Paper Citation Record · LEDGER

ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks

As of 22 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2411.16975.

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

pith.paper-citation-record.v1
2411.16975 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T12:47:08.114886Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

39 of 39 outbound references displayed

verified exact1
verified fuzzy17
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0f28f743-6ec7-4b4c-bbef-5ee777e7a335 · outbound

This paper cites Has artificial intelligence become alchemy?.

ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks Has artificial intelligence become alchemy?

Reference 1

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Observation 407bf9a1-9a69-41ab-be57-188ed5d30ef5 · outbound

This paper cites Hyper-Parameter Optimization: A Review of Algorithms and Applications.

ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks Hyper-Parameter Optimization: A Review of Algorithms and Applications

Reference 2

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Observation 4c08a42a-9656-4d2d-b1d2-144de66ce079 · outbound

This paper cites Practical recommendations for gradient-based training of deep architectures.

ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks Practical recommendations for gradient-based training of deep architectures

Reference 3

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Observation 8bd6898d-0fe4-4465-ad92-4069beb436c8 · outbound

This paper cites Some methods of speeding up the convergence of iteration methods,.

ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks Some methods of speeding up the convergence of iteration methods,

Reference 4

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This paper cites Increased rates of convergence through learning rate adaptation,.

ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks Increased rates of convergence through learning rate adaptation,

Reference 5

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Observation 81f67b16-8aed-4ad9-8368-af830a9ef030 · outbound

This paper cites Cyclical learning rates for training neural networks,.

ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks Cyclical learning rates for training neural networks,

Reference 6

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Observation c4c1ae0b-4cee-4bb5-83ac-2e2e806a8951 · outbound

This paper cites Adam: A method for stochastic optimization,.

ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks Adam: A method for stochastic optimization,

Reference 7

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Observation b8da6d46-4e2f-415c-81e5-f43511feadd1 · outbound

This paper cites A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay.

ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay

Reference 8

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Observation 1e9a7477-a92d-48bc-b0fc-9533cff1b65e · outbound

This paper cites Adam: A Method for Stochastic Optimization.

ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks Adam: A Method for Stochastic Optimization

Reference 9

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Observation 031e90a6-9aa5-4f22-b5b1-627a090e29a4 · outbound

This paper cites Six Lectures on Linearized Neural Networks.

ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks Six Lectures on Linearized Neural Networks

Reference 10

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Observation 60846d31-4815-47c8-a723-a004a825840e · outbound

This paper cites The shape of learning curves: A review,.

ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks The shape of learning curves: A review,

Reference 11

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Observation 75d8c0d0-9821-4cf7-ae0d-15e112f6aa96 · outbound

This paper cites What can linearized neural networks actually say about generalization?.

ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks What can linearized neural networks actually say about generalization?

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:47:08.673339Z

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Observation 9a1b29c6-e905-43b4-aa21-46da0646aafd · outbound

This paper cites Neural tangent kernel: Convergence and generalization in neural networks,.

ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks Neural tangent kernel: Convergence and generalization in neural networks,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:47:08.685130Z

Source-reported events for the cited work

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Observation c7c10acd-bc06-4084-86d6-19a137eeb7ca · outbound

This paper cites Incremental pid controller-based learning rate scheduler for stochastic gradient descent,.

ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks Incremental pid controller-based learning rate scheduler for stochastic gradient descent,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:47:08.649140Z

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Observation e913d2f6-beaf-423b-81d7-ab83ffc06f44 · outbound

This paper cites Wide neural networks of any depth evolve as linear models under gradient descent,.

ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks Wide neural networks of any depth evolve as linear models under gradient descent,

Reference 15

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Observation bf94e6e0-ea51-4a14-8ff8-7f13d4986963 · outbound

This paper cites Distribution of the largest eigenvalue for real wishart and gaussian random matrices and a simple approximation for the tracy–widom distribution,.

ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks Distribution of the largest eigenvalue for real wishart and gaussian random matrices and a simple approximation for the tracy–widom distribution,

Reference 16

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Observation 43cf6485-8b44-46ff-a0f8-3f36e8a01568 · outbound

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ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks Unresolved cited work

Reference 17

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Source-reported events for the cited work

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Observation bc779744-8424-470b-b98e-afe19be35b99 · outbound

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

ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks

Reference 18

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Observation b93c998f-a977-487c-b7a3-7b68f94bdff1 · outbound

This paper cites Maximal initial learning rates in deep relu networks,.

ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks Maximal initial learning rates in deep relu networks,

Reference 19

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Observation 7721a42c-8f4b-46dd-b755-247b0bb848a7 · outbound

This paper cites ADADELTA: An Adaptive Learning Rate Method.

ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks ADADELTA: An Adaptive Learning Rate Method

Reference 20

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Observation 692432f8-f454-42e6-9b67-7d6bdd9339e5 · outbound

This paper cites Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude,.

ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude,

Reference 21

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Observation e8c2b2d1-510e-410a-8d2d-f66b1b181e71 · outbound

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ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks Array programming with NumPy,

Reference 22

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ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks Van Rossum and F

Reference 23

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Observation 5705dba9-3458-4f9d-8320-8a5ca4368736 · outbound

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ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks Scikit-learn: Machine learning in Python,

Reference 24

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ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python,

Reference 25

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ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks Matplotlib: A 2d graphics environment,

Reference 26

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ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks Paszke, S

Reference 27

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ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks Sparse spatial autoregressions,

Reference 28

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Observation 752982ca-59b3-43b5-bf49-8bf3acc08716 · outbound

This paper cites The mnist database of handwritten digit images for machine learning research [best of the web],.

ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks The mnist database of handwritten digit images for machine learning research [best of the web],

Reference 29

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Observation a75c6c04-31f8-412c-b783-d206a1b2a4e0 · outbound

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ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks Learning multiple layers of features from tiny images,

Reference 30

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ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks The marginal value of adaptive gradient methods in machine learning,

Reference 31

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ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks Implicit Regularization in Deep Learning

Reference 32

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ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks Very deep convolutional networks for large-scale image recognition

Reference 33

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Observation db1d0b37-6831-4f1c-886d-0f482b5836e2 · outbound

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ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks Implicit gradient regularization,

Reference 34

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Observation 97e0294e-b227-487a-8e40-85a14f0b5c13 · outbound

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ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks Dropout: A simple way to prevent neural networks from overfitting,

Reference 35

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This paper cites Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning.

ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning

Reference 36

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This paper cites Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.

ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 39

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This paper cites Available: http://dblp.uni-trier.de/db/conf/iclr/iclr2015.

ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks Available: http://dblp.uni-trier.de/db/conf/iclr/iclr2015

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:47:08.496489Z

Source-reported events for the cited work

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Observation c5317c77-8249-4e8d-8b02-c2a2ea61b0f2 · outbound

This paper cites Cyclical Learning Rates for Training Neural Networks.

ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks Cyclical Learning Rates for Training Neural Networks

Reference 2017

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Source-reported events for the cited work

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Pith citing papers

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