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Paper Citation Record · LEDGER

A Spin Glass Characterization of Neural Networks

As of 10 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 0 inbound Pith citation observations for arXiv:2508.07397.

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

pith.paper-citation-record.v1
2508.07397 v1

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T22:10:36.934929Z

measured 54 of 54 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

54 of 54 outbound references displayed

  • verified exact3
  • verified fuzzy41
  • unresolved10
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 86228b3c-ff91-4349-b3c8-5599c2481539 · outbound

This paper cites Amit, Hanoch Gutfreund, and Haim Sompolinsky.

A Spin Glass Characterization of Neural Networks Amit, Hanoch Gutfreund, and Haim Sompolinsky

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:10:37.769076Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.685641Z digest=sha256:7f51899dd3cc440fe8e86ff5830eb3d6a63bbf2ed8e928ccc0b0232e9d27575b

Observation e90a0048-6b72-4cae-989b-f1cb97782d25 · outbound

This paper cites Symmetry & Critical Points.

A Spin Glass Characterization of Neural Networks Symmetry & Critical Points

Reference 2

Resolution
verified exact
local_arxiv, observed 2026-08-05T22:10:37.083943Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.690758Z digest=sha256:a6a2b9a6579d38dcd6aa80e899bb5514b589dba8c42469e3c47a96c0db162a71

Observation 594bb961-f664-489d-a87d-048e38c05ea4 · outbound

This paper cites Complexity of random smooth functions on the high-dimensional sphere.

A Spin Glass Characterization of Neural Networks Complexity of random smooth functions on the high-dimensional sphere

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:10:37.755181Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.695814Z digest=sha256:0ef1a2b1c88e3d1092df7d2e474ff9bff6b3edeb15ff7aaff9672aa20b55f5b0

Observation f509e5d4-7532-4d9a-9914-dc440a96a99a · outbound

This paper cites Schoenholz, Jascha Sohl-Dickstein, and Surya Ganguli.

A Spin Glass Characterization of Neural Networks Schoenholz, Jascha Sohl-Dickstein, and Surya Ganguli

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:10:37.740380Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.700888Z digest=sha256:c2ac660359daab9c6649d0d77ac010e6c7a6aa453cfe54af8e4f2824e8d97161

Observation 630d0bd0-d161-4f6d-8e9c-416d2fd37fbe · outbound

This paper cites Curtis, and Jorge Nocedal.

A Spin Glass Characterization of Neural Networks Curtis, and Jorge Nocedal

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:10:37.725967Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.705888Z digest=sha256:c4f3195258586062ce4a6b1e5b0386844c629b515ff444739a8d3f654055333d

Observation dcdd4292-623d-469f-8df6-dc1434111002 · outbound

This paper cites Bray and David S.

A Spin Glass Characterization of Neural Networks Bray and David S

Reference 6

Resolution
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raw_fallback, observed 2026-08-05T22:10:37.711938Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.710488Z digest=sha256:841f46e039afe0c0f3735718ceec843dcff287d80092198467bb9f5c38ea177c

Observation 13f53e2a-3a39-4570-af7d-46db36e1269c · outbound

This paper cites an unresolved cited work.

A Spin Glass Characterization of Neural Networks Unresolved cited work

Reference 7

Resolution
unresolved
raw_fallback, observed 2026-08-05T22:10:37.697531Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.715969Z digest=sha256:a105439a3b948c25713c944bc4e42b02e64a4f44ddd824f019bba45f282eeca2

Observation b4936dad-bb28-4f09-849e-55a8f6f05efa · outbound

This paper cites Quantum langevin dynamics for optimization.

A Spin Glass Characterization of Neural Networks Quantum langevin dynamics for optimization

Reference 8

Resolution
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raw_fallback, observed 2026-08-05T22:10:37.683272Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.720674Z digest=sha256:cd8943a7e4bd953312a63a65a73ddc4a96b0a18f553a4294a9384eeda2b8ab7a

Observation 9113d470-7b1d-49fb-ac87-09159d4c511b · outbound

This paper cites Landscape analysis for shallow neural networks: Complete classification of critical points for affine target functions.

