivanenko

Reference 32

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Observation b6a7a512-6341-4419-90d5-af187c1d48c6 · outbound

This paper cites On closed shells in nuclei.

SETOL: A Semi-Empirical Theory of (Deep) Learning On closed shells in nuclei

Reference 33

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Observation a16a2550-cf99-4b37-9379-22ed315d2ae3 · outbound

This paper cites magic numbers.

SETOL: A Semi-Empirical Theory of (Deep) Learning magic numbers

Reference 34

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Observation de1304b9-c5d0-49d6-a753-91c7537379b3 · outbound

This paper cites Characteristic vectors of bordered matrices with infinite dimensions.

SETOL: A Semi-Empirical Theory of (Deep) Learning Characteristic vectors of bordered matrices with infinite dimensions

Reference 35

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Observation b7738a13-2d6c-4966-a970-2db8ec58c248 · outbound

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SETOL: A Semi-Empirical Theory of (Deep) Learning Unresolved cited work

Reference 36

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Observation e3e17c80-a51b-4b52-8e3b-f9398c6c2d2e · outbound

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SETOL: A Semi-Empirical Theory of (Deep) Learning Unresolved cited work

Reference 37

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Observation 0d80048a-5567-4a09-be4c-29d8d326647b · outbound

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SETOL: A Semi-Empirical Theory of (Deep) Learning Unresolved cited work

Reference 38

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Observation 89b74c2b-93ea-4bc7-ad82-3b8534bcb04f · outbound

This paper cites Sener and Klaus Schulten.

SETOL: A Semi-Empirical Theory of (Deep) Learning Sener and Klaus Schulten

Reference 39

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Observation 89c34f06-b706-49ce-bad7-5a744857a363 · outbound

This paper cites Gallucio, J.-P.

SETOL: A Semi-Empirical Theory of (Deep) Learning Gallucio, J.-P

Reference 40

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Observation 23e4435f-17fd-443d-aa23-07a7751a25b1 · outbound

This paper cites Cherrier, D.

SETOL: A Semi-Empirical Theory of (Deep) Learning Cherrier, D

Reference 41

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SETOL: A Semi-Empirical Theory of (Deep) Learning Unresolved cited work

Reference 42

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Observation 229dd47e-01c5-4382-80d4-1845b0a1384b · outbound

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SETOL: A Semi-Empirical Theory of (Deep) Learning Unresolved cited work

Reference 43

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SETOL: A Semi-Empirical Theory of (Deep) Learning Unresolved cited work

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Observation 06889ff2-0a74-4a4c-bce5-a38abde1dd87 · outbound

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SETOL: A Semi-Empirical Theory of (Deep) Learning Unresolved cited work

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Observation 2601cb03-41d0-4f11-b61f-2fe7807ceb6d · outbound

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SETOL: A Semi-Empirical Theory of (Deep) Learning Unresolved cited work

Reference 46

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Observation 6417f48b-995d-4cc9-a146-c09abf0bf65f · outbound

This paper cites Theoretical studies of enzymic reactions: dielectric, electrostatic and steric stabilization of the carbonium ion in the reaction of lysozyme.

SETOL: A Semi-Empirical Theory of (Deep) Learning Theoretical studies of enzymic reactions: dielectric, electrostatic and steric stabilization of the carbonium ion in the reaction of lysozyme

Reference 47

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Observation a30f1f46-90da-4dd4-926a-439b2e73ebeb · outbound

This paper cites Chapter 21 - semiempirical quantum-chemical methods in computational chemistry.

SETOL: A Semi-Empirical Theory of (Deep) Learning Chapter 21 - semiempirical quantum-chemical methods in computational chemistry

Reference 48

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Observation 2d3d2abc-1f59-4fe6-af8f-610fd9c562d5 · outbound

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SETOL: A Semi-Empirical Theory of (Deep) Learning Unresolved cited work

Reference 49

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Observation ee02a106-e8a3-483d-b488-a02ca20e3956 · outbound

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SETOL: A Semi-Empirical Theory of (Deep) Learning Unresolved cited work

Reference 50

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Observation c9ce7375-1a21-4643-ace7-20932d14beb2 · outbound

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SETOL: A Semi-Empirical Theory of (Deep) Learning Unresolved cited work

Reference 51

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Observation ed6588f3-5490-43eb-a7ec-0580b4c11df3 · outbound

This paper cites Martin and Karl F.

