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Paper Citation Record · LEDGER

CantorNet: A Sandbox for Testing Geometrical and Topological Complexity Measures

As of 23 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2411.19713.

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

pith.paper-citation-record.v1
2411.19713 v3

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T06:02:20.401209Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+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

23 of 23 outbound references displayed

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  • verified fuzzy19
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 28eb7b1c-cb71-4b4e-894d-bdabeb394422 · outbound

This paper cites Video segmentation through multiscale texture analysis.

CantorNet: A Sandbox for Testing Geometrical and Topological Complexity Measures Video segmentation through multiscale texture analysis

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation 87200c3d-e2cb-48ad-b393-61bd0734bf53 · outbound

This paper cites Understanding Deep Neural Networks with Rectified Linear Units.

CantorNet: A Sandbox for Testing Geometrical and Topological Complexity Measures Understanding Deep Neural Networks with Rectified Linear Units

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation e3393e22-a116-44d3-b13c-5fa8cd0a4165 · outbound

This paper cites On the complexity of neural network classifiers: A comparison between shallow and deep architectures.

CantorNet: A Sandbox for Testing Geometrical and Topological Complexity Measures On the complexity of neural network classifiers: A comparison between shallow and deep architectures

Reference 3

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Observation f81cd0c4-4366-4ae5-ae05-8d915a335592 · outbound

This paper cites Ueber unendliche, lineare punktmannichfaltigkeiten.

CantorNet: A Sandbox for Testing Geometrical and Topological Complexity Measures Ueber unendliche, lineare punktmannichfaltigkeiten

Reference 4

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation ea8b7ed5-bb19-4e67-9c13-ede60a948dcf · outbound

This paper cites Visualizing music and audio using self-similarity.

CantorNet: A Sandbox for Testing Geometrical and Topological Complexity Measures Visualizing music and audio using self-similarity

Reference 5

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 8388614f-1b18-48f5-9e0a-23c79b5d1228 · outbound

This paper cites Adversarial Spheres.

CantorNet: A Sandbox for Testing Geometrical and Topological Complexity Measures Adversarial Spheres

Reference 6

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Observation 7c48e87a-69ed-4e34-a805-d6ddff394558 · outbound

This paper cites Grünwald, Jay Injae Myung, and Mark A.

CantorNet: A Sandbox for Testing Geometrical and Topological Complexity Measures Grünwald, Jay Injae Myung, and Mark A

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation 275fc7c9-a98b-4a18-8484-5ffcd031bdce · outbound

This paper cites Klir and Bo Yuan.

CantorNet: A Sandbox for Testing Geometrical and Topological Complexity Measures Klir and Bo Yuan

Reference 8

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation 2156c339-48ec-40ca-893f-5ee986af5bc7 · outbound

This paper cites Three approaches to the quantitative definition of information.

CantorNet: A Sandbox for Testing Geometrical and Topological Complexity Measures Three approaches to the quantitative definition of information

Reference 9

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Observation 99f54f1e-4746-4e2b-ba15-8df146f85675 · outbound

This paper cites Imagenet classification with deep convolutional neural networks.

CantorNet: A Sandbox for Testing Geometrical and Topological Complexity Measures Imagenet classification with deep convolutional neural networks

Reference 10

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation b14e1970-6c64-49f6-93b5-4a07d0c004c5 · outbound

This paper cites Maas, Awni Y.

CantorNet: A Sandbox for Testing Geometrical and Topological Complexity Measures Maas, Awni Y

Reference 11

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Observation 3877e620-cf24-4fd3-acff-e09f91c2d649 · outbound

This paper cites Mandelbrot.

CantorNet: A Sandbox for Testing Geometrical and Topological Complexity Measures Mandelbrot

Reference 12

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation 5b16413f-75a8-4544-ad1b-5bb4c7241d5f · outbound

This paper cites Mandelbrot.

CantorNet: A Sandbox for Testing Geometrical and Topological Complexity Measures Mandelbrot

Reference 13

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation b9c677c2-29b9-4c03-92da-ca316764aa17 · outbound

This paper cites Perceptrons: An Introduction to Computational Geometry.

CantorNet: A Sandbox for Testing Geometrical and Topological Complexity Measures Perceptrons: An Introduction to Computational Geometry

Reference 14

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation 2c18995e-055c-4d22-b7ab-4d7808b2b9a8 · outbound

This paper cites On the number of linear regions of deep neural networks.

CantorNet: A Sandbox for Testing Geometrical and Topological Complexity Measures On the number of linear regions of deep neural networks

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation 198089c8-bf31-4733-b9c2-af31f86b7595 · outbound

This paper cites Moser, Michal Lewandowski, Somayeh Kargaran, Werner Zellinger, Battista Biggio, and Christoph Koutschan.

CantorNet: A Sandbox for Testing Geometrical and Topological Complexity Measures Moser, Michal Lewandowski, Somayeh Kargaran, Werner Zellinger, Battista Biggio, and Christoph Koutschan

Reference 16

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation 1000ad8c-b7a7-4208-b993-c76854044780 · outbound

This paper cites Discovering neural nets with low kolmogorov complexity and high generalization capability.

CantorNet: A Sandbox for Testing Geometrical and Topological Complexity Measures Discovering neural nets with low kolmogorov complexity and high generalization capability

Reference 17

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation 5ac6cf3b-f793-4441-a29b-21440fd079f5 · outbound

This paper cites Relu code space: A basis for rating network quality besides accuracy.

CantorNet: A Sandbox for Testing Geometrical and Topological Complexity Measures Relu code space: A basis for rating network quality besides accuracy

Reference 18

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation 21778030-0ca2-40b1-81ac-96884555bdab · outbound

This paper cites A formal theory of inductive inference.

CantorNet: A Sandbox for Testing Geometrical and Topological Complexity Measures A formal theory of inductive inference

Reference 19

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation 40ef573d-7613-46e9-9d05-627dc24dcbb5 · outbound

This paper cites an unresolved cited work.

CantorNet: A Sandbox for Testing Geometrical and Topological Complexity Measures Unresolved cited work

Reference 20

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Unavailable: canonical work link unavailable.

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Observation fae9b830-adc1-490f-87d6-f27731f42f0e · outbound

This paper cites Sur une courbe continue sans tangente, obtenue par une construction géométrique élémentaire.

CantorNet: A Sandbox for Testing Geometrical and Topological Complexity Measures Sur une courbe continue sans tangente, obtenue par une construction géométrique élémentaire

Reference 21

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Observation 86dfd7d5-ef2b-4753-a14d-3a2fada08e2f · outbound

This paper cites A survey on face data augmentation for the training of deep neural networks.

CantorNet: A Sandbox for Testing Geometrical and Topological Complexity Measures A survey on face data augmentation for the training of deep neural networks

Reference 22

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Observation 30b96e5d-9ac4-418b-a953-68ab923e7756 · outbound

This paper cites Entropy of fractal systems.

CantorNet: A Sandbox for Testing Geometrical and Topological Complexity Measures Entropy of fractal systems

Reference 23

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Pith citing papers

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