Paper Citation Record · LEDGER

Impact of Batch Normalization on Convolutional Network Representations

As of 21 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2501.14441.

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

pith.paper-citation-record.v1
2501.14441 v2

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T15:12:50.561664Z

measured 36 of 36 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

36 of 36 outbound references displayed

verified exact1
verified fuzzy34
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 43c53e51-02ab-4437-9c22-66be181855fa · outbound

This paper cites Impact of batch normalization on convolutional network representations.

Impact of Batch Normalization on Convolutional Network Representations Impact of batch normalization on convolutional network representations

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:12:51.034193Z

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 39cfce17-f5bd-424c-a988-fd252c48d4a8 · outbound

This paper cites Understanding deep learning (still) requires rethinking generalization.

Impact of Batch Normalization on Convolutional Network Representations Understanding deep learning (still) requires rethinking generalization

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:12:51.022603Z

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 dc7f759d-ee1e-47e6-aee2-fb3b972a705a · outbound

This paper cites Sharpness-aware minimization for efficiently improving generalization.

Impact of Batch Normalization on Convolutional Network Representations Sharpness-aware minimization for efficiently improving generalization

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:12:51.011227Z

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 4c9d6ac5-c679-4eb1-9a17-e3b760ebde21 · outbound

This paper cites Algorithmic stability and generalization performance.

Impact of Batch Normalization on Convolutional Network Representations Algorithmic stability and generalization performance

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:12:51.001600Z

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 49784351-98a6-421a-b33a-dedfd68abe63 · outbound

This paper cites Input margins can predict generaliza- tion too.

Impact of Batch Normalization on Convolutional Network Representations Input margins can predict generaliza- tion too

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:12:50.990264Z

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 72560435-5796-4c6f-b299-6003ba9b3ce8 · outbound

This paper cites How does information bottleneck help deep learning? In Proceedings of the 40th International Conference on Machine Learning ICML, volume 202, 2023.

Impact of Batch Normalization on Convolutional Network Representations How does information bottleneck help deep learning? In Proceedings of the 40th International Conference on Machine Learning ICML, volume 202, 2023

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:12:50.981550Z

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 d022c8cc-f180-43cf-8962-287eccacd5ec · outbound

This paper cites Measuring generalization with optimal transport.

Impact of Batch Normalization on Convolutional Network Representations Measuring generalization with optimal transport

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:12:50.967180Z

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 7635084b-6928-4bc9-8f54-0da456c89d5e · outbound

This paper cites Predicting the generalization gap in deep networks with margin distributions.

Impact of Batch Normalization on Convolutional Network Representations Predicting the generalization gap in deep networks with margin distributions

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:12:50.955628Z

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 3454bd4b-794f-4ddc-8569-76cd1cfb4d6d · outbound

This paper cites Representation Based Complexity Measures for Predicting Generalization in Deep Learning.

Impact of Batch Normalization on Convolutional Network Representations Representation Based Complexity Measures for Predicting Generalization in Deep Learning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T15:12:50.475408Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3fdf858-8cee-44c9-a55c-c5d495689963 · outbound

This paper cites Glorot, A.

Impact of Batch Normalization on Convolutional Network Representations Glorot, A

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:12:50.945734Z

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 69322d75-581f-4040-813f-1379e92bb1d8 · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift.

Impact of Batch Normalization on Convolutional Network Representations Batch normalization: Accelerating deep network training by reducing internal covariate shift

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:12:50.936716Z

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 975a65ce-df53-460a-83c4-cdee116de30b · outbound

This paper cites An exponential learning rate schedule for deep learning.

Impact of Batch Normalization on Convolutional Network Representations An exponential learning rate schedule for deep learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:12:50.928460Z

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 91f8b32b-c823-4cae-8670-b0bda9cd4d4c · outbound

This paper cites How does batch normalization help optimization? Advances in Neural Information Processing Systems NeurIPS, 31, 2018.

Impact of Batch Normalization on Convolutional Network Representations How does batch normalization help optimization? Advances in Neural Information Processing Systems NeurIPS, 31, 2018

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:12:50.921112Z

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 1c163b3a-afdb-46a1-9dd3-1c7f21e0f338 · outbound

This paper cites Deep learning.

Impact of Batch Normalization on Convolutional Network Representations Deep learning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:12:50.912173Z

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 8dfb254f-1797-4261-b9fd-69259c113ca3 · outbound

This paper cites Relu and sigmoidal activation functions.

Impact of Batch Normalization on Convolutional Network Representations Relu and sigmoidal activation functions

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:12:50.902818Z

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 24bfdba2-7af8-403d-a6ab-8db52bd41015 · outbound

This paper cites Learning deep parsimonious representations.

Impact of Batch Normalization on Convolutional Network Representations Learning deep parsimonious representations

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:12:50.891518Z

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 3ba0bdd7-2484-4dcd-84c5-7590c3c84077 · outbound

This paper cites Understanding batch normalization.

Impact of Batch Normalization on Convolutional Network Representations Understanding batch normalization

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:12:50.880830Z

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 cde46d2a-6a79-4d26-8bd0-bfe740c1f02c · outbound

This paper cites Batch normalization increases adversarial vulnerability and decreases adversarial transferability: A non-robust feature perspective.