SETOL: A Semi-Empirical Theory of (Deep) Learning Martin and Karl F

Reference 52

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Observation 7fa56818-3d9d-456a-88dc-aa969d640583 · outbound

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SETOL: A Semi-Empirical Theory of (Deep) Learning Unresolved cited work

Reference 53

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Observation 4589489e-e4e6-4cb1-b06d-3a0a467dd5fe · outbound

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SETOL: A Semi-Empirical Theory of (Deep) Learning Unresolved cited work

Reference 54

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Observation 26cfb57d-2334-47a4-9b20-5583d218760f · outbound

This paper cites Martin and Robert R.

SETOL: A Semi-Empirical Theory of (Deep) Learning Martin and Robert R

Reference 55

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Observation 9ca6f8f8-5e37-4da4-ad27-199eaf925472 · outbound

This paper cites The nobel prize in physics 1982: Kenneth g.

SETOL: A Semi-Empirical Theory of (Deep) Learning The nobel prize in physics 1982: Kenneth g

Reference 56

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Observation 876312fb-a238-4389-bf7a-f7578897a5a9 · outbound

This paper cites an unresolved cited work.

SETOL: A Semi-Empirical Theory of (Deep) Learning Unresolved cited work

Reference 57

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Observation 196168bf-af09-41ae-84f2-3d8d460490e1 · outbound

This paper cites Improving language under- standing by generative pre-training.OpenAI, 2018.

SETOL: A Semi-Empirical Theory of (Deep) Learning Improving language under- standing by generative pre-training.OpenAI, 2018

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Observation 15ec5638-2e4b-4a9e-b40d-96256617b0c4 · outbound

This paper cites Rapid calculation of side chain packing and free energy with applications to protein molecular dynamics.

SETOL: A Semi-Empirical Theory of (Deep) Learning Rapid calculation of side chain packing and free energy with applications to protein molecular dynamics

Reference 59

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Observation 3168bf59-0eb1-4759-8608-17248ceb0d2e · outbound

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SETOL: A Semi-Empirical Theory of (Deep) Learning Unresolved cited work

Reference 60

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Observation a708339b-f46f-43a9-97bf-8383936eeb20 · outbound

This paper cites Deep Neural Networks as Gaussian Processes.

SETOL: A Semi-Empirical Theory of (Deep) Learning Deep Neural Networks as Gaussian Processes

Reference 61

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Observation 0ea6c490-5048-4e48-a373-6b4614d973cd · outbound

This paper cites Tensor Programs III: Neural Matrix Laws.

SETOL: A Semi-Empirical Theory of (Deep) Learning Tensor Programs III: Neural Matrix Laws

Reference 62

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Observation 9bcf9103-f069-47e1-8b17-6cccb97183d1 · outbound

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SETOL: A Semi-Empirical Theory of (Deep) Learning Unresolved cited work

Reference 63

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Observation 5b95d904-5c6f-4250-8687-7220b944b8fd · outbound

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SETOL: A Semi-Empirical Theory of (Deep) Learning Unresolved cited work

Reference 64

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Observation 2a4f4524-7fbd-4d7f-9050-50eec9cafffc · outbound

This paper cites an unresolved cited work.

SETOL: A Semi-Empirical Theory of (Deep) Learning Unresolved cited work

Reference 65

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Observation 2f4454a4-00b4-41b0-b764-f114a566aad8 · outbound

This paper cites Bryngelson and Peter G.

SETOL: A Semi-Empirical Theory of (Deep) Learning Bryngelson and Peter G

Reference 66

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Observation d13b6ae5-7ef3-4b75-bd11-fdb53f33220e · outbound

This paper cites an unresolved cited work.

SETOL: A Semi-Empirical Theory of (Deep) Learning Unresolved cited work

Reference 67

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Observation 3b27a2ab-75d8-4c03-a551-1f93094b3622 · outbound

This paper cites Clauset, C.

