Paper Citation Record · LEDGER

Adaptive kernel predictors from feature-learning infinite limits of neural networks

As of 9 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 6 inbound Pith citation observations for arXiv:2502.07998.

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

pith.paper-citation-record.v1
2502.07998 v2

Coverage vector

measured 65 of 65 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T11:19:07.049822Z

measured 71 of 71 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T07:44:14.864819Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

65 of 65 outbound references displayed

verified exact6
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External citation measurements

1
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation f11a5f33-ba69-4b94-835a-2fc200601510 · outbound

This paper cites High-dimensional dynamics of generalization error in neural networks.

Adaptive kernel predictors from feature-learning infinite limits of neural networks High-dimensional dynamics of generalization error in neural networks

Reference 1

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Observation f4cb9d6a-3ff9-4617-a5cc-247ba2cb966f · outbound

This paper cites Why bigger is not always better: on finite and infinite neural networks.

Adaptive kernel predictors from feature-learning infinite limits of neural networks Why bigger is not always better: on finite and infinite neural networks

Reference 2

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Observation 35945893-4b60-4f79-9cb2-14c30c390b53 · outbound

This paper cites Local kernel renormalization as a mechanism for feature learning in overparametrized convolutional neural networks.

Adaptive kernel predictors from feature-learning infinite limits of neural networks Local kernel renormalization as a mechanism for feature learning in overparametrized convolutional neural networks

Reference 3

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Observation 821a7bad-9ca0-4a91-be5d-768160597ff3 · outbound

This paper cites A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks.

Adaptive kernel predictors from feature-learning infinite limits of neural networks A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks

Reference 4

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Observation 6f8e108e-a3b1-4ba4-a092-7bfc8242e44d · outbound

This paper cites On Exact Computation with an Infinitely Wide Neural Net.

Adaptive kernel predictors from feature-learning infinite limits of neural networks On Exact Computation with an Infinitely Wide Neural Net

Reference 5

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Observation ef6f8416-55c0-42d9-8fe4-4849a3b08b31 · outbound

This paper cites Cugliandolo-Kurchan equations for dynamics of Spin-Glasses.

Adaptive kernel predictors from feature-learning infinite limits of neural networks Cugliandolo-Kurchan equations for dynamics of Spin-Glasses

Reference 6

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Observation 216b74cb-9053-4a67-8641-b8abc2a81482 · outbound

This paper cites Neural Networks as Kernel Learners: The Silent Alignment Effect.

Adaptive kernel predictors from feature-learning infinite limits of neural networks Neural Networks as Kernel Learners: The Silent Alignment Effect

Reference 7

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This paper cites Predictive power of a bayesian effective action for fully connected one hidden layer neural networks in the proportional limit.

Adaptive kernel predictors from feature-learning infinite limits of neural networks Predictive power of a bayesian effective action for fully connected one hidden layer neural networks in the proportional limit

Reference 8

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Observation b6869787-4dd8-4d19-9a28-96a123fc6bd4 · outbound

This paper cites Feature learning in finite-width Bayesian deep linear networks with multiple outputs and convolutional layers.

Adaptive kernel predictors from feature-learning infinite limits of neural networks Feature learning in finite-width Bayesian deep linear networks with multiple outputs and convolutional layers

Reference 9

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This paper cites Self-Consistent Dynamical Field Theory of Kernel Evolution in Wide Neural Networks.

Adaptive kernel predictors from feature-learning infinite limits of neural networks Self-Consistent Dynamical Field Theory of Kernel Evolution in Wide Neural Networks

Reference 10

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This paper cites and Pehlevan, C.

Adaptive kernel predictors from feature-learning infinite limits of neural networks and Pehlevan, C

Reference 11

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Observation 4c9bdd53-9dd8-435e-8a1a-1b8c506a928c · outbound

This paper cites How Feature Learning Can Improve Neural Scaling Laws.

Adaptive kernel predictors from feature-learning infinite limits of neural networks How Feature Learning Can Improve Neural Scaling Laws

Reference 12

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This paper cites J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., Vander P las, J., Wanderman- M ilne, S., and Zhang, Q.

Adaptive kernel predictors from feature-learning infinite limits of neural networks J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., Vander P las, J., Wanderman- M ilne, S., and Zhang, Q

Reference 13

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This paper cites On Lazy Training in Differentiable Programming.

Adaptive kernel predictors from feature-learning infinite limits of neural networks On Lazy Training in Differentiable Programming

Reference 14

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Adaptive kernel predictors from feature-learning infinite limits of neural networks and Saul, L

Reference 15

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Adaptive kernel predictors from feature-learning infinite limits of neural networks and Saul, L

Reference 16

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Observation 7985e8e9-24eb-4a4e-9e35-4c681148acdc · outbound

This paper cites Bayes-optimal learning of deep random networks of extensive-width.

