Paper Citation Record · LEDGER

Distributional simplicity bias and effective convexity in Energy Based Models

As of 10 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 1 inbound Pith citation observation for arXiv:2605.07844.

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

pith.paper-citation-record.v1
2605.07844 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-11T02:24:42.639358Z

measured 36 of 36 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-30T11:53:54.585915Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

35 of 35 outbound references displayed

verified exact8
verified fuzzy26
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5c3cead8-45a8-4034-8bde-47856ab7d12c · outbound

This paper cites Deep learning generalizes because the parameter-function map is biased towards simple functions.

Distributional simplicity bias and effective convexity in Energy Based Models Deep learning generalizes because the parameter-function map is biased towards simple functions

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:25:53.740589Z

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 1a343c59-1714-4e22-80b3-e90aeafcc86d · outbound

This paper cites Sgd on neural networks learns functions of increasing complexity.Advances in neural information processing systems, 32.

Distributional simplicity bias and effective convexity in Energy Based Models Sgd on neural networks learns functions of increasing complexity.Advances in neural information processing systems, 32

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:55:21.508410Z

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 3c564f30-1595-4bfb-a601-e33fac787658 · outbound

This paper cites Neural networks trained with sgd learn distributions of increasing complexity.

Distributional simplicity bias and effective convexity in Energy Based Models Neural networks trained with sgd learn distributions of increasing complexity

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:55:21.504999Z

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 3ffadbe6-2bfa-4888-acfc-c22d56627117 · outbound

This paper cites A distributional simplicity bias in the learning dynamics of transformers.Advances in Neural Information Processing Systems, 37:96207–96228.

Distributional simplicity bias and effective convexity in Energy Based Models A distributional simplicity bias in the learning dynamics of transformers.Advances in Neural Information Processing Systems, 37:96207–96228

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:55:21.501910Z

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 73e94549-0d15-4ebb-8a5e-730c7422e1d8 · outbound

This paper cites Neural Networks Learn Statistics of Increasing Complexity.

Distributional simplicity bias and effective convexity in Energy Based Models Neural Networks Learn Statistics of Increasing Complexity

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T02:25:53.721268Z

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 da0f22af-ea31-4280-ba49-1e888a21c31e · outbound

This paper cites How transformers learn structured data: insights from hierarchical filtering.

Distributional simplicity bias and effective convexity in Energy Based Models How transformers learn structured data: insights from hierarchical filtering

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:25:53.693368Z

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 3750bdbf-63ff-4677-829c-5752c6a83ba5 · outbound

This paper cites Compression of Structured Data with Autoencoders: Provable Benefit of Nonlinearities and Depth.

Distributional simplicity bias and effective convexity in Energy Based Models Compression of Structured Data with Autoencoders: Provable Benefit of Nonlinearities and Depth

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:25:53.698526Z

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 e4042118-de45-4db2-a7ea-bdf9fdaf2808 · outbound

This paper cites Inferring effective couplings with restricted boltzmann machines.SciPost Physics, 16(4):095.

Distributional simplicity bias and effective convexity in Energy Based Models Inferring effective couplings with restricted boltzmann machines.SciPost Physics, 16(4):095

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:55:21.577547Z

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 712fe6af-2c8a-47ef-807f-e52b4cf58436 · outbound

This paper cites Inferring higher-order couplings with neural networks.Physical Review Letters, 135(20):207301.

Distributional simplicity bias and effective convexity in Energy Based Models Inferring higher-order couplings with neural networks.Physical Review Letters, 135(20):207301

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:55:21.515838Z

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 0719f44d-06c4-487c-bf2d-1c483407ba00 · outbound

This paper cites How Compositional Generalization and Creativity Improve as Diffusion Models are Trained.

Distributional simplicity bias and effective convexity in Energy Based Models How Compositional Generalization and Creativity Improve as Diffusion Models are Trained

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:25:53.711358Z

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 e9933c87-9769-4f12-91cd-2620a14f1c11 · outbound

This paper cites A theory of learning data statistics in diffusion models, from easy to hard.

Distributional simplicity bias and effective convexity in Energy Based Models A theory of learning data statistics in diffusion models, from easy to hard

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-21T03:03:42.220012Z

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 47c96726-af1e-4b79-8357-c9c8241c7224 · outbound

This paper cites Exact solution for on-line learning in multilayer neural networks.Physical Review Letters, 74(21):4337.

