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Paper Citation Record · LEDGER

Neural Networks Learn Distance Metrics

As of 13 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2502.02103.

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

pith.paper-citation-record.v1
2502.02103 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T13:24:23.011431Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

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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

40 of 40 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ec66be85-a52c-4853-978b-de8508574484 · outbound

This paper cites Boolean functions and artificial neural networks.

Neural Networks Learn Distance Metrics Boolean functions and artificial neural networks

Reference 1

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Observation 6ace319f-96af-4b32-b03d-d8bc2b1971c3 · outbound

This paper cites Representation learning: A review and new perspectives.

Neural Networks Learn Distance Metrics Representation learning: A review and new perspectives

Reference 2

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Observation 4aa5339e-52da-4cd0-b6c8-959734c86152 · outbound

This paper cites Signature verification using a siamese time delay neural network.

Neural Networks Learn Distance Metrics Signature verification using a siamese time delay neural network

Reference 3

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Observation 2419d5ab-d0b5-4ddb-aada-e221cded6b04 · outbound

This paper cites Radial basis functions, multi-variable functional interpolation and adaptive networks.

Neural Networks Learn Distance Metrics Radial basis functions, multi-variable functional interpolation and adaptive networks

Reference 4

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Observation 5ba03938-3932-4575-a3df-b2430309c46b · outbound

This paper cites A simple framework for contrastive learning of visual representations.

Neural Networks Learn Distance Metrics A simple framework for contrastive learning of visual representations

Reference 5

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Observation 7aa312da-404a-4a08-98a2-a5eb66dd031b · outbound

This paper cites Reducing Overfitting in Deep Networks by Decorrelating Representations.

Neural Networks Learn Distance Metrics Reducing Overfitting in Deep Networks by Decorrelating Representations

Reference 6

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Observation 2cdd39af-b658-4566-b41a-eb6d155fe2ac · outbound

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

Neural Networks Learn Distance Metrics The mnist database of handwritten digit images for machine learning research [best of the web]

Reference 7

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Source-reported events for the cited work

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Observation 60e50d94-024a-49fc-9939-837263385acd · outbound

This paper cites High-dimensional data analysis: The curses and blessings of dimensionality.

Neural Networks Learn Distance Metrics High-dimensional data analysis: The curses and blessings of dimensionality

Reference 8

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Source-reported events for the cited work

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Observation 89efaf36-fbe6-49bf-b3a9-891d5dfebca8 · outbound

This paper cites Visualizing higher-layer features of a deep network.

Neural Networks Learn Distance Metrics Visualizing higher-layer features of a deep network

Reference 9

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Observation e1fbeaa9-2b3b-4a84-87c0-f5e1f4d871ad · outbound

This paper cites Deep sparse rectifier neural networks.

Neural Networks Learn Distance Metrics Deep sparse rectifier neural networks

Reference 10

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Source-reported events for the cited work

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Observation 2884da7c-92da-4c14-8250-cd7a85b8a74e · outbound

This paper cites Deep Learning.

Neural Networks Learn Distance Metrics Deep Learning

Reference 11

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Observation 358dce1e-3216-43c4-88aa-d2549b352800 · outbound

This paper cites Qualitatively characterizing neural network optimization problems.

Neural Networks Learn Distance Metrics Qualitatively characterizing neural network optimization problems

Reference 12

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Observation cb431ab5-1d12-410c-9427-27e44725b9e1 · outbound

This paper cites Dimensionality reduction by learning an invariant mapping.

Neural Networks Learn Distance Metrics Dimensionality reduction by learning an invariant mapping

Reference 13

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Observation a242c172-42a5-480c-82c3-28e43d28ff35 · outbound

This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification.

Neural Networks Learn Distance Metrics Delving deep into rectifiers: Surpassing human-level performance on imagenet classification

Reference 14

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Observation e87a0fd8-4279-4b49-aded-e9ee951b7f1a · outbound

This paper cites Momentum contrast for unsupervised visual representation learning.

Neural Networks Learn Distance Metrics Momentum contrast for unsupervised visual representation learning

Reference 15

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Observation def66431-8fa2-432e-aaa6-eb178da67017 · outbound

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

Neural Networks Learn Distance Metrics Neural tangent kernel: Convergence and generalization in neural networks

Reference 16

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Observation e597d481-cdd6-4046-a8bd-649679892f86 · outbound

This paper cites Learning vector quantization.