A Spin Glass Characterization of Neural Networks Landscape analysis for shallow neural networks: Complete classification of critical points for affine target functions

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:10:37.668071Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.725010Z digest=sha256:88d8ecf0bc2a1a3762fc725501b809b163bf407d295fa5aac9f6a6933d68f7de

Observation 17c6cd81-18bd-4ba6-a4b6-db8c9cebd100 · outbound

This paper cites The loss surfaces of multilayer networks.

A Spin Glass Characterization of Neural Networks The loss surfaces of multilayer networks

Reference 10

Resolution
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raw_fallback, observed 2026-08-05T22:10:37.654347Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.729477Z digest=sha256:d616d66ed2f3b79d8243718edd87f78ee98f331bf4737562f9dcc801739c0b51

Observation e0171153-b3d5-4c6d-90e4-09929c77f1a5 · outbound

This paper cites Dauphin, Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, Surya Ganguli, and Yoshua Bengio.

A Spin Glass Characterization of Neural Networks Dauphin, Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, Surya Ganguli, and Yoshua Bengio

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:10:37.640077Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.734178Z digest=sha256:4416bb0b7b47c46e9453f7807d699ef3ece30c68614f83ae0c91ca7340dccb9a

Observation e31dc1b3-80e1-4b74-93f2-84f7f6f886b7 · outbound

This paper cites Unifying Grokking and Double Descent.

A Spin Glass Characterization of Neural Networks Unifying Grokking and Double Descent

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-05T22:10:36.740327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:10:36.740327Z digest=sha256:6aaac28365d3455d718069a9b5a8fe4b7e55bf832d31b8ef2f754e8020c81f90

Observation ca817741-f122-41d9-90c8-d4543617a0e7 · outbound

This paper cites Towards a mathematical understanding of neural network-based machine learning: What we know and what we don't.

A Spin Glass Characterization of Neural Networks Towards a mathematical understanding of neural network-based machine learning: What we know and what we don't

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:10:37.624690Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.746218Z digest=sha256:c69402739be0e954f3d238819949e33e85730953c13a97baa2040a487d67bdc7

Observation 804e0a78-74d7-42ee-8d6c-bc3031b77bb6 · outbound

This paper cites Engel and C.

A Spin Glass Characterization of Neural Networks Engel and C

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:10:37.609746Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.750235Z digest=sha256:3a0b6498d4e45533fcbd7102b5f53ba63a338cbb67a739b4c9b5f8a01f59a53b

Observation 7ec34c5a-18a2-480f-9ae7-29323aef85a6 · outbound

This paper cites Entropy and mutual information in models of deep neural networks.

A Spin Glass Characterization of Neural Networks Entropy and mutual information in models of deep neural networks

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:10:37.595316Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.755035Z digest=sha256:d362c01bc8a6547be723280b8af3d07eac1a32e3e9098242733a89e840f9c6b3

Observation 1bf11610-8576-4405-ba48-c449a4e21023 · outbound

This paper cites The space of interactions in neural network models.

A Spin Glass Characterization of Neural Networks The space of interactions in neural network models

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:10:37.580726Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.759344Z digest=sha256:15cc23c59f2f1ea1b78a0fcafd125247fd2e1108071e25a4843e2bd07b7efade

Observation 4392b002-6f6d-46f7-a83e-1714fb7da2c1 · outbound

This paper cites an unresolved cited work.

A Spin Glass Characterization of Neural Networks Unresolved cited work

Reference 17

Resolution
unresolved
raw_fallback, observed 2026-08-05T22:10:37.566446Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.763598Z digest=sha256:4bf16ddbee2497dc05ba4258c2b4601919358f6b58e648679dac07f5be6eb442

Observation 0314d6df-8972-44ce-976c-672e8ecb30b9 · outbound

This paper cites Flat minima.