SETOL: A Semi-Empirical Theory of (Deep) Learning Clauset, C

Reference 68

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Observation 24edc05c-e1e2-46ab-9968-757a84471616 · outbound

This paper cites Alstott, E.

SETOL: A Semi-Empirical Theory of (Deep) Learning Alstott, E

Reference 69

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Observation 1f9d3aaf-3bf9-4c6d-ae0e-8a0a26e498d3 · outbound

This paper cites Random matrix analysis of deep neural network weight matrices.Physical Review E, 106(5):054124, 2022.

SETOL: A Semi-Empirical Theory of (Deep) Learning Random matrix analysis of deep neural network weight matrices.Physical Review E, 106(5):054124, 2022

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Observation 4e25506a-d8f9-4500-b71a-12a91cb0db49 · outbound

This paper cites Bouchaud and M.

SETOL: A Semi-Empirical Theory of (Deep) Learning Bouchaud and M

Reference 71

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source=pdf_text observed=2026-08-06T14:45:20.089313Z digest=sha256:1e2ea6b6d15667344cb2c8be843a170379f9211e456969438ddb06553fd0eaaf

Observation a613e847-7fcc-41b1-8388-d3b2239292d2 · outbound

This paper cites Edelman and Y.

SETOL: A Semi-Empirical Theory of (Deep) Learning Edelman and Y

Reference 72

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source=pdf_text observed=2026-08-06T14:45:20.091867Z digest=sha256:f136c35f64ac93226268cc256d4916d7d2dc734aeacab4d7ef48b51d84a8054a

Observation eb31549e-fc50-410a-8aa8-5811f0205533 · outbound

This paper cites Cambridge University Press, 2020.

SETOL: A Semi-Empirical Theory of (Deep) Learning Cambridge University Press, 2020

Reference 73

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Observation 50e7bfe7-9e37-431f-b619-7e3d59123937 · outbound

This paper cites Learning spectral clustering, with application to speech separation.

SETOL: A Semi-Empirical Theory of (Deep) Learning Learning spectral clustering, with application to speech separation

Reference 74

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Observation 74f4d436-48a7-4467-8cfb-722e32c6771d · outbound

This paper cites The eigenvalues and eigenvectors of finite, low rank perturbations of large random matrices.Advances in Mathematics, 227(1):494–521, 2011.

SETOL: A Semi-Empirical Theory of (Deep) Learning The eigenvalues and eigenvectors of finite, low rank perturbations of large random matrices.Advances in Mathematics, 227(1):494–521, 2011

Reference 75

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source=pdf_text observed=2026-08-06T14:45:20.099308Z digest=sha256:551fec0e0a65a612c915e971cef80026ca62edf05be64fcdeb3c3519a5dcced0

Observation 586bdfb9-08a5-4e38-a7fc-07cd226a6aab · outbound

This paper cites Oxford University Press, Oxford, UK, 1997.

SETOL: A Semi-Empirical Theory of (Deep) Learning Oxford University Press, Oxford, UK, 1997

Reference 76

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Observation 8a8a5497-e4f1-4fc9-b5bf-fc606d4a44db · outbound

This paper cites Sornette.

SETOL: A Semi-Empirical Theory of (Deep) Learning Sornette

Reference 77

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation a39515f4-fe53-4467-b3d1-988461db4ee7 · outbound

This paper cites Noisedressingoffinancialcorrelationmatrices.

SETOL: A Semi-Empirical Theory of (Deep) Learning Noisedressingoffinancialcorrelationmatrices

Reference 78

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Observation 264fa764-4a56-4bfd-b3fe-d41b3fc11959 · outbound

This paper cites Laloux, P.

SETOL: A Semi-Empirical Theory of (Deep) Learning Laloux, P

Reference 79

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Observation 11e345e0-1ca1-497c-828d-24c5a568a635 · outbound

This paper cites Taxonomizing local versus global structure in neural network loss landscapes.

SETOL: A Semi-Empirical Theory of (Deep) Learning Taxonomizing local versus global structure in neural network loss landscapes

Reference 80

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Observation 73cdb024-2bcd-4ca9-a7c3-34f95977c62d · outbound

This paper cites Heavy-Tailed Universality Predicts Trends in Test Accuracies for Very Large Pre-Trained Deep Neural Networks.

