Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-09T21:15:03.309662Z
Paper Citation Record · LEDGER
A theoretical framework for overfitting in energy-based modeling
As of 22 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 0 inbound Pith citation observations for arXiv:2501.19158.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
61 of 61 outbound references displayed
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External citation measurements
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Outbound references
Observation 2b672503-da1d-4bce-aef1-4ab0392e6069 · outbound
This paper cites H., Hinton, G.
A theoretical framework for overfitting in energy-based modeling H., Hinton, G
Reference 1
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Observation b556da5f-e1af-49f1-b57a-580da7431b93 · outbound
This paper cites S., Saxe, A.
A theoretical framework for overfitting in energy-based modeling S., Saxe, A
Reference 2
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Observation 943cfd0d-e04a-4ea0-9cd4-11f817d304e6 · outbound
This paper cites Explaining the effects of non-convergent MCMC in the training of energy-based models.
A theoretical framework for overfitting in energy-based modeling Explaining the effects of non-convergent MCMC in the training of energy-based models
Reference 3
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Observation 111f2c0a-7a55-40f9-b86e-fafca3c2996b · outbound
This paper cites From high-dimensional & mean-field dynamics to dimensionless odes: A unifying approach to sgd in two-layers networks.
A theoretical framework for overfitting in energy-based modeling From high-dimensional & mean-field dynamics to dimensionless odes: A unifying approach to sgd in two-layers networks
Reference 4
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This paper cites Scaling and renormalization in high-dimensional regression.
A theoretical framework for overfitting in energy-based modeling Scaling and renormalization in high-dimensional regression
Reference 5
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This paper cites and Silverstein, J.
A theoretical framework for overfitting in energy-based modeling and Silverstein, J
Reference 6
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Observation 02326593-68ee-46c6-8ba0-2cf2bb538ae4 · outbound
This paper cites Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation.
A theoretical framework for overfitting in energy-based modeling Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation
Reference 7
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Observation 63213ae9-1ed0-4ecf-bf6d-fabd48bbf52d · outbound
This paper cites To understand deep learning we need to understand kernel learning.
A theoretical framework for overfitting in energy-based modeling To understand deep learning we need to understand kernel learning
Reference 8
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This paper cites K., Houkpati, Y., Irungu, J., and Oladunni, T.
A theoretical framework for overfitting in energy-based modeling K., Houkpati, Y., Irungu, J., and Oladunni, T
Reference 9
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Observation 046de63d-6bfd-43aa-bf4a-e0ee510f3ebf · outbound
This paper cites Learning a Restricted Boltzmann Machine using biased Monte Carlo sampling.
A theoretical framework for overfitting in energy-based modeling Learning a Restricted Boltzmann Machine using biased Monte Carlo sampling
Reference 10
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This paper cites Fast, accurate training and sampling of restricted B oltzmann machines.
A theoretical framework for overfitting in energy-based modeling Fast, accurate training and sampling of restricted B oltzmann machines
Reference 11
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Observation df32c8bd-ce3e-40e9-bbdd-89d62e4223b8 · outbound
This paper cites Cleaning large correlation matrices: Tools from random matrix theory.
A theoretical framework for overfitting in energy-based modeling Cleaning large correlation matrices: Tools from random matrix theory
Reference 12
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This paper cites Overlaps between eigenvectors of correlated random matrices.
A theoretical framework for overfitting in energy-based modeling Overlaps between eigenvectors of correlated random matrices
Reference 13
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A theoretical framework for overfitting in energy-based modeling On lazy training in differentiable programming
Reference 14
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Observation 6a46a5b8-135d-4591-b7ee-a38152725423 · outbound
This paper cites Inverse statistical physics of protein sequences: a key issues review.
A theoretical framework for overfitting in energy-based modeling Inverse statistical physics of protein sequences: a key issues review
Reference 15
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A theoretical framework for overfitting in energy-based modeling Unresolved cited work
Reference 16
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Observation 99dca2f3-1bb4-46fe-8b7f-8fc18cccf49e · outbound
This paper cites Thermodynamics of restricted B oltzmann machines and related learning dynamics.
A theoretical framework for overfitting in energy-based modeling Thermodynamics of restricted B oltzmann machines and related learning dynamics
Reference 17
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This paper cites Unsupervised hierarchical clustering using the learning dynamics of restricted B oltzmann machines.
A theoretical framework for overfitting in energy-based modeling Unsupervised hierarchical clustering using the learning dynamics of restricted B oltzmann machines
Reference 18
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A theoretical framework for overfitting in energy-based modeling Unresolved cited work
Reference 19
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This paper cites Inferring high-order couplings with neural networks.
A theoretical framework for overfitting in energy-based modeling Inferring high-order couplings with neural networks
Reference 20
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A theoretical framework for overfitting in energy-based modeling Gromov–wasserstein distances between gaussian distributions
Reference 21
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A theoretical framework for overfitting in energy-based modeling The mnist database of handwritten digit images for machine learning research
Reference 22
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A theoretical framework for overfitting in energy-based modeling and Mordatch, I
Reference 23
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A theoretical framework for overfitting in energy-based modeling Optimal regularizations for data generation with probabilistic graphical models
Reference 24
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A theoretical framework for overfitting in energy-based modeling and Lucibello, C
Reference 25
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Observation 65e30918-b676-4a15-84ba-de474ef58288 · outbound
This paper cites Interpretable pairwise distillations for generative protein sequence models.