Adaptive kernel predictors from feature-learning infinite limits of neural networks Bayes-optimal learning of deep random networks of extensive-width

Reference 17

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Observation 11b517b3-b2d4-455a-ae02-67ab74cf4157 · outbound

This paper cites Dynamics as a substitute for replicas in systems with quenched random impurities.

Adaptive kernel predictors from feature-learning infinite limits of neural networks Dynamics as a substitute for replicas in systems with quenched random impurities

Reference 18

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This paper cites Gaussian Process Behaviour in Wide Deep Neural Networks.

Adaptive kernel predictors from feature-learning infinite limits of neural networks Gaussian Process Behaviour in Wide Deep Neural Networks

Reference 19

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This paper cites Every Model Learned by Gradient Descent Is Approximately a Kernel Machine.

Adaptive kernel predictors from feature-learning infinite limits of neural networks Every Model Learned by Gradient Descent Is Approximately a Kernel Machine

Reference 20

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Adaptive kernel predictors from feature-learning infinite limits of neural networks Critical feature learning in deep neural networks

Reference 21

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Adaptive kernel predictors from feature-learning infinite limits of neural networks Disentangling feature and lazy training in deep neural networks

Reference 22

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Adaptive kernel predictors from feature-learning infinite limits of neural networks and Zlokapa, A

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Adaptive kernel predictors from feature-learning infinite limits of neural networks Deep Learning Scaling is Predictable, Empirically

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Adaptive kernel predictors from feature-learning infinite limits of neural networks Training Compute-Optimal Large Language Models

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Adaptive kernel predictors from feature-learning infinite limits of neural networks Statistical mechanics of transfer learning in fully connected networks in the proportional limit

Reference 26

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Adaptive kernel predictors from feature-learning infinite limits of neural networks Neural Tangent Kernel: Convergence and Generalization in Neural Networks

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Adaptive kernel predictors from feature-learning infinite limits of neural networks Saddle-to-Saddle Dynamics in Deep Linear Networks: Small Initialization Training, Symmetry, and Sparsity

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Adaptive kernel predictors from feature-learning infinite limits of neural networks Scaling Laws for Neural Language Models

Reference 29

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Adaptive kernel predictors from feature-learning infinite limits of neural networks Statistical physics of particles

Reference 30

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Adaptive kernel predictors from feature-learning infinite limits of neural networks Deep Neural Networks as Gaussian Processes

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Adaptive kernel predictors from feature-learning infinite limits of neural networks S., Bahri, Y., Novak, R., Sohl-Dickstein, J., and Pennington, J

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Adaptive kernel predictors from feature-learning infinite limits of neural networks and Gur-Ari, G

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Adaptive kernel predictors from feature-learning infinite limits of neural networks and Sompolinsky, H

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Adaptive kernel predictors from feature-learning infinite limits of neural networks and Sompolinsky, H

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Adaptive kernel predictors from feature-learning infinite limits of neural networks Emergence in non-neural models: grokking modular arithmetic via average gradient outer product

Reference 36

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Adaptive kernel predictors from feature-learning infinite limits of neural networks C., Siggia, E

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Adaptive kernel predictors from feature-learning infinite limits of neural networks A mean field view of the landscape of two-layer neural networks

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Adaptive kernel predictors from feature-learning infinite limits of neural networks Is SGD a Bayesian sampler? Well, almost

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Adaptive kernel predictors from feature-learning infinite limits of neural networks and Ringel, Z

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Adaptive kernel predictors from feature-learning infinite limits of neural networks Predicting the outputs of finite deep neural networks trained with noisy gradients

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Adaptive kernel predictors from feature-learning infinite limits of neural networks Unresolved cited work

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Adaptive kernel predictors from feature-learning infinite limits of neural networks Sensitivity and Generalization in Neural Networks: an Empirical Study

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Adaptive kernel predictors from feature-learning infinite limits of neural networks A statistical mechanics framework for bayesian deep neural networks beyond the infinite-width limit

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Adaptive kernel predictors from feature-learning infinite limits of neural networks Mechanism of feature learning in deep fully connected networks and kernel machines that recursively learn features

Reference 45

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Adaptive kernel predictors from feature-learning infinite limits of neural networks Unresolved cited work

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Adaptive kernel predictors from feature-learning infinite limits of neural networks A., Yaida, S., and Hanin, B

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Adaptive kernel predictors from feature-learning infinite limits of neural networks and Vanden-Eijnden, E

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Adaptive kernel predictors from feature-learning infinite limits of neural networks A unified approach to feature learning in bayesian neural networks

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Adaptive kernel predictors from feature-learning infinite limits of neural networks Grokking as a first order phase transition in two layer networks

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Adaptive kernel predictors from feature-learning infinite limits of neural networks Exact solutions to the nonlinear dynamics of learning in deep linear neural networks

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Adaptive kernel predictors from feature-learning infinite limits of neural networks and Smola, A

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Adaptive kernel predictors from feature-learning infinite limits of neural networks Separation of scales and a thermodynamic description of feature learning in some cnns

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Adaptive kernel predictors from feature-learning infinite limits of neural networks Order parameters and phase transitions of continual learning in deep neural networks

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Adaptive kernel predictors from feature-learning infinite limits of neural networks and Zippelius, A

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Adaptive kernel predictors from feature-learning infinite limits of neural networks Coding schemes in neural networks learning classification tasks

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Observation 8da687c9-9cfd-4ea6-a967-0d71f2b75040 · outbound

This paper cites Limitations of the NTK for Understanding Generalization in Deep Learning.