SETOL: A Semi-Empirical Theory of (Deep) Learning Heavy-Tailed Universality Predicts Trends in Test Accuracies for Very Large Pre-Trained Deep Neural Networks

Reference 81

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:45:20.114139Z digest=sha256:bdaf70d94725bd712fe1fe5502d65d50e02c604c121a276bab617928a38ae683

Observation fbda80ad-d532-45e3-ba3e-4766d5ed78c2 · outbound

This paper cites Sompolinsky, N.

SETOL: A Semi-Empirical Theory of (Deep) Learning Sompolinsky, N

Reference 82

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raw_fallback, observed 2026-08-06T14:45:20.998431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T14:45:20.116968Z digest=sha256:aee2576e23678fcbb351ac97fec613625ac70577d4707d75f36e6f577da167d2

Observation 412a5e0d-2a70-4910-aaec-f3ed904dcaef · outbound

This paper cites an unresolved cited work.

SETOL: A Semi-Empirical Theory of (Deep) Learning Unresolved cited work

Reference 83

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raw_fallback, observed 2026-08-06T14:45:20.990741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T14:45:20.119344Z digest=sha256:5fcb468de32dffdcb62eb04405ee51b9ebaee9661b3154d29398d6ee1ef0959d

Observation 68a868e0-a2e8-4af6-9ef0-0b2dc6c4cdbe · outbound

This paper cites an unresolved cited work.

SETOL: A Semi-Empirical Theory of (Deep) Learning Unresolved cited work

Reference 84

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raw_fallback, observed 2026-08-06T14:45:20.982928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T14:45:20.122033Z digest=sha256:f3296920fd78d8c8c917835850c50c879325fc316d22418d6e77967afec4e017

Observation acfc2067-bdbd-456e-a484-71a3499fa42e · outbound

This paper cites an unresolved cited work.

SETOL: A Semi-Empirical Theory of (Deep) Learning Unresolved cited work

Reference 85

Resolution
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raw_fallback, observed 2026-08-06T14:45:20.975046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T14:45:20.125315Z digest=sha256:29e4e0de5d101df5f7d7ec0131c6d5ca08b56a7272bbbd7dd6620aa5c522ba4c

Observation 0c4eb063-e22e-4667-856f-078d7b000ad0 · outbound

This paper cites Dragon-kings, black swans and the prediction of crises, 2009.

SETOL: A Semi-Empirical Theory of (Deep) Learning Dragon-kings, black swans and the prediction of crises, 2009

Reference 86

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:45:20.967259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T14:45:20.128170Z digest=sha256:0d4030b10e301e5cc24b183587e3fce413ebef20661488085c464f1201e34d26

Observation 732a57b7-ec68-467a-93f7-8c9348347a2c · outbound

This paper cites The Heavy-Tail Phenomenon in SGD.

SETOL: A Semi-Empirical Theory of (Deep) Learning The Heavy-Tail Phenomenon in SGD

Reference 87

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no resolver link, observed 2026-08-06T14:45:20.130836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:45:20.130836Z digest=sha256:0345d98c22b3ebe4c2e064f33ab844d65ae788f93cdf8ebfad0c13541edc78a0

Observation 47b40d64-1369-4435-9986-d816d750f9d6 · outbound

This paper cites Nice: Noise injection and clamping estimation for neural network quantization.Mathematics, 9(17):2144, 2021.

SETOL: A Semi-Empirical Theory of (Deep) Learning Nice: Noise injection and clamping estimation for neural network quantization.Mathematics, 9(17):2144, 2021

Reference 88

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no resolver link, observed 2026-08-06T14:45:20.133626Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:45:20.133626Z digest=sha256:9ce2f91b8c029160038eb1937e2f9f0def7310995788d9f08e8c24c50f41abab

Observation fcf29405-ebb6-4dce-94fc-b5e7347a9d47 · outbound

This paper cites PACT: Parameterized Clipping Activation for Quantized Neural Networks.