A theoretical framework for overfitting in energy-based modeling Interpretable pairwise distillations for generative protein sequence models
Reference 26
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A theoretical framework for overfitting in energy-based modeling Free dynamics of feature learning processes
Reference 27
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A theoretical framework for overfitting in energy-based modeling Generalized cross-validation as a method for choosing a good ridge parameter
Reference 28
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Observation 0bbf7a97-7a39-4247-97c8-2bc81c27016d · outbound
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A theoretical framework for overfitting in energy-based modeling Deterministic equivalents for certain functionals of large random matrices
Reference 29
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A theoretical framework for overfitting in energy-based modeling Surprises in high-dimensional ridgeless least squares interpolation
Reference 30
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A theoretical framework for overfitting in energy-based modeling and Dayan, P
Reference 31
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A theoretical framework for overfitting in energy-based modeling Neural tangent kernel: Convergence and generalization in neural networks
Reference 32
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A theoretical framework for overfitting in energy-based modeling Unresolved cited work
Reference 33
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A theoretical framework for overfitting in energy-based modeling Estimation of quenched random fields in the inverse ising problem using a diagonal matching method
Reference 34
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A theoretical framework for overfitting in energy-based modeling Learning multiple layers of features from tiny images
Reference 35
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A theoretical framework for overfitting in energy-based modeling and P \'e ch \'e , S
Reference 36
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A theoretical framework for overfitting in energy-based modeling and Wolf, M
Reference 37
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A theoretical framework for overfitting in energy-based modeling and Wolf, M
Reference 38
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A theoretical framework for overfitting in energy-based modeling Restoring balance: principled under/oversampling of data for optimal classification
Reference 39
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Reference 40
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A theoretical framework for overfitting in energy-based modeling A large scale analysis of logistic regression: Asymptotic performance and new insights
Reference 42
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A theoretical framework for overfitting in energy-based modeling and Pastur, L
Reference 43
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A theoretical framework for overfitting in energy-based modeling A mean field view of the landscape of two-layer neural networks
Reference 44
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A theoretical framework for overfitting in energy-based modeling S., Sander, C., Zecchina, R., Onuchic, J
Reference 45
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A theoretical framework for overfitting in energy-based modeling C., Zecchina, R., and Berg, J
Reference 46
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A theoretical framework for overfitting in energy-based modeling Failures and successes of cross-validation for early-stopped gradient descent
Reference 47
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A theoretical framework for overfitting in energy-based modeling and Bouchaud, J.-P
Reference 48
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Observation d264fbbb-7983-4a5c-b933-7be6d6265140 · outbound
This paper cites On the spectral bias of neural networks.
A theoretical framework for overfitting in energy-based modeling On the spectral bias of neural networks
Reference 49
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Observation 2ad264cf-c027-45d5-b9b1-a64e24e30556 · outbound
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A theoretical framework for overfitting in energy-based modeling The bethe approximation for solving the inverse I sing problem: a comparison with other inference methods
Reference 50
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Reference 51
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Observation 694a7eba-f647-4099-8690-6f1d61b96e1d · outbound
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A theoretical framework for overfitting in energy-based modeling and Solla, S
Reference 52
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Observation 97a70c0c-f0c5-4120-9e30-ab9bba8ab35e · outbound
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A theoretical framework for overfitting in energy-based modeling M., McClelland, J
Reference 53
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Observation 51016a6b-65ce-456e-b11b-04c63713ded5 · outbound
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A theoretical framework for overfitting in energy-based modeling and Kubo, R
Reference 54
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A theoretical framework for overfitting in energy-based modeling M., Sclocchi, A., and Wyart, M
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T21:15:04.216236Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 1731ee77-54c5-4783-8624-31d7ec5a90b0 · outbound
This paper cites Learning protein constitutive motifs from sequence data.
A theoretical framework for overfitting in energy-based modeling Learning protein constitutive motifs from sequence data
Reference 56
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T21:15:03.284462Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e1b9be8a-c3dc-44a9-b3e0-8b53697e4742 · outbound
This paper cites More than a toy: Random matrix models predict how real-world neural representations generalize.
A theoretical framework for overfitting in energy-based modeling More than a toy: Random matrix models predict how real-world neural representations generalize
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T21:15:04.190373Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 1924a006-d8a7-44f2-bf34-3f5786b8ea98 · outbound
This paper cites E., Arnold, F.
A theoretical framework for overfitting in energy-based modeling E., Arnold, F
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T21:15:04.174892Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 31b95b07-39a9-4f6e-9cee-b4798ab27c56 · outbound
This paper cites and Tanaka, K.
A theoretical framework for overfitting in energy-based modeling and Tanaka, K
Reference 59
- Resolution
- verified exact
- doi, observed 2026-08-09T21:15:03.349937Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 27ee8c55-6d06-44cc-8aae-1da71e0641b9 · outbound
This paper cites Creating artificial human genomes using generative neural networks.
A theoretical framework for overfitting in energy-based modeling Creating artificial human genomes using generative neural networks
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T21:15:04.159440Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation a2cdf080-505b-4579-85b3-2aa4b04111f0 · outbound
This paper cites L., Szatkownik, A., Furtlehner, C., Charpiat, G., and Jay, F.
A theoretical framework for overfitting in energy-based modeling L., Szatkownik, A., Furtlehner, C., Charpiat, G., and Jay, F
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T21:15:04.143201Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Pith citing papers
No inbound Pith citation observations are available.