Neural Networks Learn Distance Metrics Learning vector quantization

Reference 17

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Observation a4c0702d-bcee-49d2-a8d2-8de201edbfc0 · outbound

This paper cites Gradient-based learning applied to document recognition.

Neural Networks Learn Distance Metrics Gradient-based learning applied to document recognition

Reference 18

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Observation 9327d705-9d64-4123-b1fb-97fc36d425e6 · outbound

This paper cites Deep learning.

Neural Networks Learn Distance Metrics Deep learning

Reference 19

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Observation 1db63dcc-73d5-41f2-8be6-ad20d638f239 · outbound

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

Neural Networks Learn Distance Metrics Wide neural networks of any depth evolve as linear models under gradient descent

Reference 20

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Observation a5b2be3c-5009-45eb-acfb-be3121440e16 · outbound

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Neural Networks Learn Distance Metrics Visualizing the loss landscape of neural nets

Reference 21

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Neural Networks Learn Distance Metrics The mythos of model interpretability

Reference 22

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Observation e89f721c-d2a6-407c-bf3d-4ec8fb6a86f9 · outbound

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Neural Networks Learn Distance Metrics On the generalized distance in statistics

Reference 23

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Neural Networks Learn Distance Metrics A logical calculus of the ideas immanent in nervous activity

Reference 24

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Observation 973adc3f-2e80-4556-b246-5aa94124f949 · outbound

This paper cites The mahalanobis distance and its applications in discriminant analysis.

Neural Networks Learn Distance Metrics The mahalanobis distance and its applications in discriminant analysis

Reference 25

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Neural Networks Learn Distance Metrics Mish: A Self Regularized Non-Monotonic Activation Function

Reference 26

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Neural Networks Learn Distance Metrics Methods for interpreting and understanding deep neural networks

Reference 27

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Neural Networks Learn Distance Metrics Fast learning in networks of locally-tuned processing units

Reference 28

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Neural Networks Learn Distance Metrics Unresolved cited work

Reference 29

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Neural Networks Learn Distance Metrics Feature visualization

Reference 30

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Neural Networks Learn Distance Metrics Interpreting Neural Networks through Mahalanobis Distance

Reference 31

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Neural Networks Learn Distance Metrics Neural Networks Use Distance Metrics

Reference 32

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Neural Networks Learn Distance Metrics Universal approximation using radial-basis-function networks

Reference 33

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Observation ed968fb2-2fbd-4b0a-9da0-89a8922eb3b9 · outbound

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Neural Networks Learn Distance Metrics The Effectiveness of Data Augmentation in Image Classification using Deep Learning

Reference 34

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Neural Networks Learn Distance Metrics Introduction to a general theory of elementary propositions

Reference 35

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Observation 208a4e36-5b9f-40fb-9eaa-f25220e6596a · outbound

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Neural Networks Learn Distance Metrics Searching for Activation Functions

Reference 36

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Observation c0a1e7c1-b07c-4852-9cc7-d5608cf0e16b · outbound

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Neural Networks Learn Distance Metrics The perceptron: a probabilistic model for information storage and organization in the brain

Reference 37

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Source-reported events for the cited work

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Observation 1efca63c-f775-43fe-8fec-5255b2a44d04 · outbound

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Neural Networks Learn Distance Metrics Prototypical networks for few-shot learning

Reference 38

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-09T13:24:23.004000Z digest=sha256:d6d948575e27f0baca41e5a3ab07b9ac7c23f280409e114d733cc5f0c7eeb0e0

Observation 4f8c2672-302c-4f24-8b71-3bd5b9194158 · outbound

This paper cites Distance metric learning for large margin nearest neighbor classification.

Neural Networks Learn Distance Metrics Distance metric learning for large margin nearest neighbor classification

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:24:23.155921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-09T13:24:23.007677Z digest=sha256:014ee39d8a637684266d4e04d5fbf315b6482d047b4ad77fdba8a142128ef2f9

Observation 791dfc58-5b74-4bb4-bcee-f8ca6644a73d · outbound

This paper cites Xing, Andrew Y.

Neural Networks Learn Distance Metrics Xing, Andrew Y

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:24:23.142031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-09T13:24:23.011431Z digest=sha256:607c17fef6229a9de9d8f26991858c0509cced90c455e2e6de01bc362b813847

Pith citing papers

No inbound Pith citation observations are available.