Distributional simplicity bias and effective convexity in Energy Based Models Exact solution for on-line learning in multilayer neural networks.Physical Review Letters, 74(21):4337

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:55:21.529727Z

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 77433117-fe8d-41f0-9327-ecedee3ab586 · outbound

This paper cites A mean field view of the landscape of two-layer neural networks.Proceedings of the National Academy of Sciences, 115(33):E7665–E7671.

Distributional simplicity bias and effective convexity in Energy Based Models A mean field view of the landscape of two-layer neural networks.Proceedings of the National Academy of Sciences, 115(33):E7665–E7671

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:55:21.526576Z

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 693b8660-2db3-42bc-abcf-500d7a905e71 · outbound

This paper cites Learning protein constitutive motifs from sequence data.

Distributional simplicity bias and effective convexity in Energy Based Models Learning protein constitutive motifs from sequence data

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:55:21.568448Z

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 be1e64fc-4f30-46f2-bed1-04f1c6099848 · outbound

This paper cites Uncovering statistical structure in large-scale neural activity with restricted boltzmann machines.

Distributional simplicity bias and effective convexity in Energy Based Models Uncovering statistical structure in large-scale neural activity with restricted boltzmann machines

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:25:53.684119Z

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 ea321a84-2552-4663-a562-722a0d3e8ffe · outbound

This paper cites The loss surfaces of multilayer networks.Proceedings of AISTATS.

Distributional simplicity bias and effective convexity in Energy Based Models The loss surfaces of multilayer networks.Proceedings of AISTATS

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:55:21.552306Z

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 7201b81e-8bc0-4b0d-a482-37f8b5eab4e8 · outbound

This paper cites Escaping from saddle points—online stochastic gradient for tensor decomposition.

Distributional simplicity bias and effective convexity in Energy Based Models Escaping from saddle points—online stochastic gradient for tensor decomposition

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:55:21.555547Z

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 349e0564-f881-4954-bace-f1c79f27f1c8 · outbound

This paper cites On nonconvex optimization for machine learning: Gradients, stochasticity, and saddle points.Journal of the ACM (JACM), 68(2):1–29.

Distributional simplicity bias and effective convexity in Energy Based Models On nonconvex optimization for machine learning: Gradients, stochasticity, and saddle points.Journal of the ACM (JACM), 68(2):1–29

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:55:21.564584Z

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 428730f2-4439-498b-8b14-c9d0cf8cd1f9 · outbound

This paper cites Neural tangent kernel: Convergence and generalization in neural networks.Advances in neural information processing systems, 31.

Distributional simplicity bias and effective convexity in Energy Based Models Neural tangent kernel: Convergence and generalization in neural networks.Advances in neural information processing systems, 31

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:55:21.548747Z

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 370eb9fd-c254-4478-8d7b-d6c633561bb9 · outbound

This paper cites Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit.

Distributional simplicity bias and effective convexity in Energy Based Models Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:55:21.519981Z

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 b6cc9a3d-6e60-4ac9-a1bb-47e26b121c4e · outbound

This paper cites Implicit regularization in nonconvex statistical estimation: Gradient descent converges linearly for phase retrieval and matrix completion.

Distributional simplicity bias and effective convexity in Energy Based Models Implicit regularization in nonconvex statistical estimation: Gradient descent converges linearly for phase retrieval and matrix completion

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:55:21.580395Z

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 d42d4aa4-28c8-4cc4-861a-13220a3ab2fb · outbound

This paper cites Understanding deep learning requires rethinking generalization.

Distributional simplicity bias and effective convexity in Energy Based Models Understanding deep learning requires rethinking generalization

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-13T11:56:40.372790Z

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 67d0ad11-298e-4c01-a774-610af1fe632e · outbound

This paper cites Exact training of restricted boltzmann machines on intrinsically low dimensional data.Physical Review Letters, 127(15):158303.

Distributional simplicity bias and effective convexity in Energy Based Models Exact training of restricted boltzmann machines on intrinsically low dimensional data.Physical Review Letters, 127(15):158303

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:55:21.561512Z

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 954ddfda-932f-4450-b792-a2808f410eea · outbound

This paper cites On the anatomy of mcmc-based maximum likelihood learning of energy-based models.Proceedings of the AAAI Conference on Artificial Intelligence.