SETOL: A Semi-Empirical Theory of (Deep) Learning PACT: Parameterized Clipping Activation for Quantized Neural Networks

Reference 89

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no resolver link, observed 2026-08-06T14:45:20.136533Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:45:20.136533Z digest=sha256:9461172fcdcf8aa1939e35886fd160f0de50d40f7b9a5e5923d33c6ab42ea18e

Observation 9bea8a30-f1af-415c-8633-5da0e2bc1bd8 · outbound

This paper cites an unresolved cited work.

SETOL: A Semi-Empirical Theory of (Deep) Learning Unresolved cited work

Reference 90

Resolution
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raw_fallback, observed 2026-08-06T14:45:20.953556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T14:45:20.139281Z digest=sha256:a933f282c22792194a2a3a82458b8913b3e99b91bf9ad990092f0c673f1599f9

Observation ca600568-16c9-4d50-9517-737bc706ca9f · outbound

This paper cites Engel, C.

SETOL: A Semi-Empirical Theory of (Deep) Learning Engel, C

Reference 91

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:45:20.946013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T14:45:20.141910Z digest=sha256:32850f796237f3af57894017e16fe22eddab0eac7203bc92b14e5a9fad4d1611

Observation 78b2cdaf-df16-49a4-ba4c-dc512919c73f · outbound

This paper cites Spin-glass models of neural networks.

SETOL: A Semi-Empirical Theory of (Deep) Learning Spin-glass models of neural networks

Reference 92

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:45:20.938272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T14:45:20.144782Z digest=sha256:f1334d24633a6dfb202742f77458caa06da4b7943f30b90a8c8e93aab8dcd1af

Observation 1f21dec4-c752-4cbe-bc26-5d6f8155f21d · outbound

This paper cites Martin and Karl F.

SETOL: A Semi-Empirical Theory of (Deep) Learning Martin and Karl F

Reference 94

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:45:20.930457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T14:45:20.150330Z digest=sha256:297db5e42982de4c91d0805513b1d796f101ea503f57398ed9cde95fed744bea

Observation afb8b973-1605-4a2b-81d1-b968ad0efbe9 · outbound

This paper cites an unresolved cited work.

SETOL: A Semi-Empirical Theory of (Deep) Learning Unresolved cited work

Reference 95

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unresolved
raw_fallback, observed 2026-08-06T14:45:20.922179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T14:45:20.153033Z digest=sha256:40d7009a8f536e390922a95f98308e01a23980d6f862551b83fc2d37f63b1c93

Observation 0b718813-c617-426e-a2be-953605dc93d0 · outbound

This paper cites Parisi and M.

SETOL: A Semi-Empirical Theory of (Deep) Learning Parisi and M

Reference 96

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:45:20.914386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T14:45:20.155418Z digest=sha256:3244899ef48e85cfb607387e35cd5c6b04656239fde7229891832837ddac790d

Observation 89432170-7e74-41ea-8da4-569c7984f32d · outbound

This paper cites Training Compute-Optimal Large Language Models.

SETOL: A Semi-Empirical Theory of (Deep) Learning Training Compute-Optimal Large Language Models

Reference 97

Resolution
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no resolver link, observed 2026-08-06T14:45:20.158379Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:45:20.158379Z digest=sha256:ed7ec0f23206500d0e68449e79e0ef3593ff8a2cfb2be1cca0afb574d6aaa25d

Observation a2255ed7-13bf-4a0d-a204-d7679aa664d0 · outbound

This paper cites an unresolved cited work.

SETOL: A Semi-Empirical Theory of (Deep) Learning Unresolved cited work

Reference 98

Resolution
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raw_fallback, observed 2026-08-06T14:45:20.906200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T14:45:20.160677Z digest=sha256:76870ad7ffe85d9a98e2ae4f6234179abf9cc1059e5fc866bc9cb3d3d1982747

Observation bbbce530-0728-4de1-a2d8-1dfe419428fe · outbound

This paper cites Teacher-student architecture for knowledge distillation: A survey, 2023.

SETOL: A Semi-Empirical Theory of (Deep) Learning Teacher-student architecture for knowledge distillation: A survey, 2023

Reference 99

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:45:20.897892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T14:45:20.163454Z digest=sha256:21b7dbefcd52b82670df1f1d33c3e40e6b211bf6841a0b02d7f57d756cb8bdae

Observation ccfa045d-0a7a-4eae-8a82-7ba39c71aaf3 · outbound

This paper cites Vallet, J.-G.