A Spin Glass Characterization of Neural Networks Flat minima

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:10:37.550070Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.767863Z digest=sha256:6d0b8eb45c21e00d5b246da7b8347f4f796a9bfbf95c5e8c3c767253a81ca095

Observation 9938f338-1c52-49cb-b342-da257cd076c0 · outbound

This paper cites Hopfield.

A Spin Glass Characterization of Neural Networks Hopfield

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:10:37.534920Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.771840Z digest=sha256:0fe5708553a426525b05ffaa77b939918f5f09a48d17c594079993f5d6bcd91b

Observation 7fcff1ce-f919-4208-bd97-1013a7aa10ae · outbound

This paper cites an unresolved cited work.

A Spin Glass Characterization of Neural Networks Unresolved cited work

Reference 20

Resolution
unresolved
raw_fallback, observed 2026-08-05T22:10:37.519767Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.776944Z digest=sha256:3b4e7ae8b499092011173959ab71fce2b30de5bed7a50c60718f12790c032f67

Observation 84b68ad5-c5d0-4f50-878c-1eb48f82e889 · outbound

This paper cites Schmidt, and Michael Riis Andersen.

A Spin Glass Characterization of Neural Networks Schmidt, and Michael Riis Andersen

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:10:37.503880Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.781183Z digest=sha256:c57263ddbc9a206c533a7bfb23c6383e3a0623801e99f9d6cc020048fce800f4

Observation e49df658-a69a-4364-a354-452ac49b7039 · outbound

This paper cites Inference from correlated patterns: a unified theory for perceptron learning and linear vector channels.

A Spin Glass Characterization of Neural Networks Inference from correlated patterns: a unified theory for perceptron learning and linear vector channels

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:10:37.489055Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.786087Z digest=sha256:a66176e4eb4ec41198d0a7cf418b855f4262d3d05cf1bca4a9d8b16354d46fb0

Observation 267ed49e-e797-493b-abc9-e8bc170253ec · outbound

This paper cites mingpt: A minimal pytorch re-implementation of gpt.

A Spin Glass Characterization of Neural Networks mingpt: A minimal pytorch re-implementation of gpt

Reference 23

Resolution
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no resolver link, observed 2026-08-05T22:10:36.790274Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:10:36.790274Z digest=sha256:8239b5ba1367d1de7276eaad23e0c328d5c82557378668ed8a92147996f121be

Observation 8133c255-5e08-434f-ba3c-4e1b27019bcf · outbound

This paper cites Adam: A Method for Stochastic Optimization.

A Spin Glass Characterization of Neural Networks Adam: A Method for Stochastic Optimization

Reference 24

Resolution
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no resolver link, observed 2026-08-05T22:10:36.794592Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:10:36.794592Z digest=sha256:ff5480132c24ebb10b66b04dfdaec51943c2466681470975aed5b0a27af90138

Observation 3d50c9c3-e7d8-4ad4-b6b2-37cce0cca504 · outbound

This paper cites Berg, Wan-Yen Lo, Piotr Doll \'a r, and Ross Girshick.

A Spin Glass Characterization of Neural Networks Berg, Wan-Yen Lo, Piotr Doll \'a r, and Ross Girshick

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:10:37.464077Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.799548Z digest=sha256:151428254c8d90de0a2f0d5c131956a9651885a104a36d3d969a317d250746d0

Observation 9e5dfefa-28b2-43a7-951f-2fba1659d442 · outbound

This paper cites Learning multiple layers of features from tiny images.

A Spin Glass Characterization of Neural Networks Learning multiple layers of features from tiny images

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-05T22:10:36.804479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:10:36.804479Z digest=sha256:68b08dc843b15214b08b244da8efeda54a624b629cfb7edf69a5b896800aa385

Observation 05aa5719-9ad2-4817-8f6b-5744866f2743 · outbound

This paper cites Deep learning.

A Spin Glass Characterization of Neural Networks Deep learning

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:10:37.440167Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.808968Z digest=sha256:40349dc00327d7bbe51983d60c145f4ae1db325aa1f4850aa7e77149fcffd30d

Observation 18f1b297-7dd5-4ee6-b142-93f744aeac76 · outbound

This paper cites Gradient-based learning applied to document recognition.