Impact of Batch Normalization on Convolutional Network Representations Batch normalization increases adversarial vulnerability and decreases adversarial transferability: A non-robust feature perspective

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:12:50.870849Z

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 0fdc5072-bcf0-4505-adcc-744444ce5bf4 · outbound

This paper cites Batch Normalization Explained.

Impact of Batch Normalization on Convolutional Network Representations Batch Normalization Explained

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-10T15:12:50.591693Z

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 7de0b18a-5395-40ef-bfb0-b6b9b2977615 · outbound

This paper cites Batch normalization orthogonalizes representations in deep random networks.

Impact of Batch Normalization on Convolutional Network Representations Batch normalization orthogonalizes representations in deep random networks

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:12:50.859788Z

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 3d1f58ec-52f0-48e3-86de-d6a8b72e9d20 · outbound

This paper cites Bengio, Aaron Courville, and Pascal Vincent.

Impact of Batch Normalization on Convolutional Network Representations Bengio, Aaron Courville, and Pascal Vincent

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:12:50.813622Z

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 738515ae-5113-41fe-8850-3ad748cade66 · outbound

This paper cites Some methods for classification and analysis of multivariate observations.

Impact of Batch Normalization on Convolutional Network Representations Some methods for classification and analysis of multivariate observations

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:12:50.803373Z

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 978e9ba8-17b6-4ea9-8b49-697cc8e685cb · outbound

This paper cites Hierarchical clustering, pages 195–211.

Impact of Batch Normalization on Convolutional Network Representations Hierarchical clustering, pages 195–211

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:12:50.795203Z

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 e4c7c225-3237-4c4f-880c-ca5c5775b9dd · outbound

This paper cites Normalized cuts and image segmentation.

Impact of Batch Normalization on Convolutional Network Representations Normalized cuts and image segmentation

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:12:50.782175Z

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 53e4af8f-3e86-4656-9874-2021ee5b7a2d · outbound

This paper cites Exploring layerwise decision making in dnns.Communications in Computer and Information Science CCIS, 1551, 2022.

Impact of Batch Normalization on Convolutional Network Representations Exploring layerwise decision making in dnns.Communications in Computer and Information Science CCIS, 1551, 2022

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:12:50.759524Z

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 784c5b3c-a913-4bcb-a3f1-2a1e33118dc1 · outbound

This paper cites Deep clustering for unsupervised learning of visual features.

Impact of Batch Normalization on Convolutional Network Representations Deep clustering for unsupervised learning of visual features

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:12:50.721482Z

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 6f19bfcb-8b5f-432e-8373-c18cb479ec10 · outbound

This paper cites K-means++: the advantages of careful seeding.

Impact of Batch Normalization on Convolutional Network Representations K-means++: the advantages of careful seeding

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:12:50.711266Z

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 e5093612-6ea5-48e3-af0f-159a19d9a999 · outbound

This paper cites A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters.

Impact of Batch Normalization on Convolutional Network Representations A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:12:50.701946Z

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 34cddb4a-a56a-4d7b-8240-015533a8f752 · outbound

This paper cites Algorithms for clustering data.

Impact of Batch Normalization on Convolutional Network Representations Algorithms for clustering data

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:12:50.693510Z

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 311573b5-fb6e-4b19-91b3-362708675c4e · outbound

This paper cites A cluster separation measure.

Impact of Batch Normalization on Convolutional Network Representations A cluster separation measure

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:12:50.684595Z

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 79546baa-550c-492a-a1e9-d071d0cd42b2 · outbound

This paper cites Online deep clustering for unsupervised representation learning.

Impact of Batch Normalization on Convolutional Network Representations Online deep clustering for unsupervised representation learning

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:12:50.669885Z

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 635ce8a3-3919-49c3-9a5d-53caa4041dbb · outbound

This paper cites Prototypical contrastive learning of unsupervised representations.

Impact of Batch Normalization on Convolutional Network Representations Prototypical contrastive learning of unsupervised representations

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:12:50.657547Z

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 a9038382-02b4-4bbc-b238-fe2069da8513 · outbound

This paper cites The MNIST database of handwritten digits.

Impact of Batch Normalization on Convolutional Network Representations The MNIST database of handwritten digits

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:12:50.646041Z

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 acae66ed-196c-4614-bb07-d025d288d506 · outbound

This paper cites Learning multiple layers of features from tiny images.

Impact of Batch Normalization on Convolutional Network Representations Learning multiple layers of features from tiny images

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:12:50.634958Z

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 bbdad81d-8304-498d-bd6b-12e1dd582ba4 · outbound

This paper cites Deep double descent: where bigger models and more data hurt.

Impact of Batch Normalization on Convolutional Network Representations Deep double descent: where bigger models and more data hurt

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:12:50.623208Z

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 3b251501-e50f-4058-8e5a-49ba4b542b55 · outbound

This paper cites Very deep convolutional networks for large-scale image recognition.

Impact of Batch Normalization on Convolutional Network Representations Very deep convolutional networks for large-scale image recognition

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:12:50.614440Z

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.