SETOL: A Semi-Empirical Theory of (Deep) Learning Vallet, J.-G

Reference 100

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:45:20.889555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T14:45:20.166498Z digest=sha256:86031ec4db43fca889b24ac4474b83ffddf309ec50c20a7567f85814ea9e37dd

Observation 4980da65-f130-4ad1-a403-509838d2bc0e · outbound

This paper cites Opper and W.

SETOL: A Semi-Empirical Theory of (Deep) Learning Opper and W

Reference 101

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:45:20.881292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T14:45:20.169177Z digest=sha256:e2f7a85daa4113267379dd4f0eb2add5261549a757ed5c0a71f720632e0fcfe9

Pith citing papers

Observation 72cb16c0-a1f1-4347-84bc-3523ced7367d · inbound

Detecting overfitting in Neural Networks during long-horizon grokking using Random Matrix Theory cites this paper.

Detecting overfitting in Neural Networks during long-horizon grokking using Random Matrix Theory SETOL: A Semi-Empirical Theory of (Deep) Learning

Reference 14

Resolution
metadata mismatch
arxiv_id, observed 2026-05-13T05:27:18.413548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-13T05:26:59.138667Z digest=sha256:9908d6d2b7bfeb7ebcb748ccdc7ed102c6a01fd5db933a4c591169cd00e72e04

Observation a3827298-11fd-4c51-a6b1-7e11dd4469e6 · inbound

Detecting overfitting in Neural Networks during long-horizon grokking using Random Matrix Theory cites this paper.

Detecting overfitting in Neural Networks during long-horizon grokking using Random Matrix Theory SETOL: A Semi-Empirical Theory of (Deep) Learning

Reference 14

Resolution
metadata mismatch
arxiv_id, observed 2026-05-15T05:35:04.252983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-15T05:32:21.091094Z digest=sha256:7edbe621f39e79559179a9b8fda3fce4289f0d3ff65cea7b53d14f254b5cb903

Observation d3230d6e-cf54-4dc8-a7e8-4b19bf292f4c · inbound

Characterizing Learning in Deep Neural Networks using Tractable Algorithmic Complexity Analysis cites this paper.

Characterizing Learning in Deep Neural Networks using Tractable Algorithmic Complexity Analysis SETOL: A Semi-Empirical Theory of (Deep) Learning

Reference 298

Resolution
verified exact
arxiv_id, observed 2026-05-20T20:29:00.035235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-05-20T20:24:16.373261Z digest=sha256:11bac9b027726f405d7b6afa99ffb10f6dc81ece0c19a14b1605cc928fdc5222

Observation ff1b2549-9f1f-4014-9568-60e1c3c80ce9 · inbound

Fast Generalization after Interpolation via Critically Damped Momentum Optimization cites this paper.

Fast Generalization after Interpolation via Critically Damped Momentum Optimization SETOL: A Semi-Empirical Theory of (Deep) Learning

Reference 56

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T21:56:16.189925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-06-28T15:56:15.199333Z digest=sha256:6df7e557d8ca79d15f6d270138a988c8629f97b6e4c0b8ed0ae985971262d11b

Observation 35c31516-5a2d-4152-951b-d1a54d05aa01 · inbound

Patnaik-Pearson intrinsic dimension for internal representations of neural networks cites this paper.

Patnaik-Pearson intrinsic dimension for internal representations of neural networks SETOL: A Semi-Empirical Theory of (Deep) Learning

Reference 22

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T02:59:25.686745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-06-26T18:42:23.506693Z digest=sha256:cd3483d345a3fa6e4fa9ab8ae46764e22382985455654887f83b13106a873118

Observation 1e2ab913-6ec1-464f-ac2c-69eff9486ca9 · inbound

Patnaik-Pearson intrinsic dimension for internal representations of neural networks cites this paper.

Patnaik-Pearson intrinsic dimension for internal representations of neural networks SETOL: A Semi-Empirical Theory of (Deep) Learning

Reference 23

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T23:49:02.147665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-07-03T23:41:26.610335Z digest=sha256:d4f14e13019201be0175d24cf565f511487928a473107530b4251107912d97f6