Paper Citation Record · LEDGER

Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training

As of 11 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2501.09659.

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

pith.paper-citation-record.v1
2501.09659 v2

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measured 48 of 48 reference resolution

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Reference resolution

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External citation measurements

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Outbound references

Observation 646dc63d-3d4e-4d99-9766-9ffe503b9bfb · outbound

This paper cites write newline.

Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training write newline

Reference 1

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Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training Machine learning in and out of equilibrium

Reference 2

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

Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training On Exact Computation with an Infinitely Wide Neural Net

Reference 3

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This paper cites Conditional Image Generation with Score-Based Diffusion Models.

Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training Conditional Image Generation with Score-Based Diffusion Models

Reference 4

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Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training and Pehlevan , C

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This paper cites Dynamics of Finite Width Kernel and Prediction Fluctuations in Mean Field Neural Networks.

Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training Dynamics of Finite Width Kernel and Prediction Fluctuations in Mean Field Neural Networks

Reference 6

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This paper cites Langevin algorithms for Markovian Neural Networks and Deep Stochastic control.

Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training Langevin algorithms for Markovian Neural Networks and Deep Stochastic control

Reference 7

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Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training Unresolved cited work

Reference 8

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This paper cites On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport.

Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport

Reference 9

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Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training and Rezchikov, S

Reference 10

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Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training and Rezchikov, S

Reference 11

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Observation 0840a566-fb68-45a5-969c-37884adc76fc · outbound

This paper cites Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next.

Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next

Reference 12

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This paper cites Stochastic Langevin Differential Inclusions with Applications to Machine Learning.

Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training Stochastic Langevin Differential Inclusions with Applications to Machine Learning

Reference 13

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Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training Tutorial on Variational Autoencoders

Reference 14

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Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training Renormalization in the neural network-quantum field theory correspondence

Reference 15

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Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training Disentangling feature and lazy training in deep neural networks

Reference 16

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This paper cites Scalable computation of dynamic flow problems via multi-marginal graph-structured optimal transport.

Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training Scalable computation of dynamic flow problems via multi-marginal graph-structured optimal transport

Reference 17

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Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training Neural networks and quantum field theory

Reference 18

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Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training Unresolved cited work

Reference 19

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Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training Denoising Diffusion Probabilistic Models

Reference 20

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Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training N., Klinger, M

Reference 21

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Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training Neural Tangent Kernel: Convergence and Generalization in Neural Networks

Reference 22

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Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training Dynamic scaling of growing interfaces

Reference 23

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Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training Physics-informed machine learning

Reference 24

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Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training Auto-Encoding Variational Bayes

Reference 25

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Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training Optimal Flow Matching: Learning Straight Trajectories in Just One Step

Reference 26

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Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training S., Bahri, Y., Novak, R., Sohl-Dickstein, J., and Pennington, J

Reference 28

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Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training Accelerating Convergence of Score-Based Diffusion Models, Provably

Reference 29

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Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training GenPhys: From Physical Processes to Generative Models

Reference 30

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Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training Designing optimal networks for multicommodity transport problem

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Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training PinnDE: Physics-Informed Neural Networks for Solving Differential Equations

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Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training A mean field view of the landscape of two-layer neural networks

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Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training Mean-field langevin dynamics and energy landscape of neural networks

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Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training and Bouchaud, J.-P

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Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training J., Mohamed, S., and Wierstra, D

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Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training and Vanden‐Eijnden, E

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Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training and Spiliopoulos, K

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Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training Maximum Likelihood Training of Score-Based Diffusion Models

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Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training Score-Based Generative Modeling through Stochastic Differential Equations

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Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training Small distance behavior in field theory and power counting

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Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training Improving and generalizing flow-based generative models with minibatch optimal transport

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Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training and Teh, Y

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Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training Feature Learning in Infinite-Width Neural Networks

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Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training Tensor Programs IIb: Architectural Universality of Neural Tangent Kernel Training Dynamics

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Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks

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Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training and Huang, H

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