Paper Citation Record · LEDGER

Training NTK to Generalize with KARE

As of 20 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2505.11347.

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

pith.paper-citation-record.v1
2505.11347 v2

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:59:14.621322Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

18 of 18 outbound references displayed

verified exact0
verified fuzzy10
unresolved8
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metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 54b713fb-c200-40ce-98c4-df117f642e1b · outbound

This paper cites Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks.

Training NTK to Generalize with KARE Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T20:59:14.522120Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 109571fd-4425-47bc-9c53-ad762ab37a71 · outbound

This paper cites Smoothing noisy data with spline functions: estimating the cor- rect degree of smoothing by the method of generalized cross-validation,.

Training NTK to Generalize with KARE Smoothing noisy data with spline functions: estimating the cor- rect degree of smoothing by the method of generalized cross-validation,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:59:15.033535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3dd28630-17dd-45e8-9a59-b1ba199c1267 · outbound

This paper cites Ker- nel alignment risk estimator: Risk prediction from training data,.

Training NTK to Generalize with KARE Ker- nel alignment risk estimator: Risk prediction from training data,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:59:14.986807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 75fe4303-d842-4c81-93a0-60992ed131cb · outbound

This paper cites A non-asymptotic theory of Kernel Ridge Regression: deterministic equivalents, test error, and GCV estimator.

Training NTK to Generalize with KARE A non-asymptotic theory of Kernel Ridge Regression: deterministic equivalents, test error, and GCV estimator

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T20:59:14.589412Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1d9903f-9fa4-4e3c-b8d9-beb9e4ae4cdb · outbound

This paper cites More is Better in Modern Machine Learning: when Infinite Overparameterization is Optimal and Overfitting is Obligatory.

Training NTK to Generalize with KARE More is Better in Modern Machine Learning: when Infinite Overparameterization is Optimal and Overfitting is Obligatory

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T20:59:14.600954Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ccbe9f5-5439-4fed-a6d7-2a56f560714f · outbound

This paper cites For datasets with fewer than 100 observations, we standardize the data using RobustScaler() from sklearn.

Training NTK to Generalize with KARE For datasets with fewer than 100 observations, we standardize the data using RobustScaler() from sklearn

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:59:14.894508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 08b3f3b7-1f65-428c-b20d-7314c9d074cd · outbound

This paper cites A.2 Details For the MNIST Dataset.

Training NTK to Generalize with KARE A.2 Details For the MNIST Dataset

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:59:14.910284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7e70f9b2-adb2-4fb9-81ea-d838ce9aed72 · outbound

This paper cites Gradient descent finds global minima of deep neural networks,.

Training NTK to Generalize with KARE Gradient descent finds global minima of deep neural networks,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:59:15.001702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3f9c8025-0c31-44c5-9f4a-18e9cded01c8 · outbound

This paper cites Toward deeper understanding of neural networks: The power of initialization and a dual view on expressivity,.

Training NTK to Generalize with KARE Toward deeper understanding of neural networks: The power of initialization and a dual view on expressivity,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:59:15.017782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f3c1814f-c73c-4136-b25a-0a878640e987 · outbound

This paper cites Adaptive kernel predictors from feature-learning infinite limits of neural networks.

Training NTK to Generalize with KARE Adaptive kernel predictors from feature-learning infinite limits of neural networks

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T20:59:14.572606Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86758e25-3e42-48b0-85e3-167513aab967 · outbound

This paper cites Diving into the shallows: a computational perspective on large- scale shallow learning,.

Training NTK to Generalize with KARE Diving into the shallows: a computational perspective on large- scale shallow learning,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:59:14.954854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d97925e8-f7aa-459a-bf43-3824cd2ce676 · outbound

This paper cites Surprises in High-Dimensional Ridgeless Least Squares Interpolation.

Training NTK to Generalize with KARE Surprises in High-Dimensional Ridgeless Least Squares Interpolation

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-15T20:59:14.560837Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14009e15-5dd0-4d55-8939-d535ddeb3f8f · outbound

This paper cites On Lazy Training in Differentiable Programming.

Training NTK to Generalize with KARE On Lazy Training in Differentiable Programming

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-15T20:59:14.535822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b99484f-94d0-4f61-b1f8-a342c256831c · outbound

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

Training NTK to Generalize with KARE Neural Networks as Kernel Learners: The Silent Alignment Effect

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-15T20:59:14.528968Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6261556c-0289-4fa3-9e6a-789f6a53cc78 · outbound

This paper cites More than a toy: Random matrix models predict how real-world neural representations generalize,.

Training NTK to Generalize with KARE More than a toy: Random matrix models predict how real-world neural representations generalize,

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:59:14.925084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 544d55d1-a440-4509-9a2b-d77b70c25011 · outbound

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

Training NTK to Generalize with KARE Limitations of the NTK for Understanding Generalization in Deep Learning

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-15T20:59:14.606693Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb5f46b9-41e6-44ed-aba5-623066c53ae7 · outbound

This paper cites Uniform consistency of cross-validation estimators for high-dimensional ridge regression,.

Training NTK to Generalize with KARE Uniform consistency of cross-validation estimators for high-dimensional ridge regression,

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:59:14.940136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 81fee9a2-5b6e-4b01-b0be-8b0e48e6442a · outbound

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

Training NTK to Generalize with KARE Wide neural networks of any depth evolve as linear models under gradient descent,

Reference 2025

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:59:14.971025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

No inbound Pith citation observations are available.