Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks | Paper Citation Record

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This paper cites Priors for infinite networks,.

Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Priors for infinite networks,

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This paper cites Introduction to gaussian processes,.

Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Introduction to gaussian processes,

Reference 2

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Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Computing with infinite networks,

Reference 3

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Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Gaussian process behaviour in wide deep neural networks,

Reference 4

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Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Deep neural networks as gaussian processes,

Reference 5

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Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks On exact computation with an infinitely wide neural net,

Reference 6

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Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks On lazy training in differentiable programming,

Reference 7

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Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Why bigger is not always better: on finite and infinite neural networks,

Reference 8

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Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Quantitative CLTs in Deep Neural Networks

Reference 9

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This paper cites Deep convolutional networks as shallow gaussian processes,.

Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Deep convolutional networks as shallow gaussian processes,

Reference 10

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Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Bayesian deep convolutional networks with many channels are gaussian processes,

Reference 11

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Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Tensor programs i: Wide feedforward or recurrent neural networks of any architecture are gaussian processes,

Reference 12

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This paper cites Feature Learning in Deep Neural Networks - Studies on Speech Recognition Tasks.

Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Feature Learning in Deep Neural Networks - Studies on Speech Recognition Tasks

Reference 13

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This paper cites Learning sparse features can lead to overfitting in neural networks,.

Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Learning sparse features can lead to overfitting in neural networks,

Reference 14

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Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Inversion dynamics of class manifolds in deep learning reveals tradeoffs underlying generalization,

Reference 15

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Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Spring-block theory of feature learning in deep neural networks

Reference 16

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Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks A mean field view of the landscape of two- layer neural networks,

Reference 17

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This paper cites On the global convergence of gradient descent for over-parameterized models using optimal transport,.

Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks On the global convergence of gradient descent for over-parameterized models using optimal transport,

Reference 18

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Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Mean field analysis of neural networks: A law of large numbers,

Reference 19

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Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Parameters as interacting particles: long time convergence and asymptotic error scaling of neural networks,

Reference 20

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Observation 01e519de-f876-4490-a67e-c5120c6f3b41 · outbound

This paper cites Tensor programs iv: Feature learning in infinite-width neural networks,.

Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Tensor programs iv: Feature learning in infinite-width neural networks,

Reference 21

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Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks What can be learnt with wide convolutional neural networks?

Reference 22

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Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Locality defeats the curse of dimensionality in convolutional teacher-student scenarios,

Reference 23

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Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks A theory of representation learning gives a deep generalisation of kernel methods

Reference 24

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Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Statistical mechanics of deep linear neural networks: The backpropagating kernel renormalization,

Reference 25

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Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks A statistical mechanics framework for bayesian deep neural networks beyond the infinite-width limit,

Reference 26

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Observation a7d166d9-4138-4daf-b1f0-8d391f82bb7b · outbound

This paper cites Local Kernel Renormalization as a mechanism for feature learning in overparametrized Convolutional Neural Networks.

Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Local Kernel Renormalization as a mechanism for feature learning in overparametrized Convolutional Neural Networks

Reference 27

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This paper cites Statistical mechanics of transfer learning in fully-connected networks in the proportional limit.

Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Statistical mechanics of transfer learning in fully-connected networks in the proportional limit

Reference 28

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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,.

Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Predictive power of a bayesian effective action for fully connected one hidden layer neural networks in the proportional limit,

Reference 29

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Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Critical feature learning in deep neural networks,

Reference 30

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Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks High-dimensional learning of narrow neural networks

Reference 31

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Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Fundamental limits of overparametrized shallow neural networks for supervised learning

Reference 32

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Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Dissecting the Interplay of Attention Paths in a Statistical Mechanics Theory of Transformers

Reference 33

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Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Adam: A Method for Stochastic Optimization

Reference 34

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Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Tune: A Research Platform for Distributed Model Selection and Training

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

Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Feature learning in finite-width Bayesian deep linear networks with multiple outputs and convolutional layers

Reference 36

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This paper cites The generalization error of random features regression: Precise asymptotics and the double descent curve,.

Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks The generalization error of random features regression: Precise asymptotics and the double descent curve,

Reference 37

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Observation f0c855eb-c656-43f8-b9da-0d65013af849 · outbound

This paper cites Generalisation error in learning with random features and the hidden manifold model,.

Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Generalisation error in learning with random features and the hidden manifold model,

Reference 38

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Observation c72997ee-2105-49ac-9010-9f3a5a0509f8 · outbound

This paper cites Random features and polynomial rules.

Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Random features and polynomial rules

Reference 39

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Observation d26300a7-0779-4d6a-9cc6-1f92926a2daa · outbound

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Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Universal mean-field upper bound for the generalization gap of deep neural networks,

Reference 40

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Observation ffc01b2a-2be9-4a22-9421-892aa3d80ad8 · outbound

This paper cites Central limit theorems for non-linear functionals of gaussian fields,.

Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Central limit theorems for non-linear functionals of gaussian fields,

Reference 41

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Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Quantitative Breuer-Major theorems,

Reference 42

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Observation fd3ea558-cd03-42ce-8a4c-88278c421940 · outbound

This paper cites A smaller subset of 10 easily classified classes from imagenet,.

Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks A smaller subset of 10 easily classified classes from imagenet,

Reference 43

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Observation 79a4267b-8d72-489a-b188-c8ec90c70521 · outbound

This paper cites Finite versus infinite neural networks: an empirical study,.

Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Finite versus infinite neural networks: an empirical study,

Reference 44

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Observation fe45e1c5-f643-4e06-9de3-8c4f26659121 · outbound

This paper cites Representations and generalization in artificial and brain neural networks,.

Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Representations and generalization in artificial and brain neural networks,

Reference 45

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Observation 5c9c13a8-0399-4341-9866-0d5780440eb2 · outbound

This paper cites A self consistent theory of gaussian processes captures feature learning effects in finite cnns,.

Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks A self consistent theory of gaussian processes captures feature learning effects in finite cnns,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:07:56.353517Z

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Observation 68f6db5c-48f2-4f22-86d9-31848cfbeafd · outbound

This paper cites Separation of scales and a thermodynamic description of feature learning in some cnns,.

Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Separation of scales and a thermodynamic description of feature learning in some cnns,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:07:56.337059Z

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Observation 93717ad5-c0c6-4cf6-9d7c-20dda17bc3a6 · outbound

This paper cites Bayesian Inference with Deep Weakly Nonlinear Networks.

Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Bayesian Inference with Deep Weakly Nonlinear Networks

Reference 48

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Observation 365194d3-29d7-4390-9a97-e69e5051d9f3 · outbound

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Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Data- driven emergence of convolutional structure in neural networks,

Reference 49

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Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Moment bounds and central limit theorems for gaussian subordinated arrays,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:07:56.319275Z

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Observation e1bb4951-c352-4d1e-bac2-08aebff7f7ba · outbound

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Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Multivariate normal approximation using stein’s method and malliavin calculus,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:07:56.302546Z

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Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks An integral which occurs in statistics,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:07:56.278821Z

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Observation 0598f304-1572-4068-bae5-7277baf17464 · outbound

This paper cites Kernel shape renormalization in bayesian shallow networks: a gaussian process perspective,.

Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Kernel shape renormalization in bayesian shallow networks: a gaussian process perspective,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:07:56.258771Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 633ff120-b92f-4579-90a0-fc005c27eddc · outbound

This paper cites an unresolved cited work.

Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-08-11T11:07:56.227391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 097e7d5b-b09d-468a-bc0a-1a419c1e5e30 · outbound

This paper cites Heermann, Monte Carlo Simulation in Statistical Physics (Springer Berlin Heidelberg, 2002).

Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Heermann, Monte Carlo Simulation in Statistical Physics (Springer Berlin Heidelberg, 2002)

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:07:56.213099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 01cd80e3-8fff-4e4c-96c4-a6adc4d5c4a4 · outbound

This paper cites Monte carlo errors with less errors,.

Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Monte carlo errors with less errors,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:07:56.198556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 735172a9-be5b-4ca7-af9e-6d4f194213d0 · outbound

This paper cites EfficientNet: Rethinking model scaling for convolutional neural networks,.

Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks EfficientNet: Rethinking model scaling for convolutional neural networks,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:07:56.178802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6d13d31d-5eef-4ddd-bbb6-5a9d0814243f · outbound

This paper cites Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden layer networks.

Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden layer networks

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:07:56.164275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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