A Spin Glass Characterization of Neural Networks Gradient-based learning applied to document recognition

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:10:37.425639Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.813298Z digest=sha256:a820ac0388083681f71a66287509deee19742e0680acebf239ebb5aac5bb7a61

Observation 5980170b-c884-4271-aff3-f410154749b0 · outbound

This paper cites Schoenholz, Jeffrey Pennington, and Jascha Sohl-Dickstein.

A Spin Glass Characterization of Neural Networks Schoenholz, Jeffrey Pennington, and Jascha Sohl-Dickstein

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:10:37.410761Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.818197Z digest=sha256:1711212638195e9a998a587bbd72566ee3526ed727aa210898aec66cdc29d3c6

Observation 08a8281b-4ef0-4fac-8639-c2af926758c2 · outbound

This paper cites Stochastic modified equations and dynamics of stochastic gradient algorithms i: Mathematical foundations.

A Spin Glass Characterization of Neural Networks Stochastic modified equations and dynamics of stochastic gradient algorithms i: Mathematical foundations

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:10:37.395104Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.823242Z digest=sha256:b6f4103585937645d4c196a9196afc4da6b7780ba3eacc98567473f947192225

Observation 567053f8-4e96-4cf9-baaa-dade6869bde9 · outbound

This paper cites Hoffman, and David M.

A Spin Glass Characterization of Neural Networks Hoffman, and David M

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:10:37.379813Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.827546Z digest=sha256:b57c615bbb57d8cf3ba2c30a15f600058e95d985e03f7896c312769cb4cf7271

Observation 23d1b6aa-60ff-438d-8004-6e23706a38e6 · outbound

This paper cites Information, Physics, and Computation.

A Spin Glass Characterization of Neural Networks Information, Physics, and Computation

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:10:37.364700Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.832846Z digest=sha256:3d7569e8abb139d45146259c45b147e92d92fb8d3fd63ab6c88f0f3b2d49bcfc

Observation a25ef33e-a7dc-4448-a5a8-05b4d587d0ac · outbound

This paper cites Spin Glass Theory and Beyond: An Introduction to the Replica Method and Its Applications , volume 9 of World Scientific Lecture Notes in Physics.

A Spin Glass Characterization of Neural Networks Spin Glass Theory and Beyond: An Introduction to the Replica Method and Its Applications , volume 9 of World Scientific Lecture Notes in Physics

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:10:37.348844Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.837155Z digest=sha256:0fdf29c84a64988db561a7d9a57acbed8b8b91fe1200daabf14d1f8c2bfcc479

Observation 9a708c58-6813-4f90-84ed-c2eea132b5c4 · outbound

This paper cites Bridging lottery ticket and grokking: Understanding grokking from inner structure of networks.

A Spin Glass Characterization of Neural Networks Bridging lottery ticket and grokking: Understanding grokking from inner structure of networks

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:10:37.334434Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.841949Z digest=sha256:4e472ff6f8ced62ab49ecf4bd9e99f8f8475913ffc484aeefbba5ad6fe355a11

Observation 45a2c7c8-c9bb-4ca5-a0ed-7d4e0456e622 · outbound

This paper cites an unresolved cited work.

A Spin Glass Characterization of Neural Networks Unresolved cited work

Reference 35

Resolution
unresolved
raw_fallback, observed 2026-08-05T22:10:37.319047Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.846331Z digest=sha256:a5e1ae262381797bd42a1fbd528861e87877d0d0839f73125a9f04e313387e8f

Observation 14bb0c17-66e2-4a60-850f-283010f312b6 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

A Spin Glass Characterization of Neural Networks Pytorch: An imperative style, high-performance deep learning library

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:10:37.305414Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.850704Z digest=sha256:5a078de5f2c980aef08da16c213c038dd1ee030dba14881a3ec04ee7c51d4a2a

Observation 088ae899-55c0-4843-917e-14fb15376ca0 · outbound

This paper cites Exponential expressivity in deep neural networks through transient chaos.