Distributional simplicity bias and effective convexity in Energy Based Models On the anatomy of mcmc-based maximum likelihood learning of energy-based models.Proceedings of the AAAI Conference on Artificial Intelligence

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:55:21.585903Z

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 a4515460-735e-4a6b-889d-d4d9fc0e4b89 · outbound

This paper cites Exact training of restricted boltzmann machines on intrinsically low- dimensional data.Physical Review Letters, 127:158303.

Distributional simplicity bias and effective convexity in Energy Based Models Exact training of restricted boltzmann machines on intrinsically low- dimensional data.Physical Review Letters, 127:158303

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:55:21.583392Z

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 ed1d2e76-d61c-4656-84c3-d62ea0154604 · outbound

This paper cites Explaining the effects of non- convergent MCMC in the training of energy-based models.

Distributional simplicity bias and effective convexity in Energy Based Models Explaining the effects of non- convergent MCMC in the training of energy-based models

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:55:21.571593Z

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 fbf7e3b8-9cb6-47c5-8550-d8f3812daabb · outbound

This paper cites Representational power of restricted boltzmann machines and deep belief networks.Neural computation, 20(6):1631–1649.

Distributional simplicity bias and effective convexity in Energy Based Models Representational power of restricted boltzmann machines and deep belief networks.Neural computation, 20(6):1631–1649

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:55:21.511993Z

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 9b934f0e-5c5a-48bb-9059-0de3348aef56 · outbound

This paper cites Refinements of universal approximation results for deep belief networks and restricted boltzmann machines.Neural computation, 23(5):1306–1319.

Distributional simplicity bias and effective convexity in Energy Based Models Refinements of universal approximation results for deep belief networks and restricted boltzmann machines.Neural computation, 23(5):1306–1319

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:55:21.558521Z

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 aa1d1782-a238-46f1-8aba-93a1d5ff8065 · outbound

This paper cites Expressive power and approximation errors of restricted boltzmann machines.Advances in neural information processing systems, 24.

Distributional simplicity bias and effective convexity in Energy Based Models Expressive power and approximation errors of restricted boltzmann machines.Advances in neural information processing systems, 24

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:55:21.536759Z

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 e36f9728-0a4a-45cb-898a-00b933f6826d · outbound

This paper cites Backpropagation applied to handwritten zip code recognition.Neural computation, 1(4):541– 551.

Distributional simplicity bias and effective convexity in Energy Based Models Backpropagation applied to handwritten zip code recognition.Neural computation, 1(4):541– 551

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:55:21.544311Z

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 965cc23c-d06b-49e0-a860-58a5fe0ec78a · outbound

This paper cites Neuropixels visual coding (dataset) 2019.

Distributional simplicity bias and effective convexity in Energy Based Models Neuropixels visual coding (dataset) 2019

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:55:21.540920Z

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 dd4ea5ac-a71d-43d3-8f7d-e48e47f87ba8 · outbound

This paper cites Cambridge University Press.

Distributional simplicity bias and effective convexity in Energy Based Models Cambridge University Press

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:55:21.523235Z

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 14f7622a-3b28-446f-ad6f-c4a7f204bfcb · outbound

This paper cites Mnist handwritten digit database.

Distributional simplicity bias and effective convexity in Energy Based Models Mnist handwritten digit database

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:55:21.574595Z

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 a5946b66-94d3-4c86-a461-a403246cb32d · outbound

This paper cites Fast training and sampling of restricted boltzmann machines.

Distributional simplicity bias and effective convexity in Energy Based Models Fast training and sampling of restricted boltzmann machines

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:25:53.746777Z

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 b01183e5-9e68-4c2c-bc3c-260adff5038a · outbound

This paper cites Training energy-based models with parallel trajectory tempering.

Distributional simplicity bias and effective convexity in Energy Based Models Training energy-based models with parallel trajectory tempering

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:55:21.533174Z

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.

Pith citing papers

Observation 2ffa2388-abad-4f65-9fed-e088d18c2db0 · inbound

Equilibrium Training of Energy-Based Models with Parallel Trajectory Tempering cites this paper.

Equilibrium Training of Energy-Based Models with Parallel Trajectory Tempering Distributional simplicity bias and effective convexity in Energy Based Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-30T11:53:54.585915Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.