Adaptive kernel predictors from feature-learning infinite limits of neural networks Limitations of the NTK for Understanding Generalization in Deep Learning

Reference 57

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Observation 4f4726ba-6eac-46d5-b1c4-3c716a872113 · outbound

This paper cites Feature-Learning Networks Are Consistent Across Widths At Realistic Scales.

Adaptive kernel predictors from feature-learning infinite limits of neural networks Feature-Learning Networks Are Consistent Across Widths At Realistic Scales

Reference 58

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Observation 61ba1729-c35b-4a9c-9a77-1926a53d1574 · outbound

This paper cites More Than a Toy: Random Matrix Models Predict How Real-World Neural Representations Generalize.

Adaptive kernel predictors from feature-learning infinite limits of neural networks More Than a Toy: Random Matrix Models Predict How Real-World Neural Representations Generalize

Reference 59

Resolution: verified exact; local_arxiv, observed 2026-08-08T11:19:07.206339Z

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Observation 3f0426e9-5a73-44a1-951d-5e4cfe740c20 · outbound

This paper cites and Teh, Y.

Adaptive kernel predictors from feature-learning infinite limits of neural networks and Teh, Y

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:19:07.544336Z

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Adaptive kernel predictors from feature-learning infinite limits of neural networks X., Robeyns, M., Milsom, E., Anson, B., Schoots, N., and Aitchison, L

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:19:07.533673Z

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Observation ee08f366-21d2-4bfd-8f63-176e95ca622a · outbound

This paper cites Feature Learning in Infinite-Width Neural Networks.

Adaptive kernel predictors from feature-learning infinite limits of neural networks Feature Learning in Infinite-Width Neural Networks

Reference 62

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Observation f0f72734-4604-44e2-8874-80dae20de07e · outbound

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Adaptive kernel predictors from feature-learning infinite limits of neural networks Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer

Reference 63

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Adaptive kernel predictors from feature-learning infinite limits of neural networks A., Canatar, A., Ruben, B

Reference 64

Resolution: verified exact; doi, observed 2026-08-08T11:19:07.082812Z

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Observation cb786d73-1c23-419f-ba36-0b335a306d82 · outbound

This paper cites A., Tong, W.

Adaptive kernel predictors from feature-learning infinite limits of neural networks A., Tong, W

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:19:07.522724Z

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Pith citing papers

Observation 377dc5a1-bda8-438c-baa8-7845ddd15a42 · inbound

Statistical physics of deep learning: Optimal learning of a multi-layer perceptron near interpolation cites this paper.

Statistical physics of deep learning: Optimal learning of a multi-layer perceptron near interpolation Adaptive kernel predictors from feature-learning infinite limits of neural networks

Reference 73

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Observation a73e6885-3911-4ce0-92bd-2548f58b676c · inbound

Dynamics of neural scaling laws in random feature regression with powerlaw-distributed kernel eigenvalues cites this paper.

Dynamics of neural scaling laws in random feature regression with powerlaw-distributed kernel eigenvalues Adaptive kernel predictors from feature-learning infinite limits of neural networks

Reference 21

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Observation e02057ba-af11-4e84-8a97-ca13153679b9 · inbound

Discrete signaling mediates chaotic regularization in recurrent neural networks cites this paper.

Discrete signaling mediates chaotic regularization in recurrent neural networks Adaptive kernel predictors from feature-learning infinite limits of neural networks

Reference 60

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Observation afe6d8af-682c-4d61-a89e-c13e1beed33f · inbound

Statistical Properties of Training & Generalization cites this paper.

Statistical Properties of Training & Generalization Adaptive kernel predictors from feature-learning infinite limits of neural networks

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-06-26T15:39:33.210724Z

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Observation 1e08dc13-4114-4336-a807-af3c274e68fa · inbound

Statistical Properties of Training & Generalization cites this paper.

Statistical Properties of Training & Generalization Adaptive kernel predictors from feature-learning infinite limits of neural networks

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-07-02T21:57:25.361396Z

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Observation 5a8e3961-d350-466d-b79a-a55f4d0c37ce · inbound

Width-Robust Learnability in Mean-Field Bayesian Neural Networks cites this paper.

Width-Robust Learnability in Mean-Field Bayesian Neural Networks Adaptive kernel predictors from feature-learning infinite limits of neural networks

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-07-11T02:57:47.204069Z

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