A Spin Glass Characterization of Neural Networks Exponential expressivity in deep neural networks through transient chaos

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:10:37.290888Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.855754Z digest=sha256:e67da0cbb820929b841e25b9c89014109e02123d0e6da173d6b60a8d4b497165

Observation d26c06fd-6bd2-4cdb-bc78-7b502ec7a282 · outbound

This paper cites On the expressive power of deep neural networks.

A Spin Glass Characterization of Neural Networks On the expressive power of deep neural networks

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:10:37.276292Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.859797Z digest=sha256:6c89e0ca1dc02e74806b7e9106b0bf532d6b53b941a852d6c09bde38755abf51

Observation 3fb486ad-801b-4778-89d4-8ab65f60afca · outbound

This paper cites High-Resolution Image Synthesis with Latent Diffusion Models.

A Spin Glass Characterization of Neural Networks High-Resolution Image Synthesis with Latent Diffusion Models

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-05T22:10:36.863825Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:10:36.863825Z digest=sha256:0083b8946770b3cf834c1cedfaf7f6a54079c12fa104ecb7f521faa427901660

Observation eae52684-0e2d-4780-8058-436a55d27974 · outbound

This paper cites Singularity of the H essian in deep learning.

A Spin Glass Characterization of Neural Networks Singularity of the H essian in deep learning

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:10:37.262915Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.868024Z digest=sha256:e9c9e154278266f7f43b926543b2dc74ae70841010c35e17a0c3f75d2ec02e2c

Observation 3b8774df-09a5-4d85-8cdc-ba4d90796fa1 · outbound

This paper cites Schoenholz, Justin Gilmer, Surya Ganguli, and Jascha Sohl-Dickstein.

A Spin Glass Characterization of Neural Networks Schoenholz, Justin Gilmer, Surya Ganguli, and Jascha Sohl-Dickstein

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:10:37.249265Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.873055Z digest=sha256:7ddb7eac9d3fa749eec7d55b9cc79bc8326dd8d96aefad9e01bff7e057c666bf

Observation 9dc64373-f7cd-44e9-a98c-dbee3e89f447 · outbound

This paper cites What Is Life? The Physical Aspect of the Living Cell.

A Spin Glass Characterization of Neural Networks What Is Life? The Physical Aspect of the Living Cell

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:10:37.234599Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.877247Z digest=sha256:2666761b61964cffa4828b9f8080e640af895c5a85525d387b4739975963f792

Observation aa37295f-de20-4a71-be47-4f75a14c1e44 · outbound

This paper cites Solvable model of a spin-glass.

A Spin Glass Characterization of Neural Networks Solvable model of a spin-glass

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:10:37.219785Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.882593Z digest=sha256:cc4e7a9040cca29d0f5e7b2632e63b2e043720868415fe8690bed161e4ec8142

Observation dad12ce1-3705-4001-9589-d72c58e21c89 · outbound

This paper cites Su, and Michael I.

A Spin Glass Characterization of Neural Networks Su, and Michael I

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:10:37.205410Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.887379Z digest=sha256:c55227f59faa1c0cdc0a2d8e0079e940412230712eb04c02fc25642f60e1a7d0

Observation 946ef3ad-3c3b-4878-9020-69c150650947 · outbound

This paper cites Mean Field Models for Spin Glasses: Volume I: Basic Examples , volume 54 of Ergebnisse der Mathematik und ihrer Grenzgebiete.

A Spin Glass Characterization of Neural Networks Mean Field Models for Spin Glasses: Volume I: Basic Examples , volume 54 of Ergebnisse der Mathematik und ihrer Grenzgebiete

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:10:37.190521Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.891769Z digest=sha256:34413a23eca3abcb5566201512feacebb702aa66641554bc4f500d8fc1023839

Observation 150a4066-e9d2-4a70-93d3-c42eaf9ef865 · outbound

This paper cites Opening the Black Box: predicting the trainability of deep neural networks with reconstruction entropy.

A Spin Glass Characterization of Neural Networks Opening the Black Box: predicting the trainability of deep neural networks with reconstruction entropy

Reference 46

Resolution
verified exact
local_arxiv, observed 2026-08-05T22:10:37.017416Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.896815Z digest=sha256:9d013b41d1d7fdd046b82e8c78f34d8864b963bf698df9be4e3d765df4fafe56

Observation e3bef18a-c8df-44c9-b9c2-dd95ec55d0c1 · outbound

This paper cites Pereira, and William Bialek.

A Spin Glass Characterization of Neural Networks Pereira, and William Bialek

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:10:37.176053Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.902469Z digest=sha256:5c0a85f7af62ac844ee14857808a61d03523437885a36b37ed697951775ae7d4

Observation fc217df2-fc32-48e6-9b0d-fe6e8552e7c8 · outbound

This paper cites Gomez, Łukasz Kaiser, and Illia Polosukhin.

A Spin Glass Characterization of Neural Networks Gomez, Łukasz Kaiser, and Illia Polosukhin

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:10:37.160104Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.907016Z digest=sha256:1c7a6a266c744f433474b0dca480f24ed9a8596ffaf9573b261645745c7ff4ba

Observation 325af8e8-56d1-4e08-9f8d-0ebc77dfe8fa · outbound

This paper cites Tent: Fully test-time adaptation by entropy minimization.

A Spin Glass Characterization of Neural Networks Tent: Fully test-time adaptation by entropy minimization

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:10:37.145512Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.912196Z digest=sha256:b55b6947fbc03f56e15190b440d4a31362a8968f86db78cae37a219a05cb6b47

Observation 34ee1b45-73af-41cc-938c-14da27e6b2e6 · outbound

This paper cites Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks.

A Spin Glass Characterization of Neural Networks Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-05T22:10:36.916952Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:10:36.916952Z digest=sha256:8d417920353d682a8efd8ed3e8fa28541129be4d891acb450d35ef0a4c53f5a6

Observation afb9b11a-5971-49a8-90b1-c9a26d458fad · outbound

This paper cites Stochastic gradient descent introduces an effective landscape-dependent regularization favoring flat solutions.

A Spin Glass Characterization of Neural Networks Stochastic gradient descent introduces an effective landscape-dependent regularization favoring flat solutions

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:10:37.130240Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.921941Z digest=sha256:8f475e3d04c3416e9654d4c1f976930c5a3e2a1c8b49e020a9dff6e9044a3a6e

Observation 7b7b3f26-d640-4dec-8bd2-970b77dd829c · outbound

This paper cites Statistical physics of inference: Thresholds and algorithms.

A Spin Glass Characterization of Neural Networks Statistical physics of inference: Thresholds and algorithms

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:10:37.113526Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.926259Z digest=sha256:b064743d07994fc5a03aa501d699e7ad13194df50b24a1fd96ee1d8ddd4ebe99

Observation ae64905e-ef30-4b1e-b4d0-8123b196629a · outbound

This paper cites Understanding deep learning requires rethinking generalization.

A Spin Glass Characterization of Neural Networks Understanding deep learning requires rethinking generalization

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:10:37.098964Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.930471Z digest=sha256:d4cc0378a671774d401b9f0d9464d23602c97bb6b666ccf1903b742f45fb9c28

Observation c06d3ca6-5826-416f-a04a-06fc84a6eb43 · outbound

This paper cites Edge of chaos as a guiding principle for modern neural network training.

A Spin Glass Characterization of Neural Networks Edge of chaos as a guiding principle for modern neural network training

Reference 54

Resolution
verified exact
local_arxiv, observed 2026-08-05T22:10:36.978404Z

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.

source=arxiv_source observed=2026-08-05T22:10:36.934929Z digest=sha256:3311a75aca5c9ee8e35d7d7ee639090ffe95a4de253d3906633031ff6125c216

Pith citing papers

No inbound Pith citation observations are available.