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Paper Citation Record · LEDGER

Riemannian Deep Learning: Modules, Networks, and Geometries

As of 8 August 2026, this Paper Citation Record lists 100 of 300 outbound references and 0 inbound Pith citation observations for arXiv:2607.19305.

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

pith.paper-citation-record.v1
2607.19305 v2

Coverage vector

measured 100 of 300 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T12:55:20.750796Z

measured 100 of 100 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

100 of 300 outbound references displayed

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  • verified fuzzy0
  • unresolved100
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7cc461cd-a514-4f2c-b7ac-ffe4650cc391 · outbound

This paper cites ICCV , year =.

Riemannian Deep Learning: Modules, Networks, and Geometries ICCV , year =

Reference 1

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Observation ca1070cb-a898-4b28-ac05-46745d3ec6e3 · outbound

This paper cites CVPR , year =.

Riemannian Deep Learning: Modules, Networks, and Geometries CVPR , year =

Reference 2

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Observation 33eceee9-42bb-4287-a7b8-5231520be99b · outbound

This paper cites ICCV , year =.

Riemannian Deep Learning: Modules, Networks, and Geometries ICCV , year =

Reference 3

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Observation 40809372-46c8-43b4-bfa8-85a9ed9a6724 · outbound

This paper cites Training Deep Networks with Structured Layers by Matrix Backpropagation.

Riemannian Deep Learning: Modules, Networks, and Geometries Training Deep Networks with Structured Layers by Matrix Backpropagation

Reference 4

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Observation e55c2348-cbc4-4bb5-a53c-be460835c525 · outbound

This paper cites 2008 , publisher=.

Riemannian Deep Learning: Modules, Networks, and Geometries 2008 , publisher=

Reference 5

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Observation daaa7477-6e69-40a2-b5f9-10475a1c259a · outbound

This paper cites 2007 , publisher=.

Riemannian Deep Learning: Modules, Networks, and Geometries 2007 , publisher=

Reference 6

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Observation d4a687ed-7731-4ec3-a14d-de4015c39da7 · outbound

This paper cites 2013 , series=.

Riemannian Deep Learning: Modules, Networks, and Geometries 2013 , series=

Reference 7

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Observation 884649e5-fd0a-4a15-aa4d-8161b70c619f · outbound

This paper cites BMVC , year=.

Riemannian Deep Learning: Modules, Networks, and Geometries BMVC , year=

Reference 8

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Observation faade106-592e-4ccb-95db-bb5c10f261ed · outbound

This paper cites NeurIPS , year =.

Riemannian Deep Learning: Modules, Networks, and Geometries NeurIPS , year =

Reference 9

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Observation 625069d4-2a8b-4809-bca1-303006ae5265 · outbound

This paper cites Essentials of Pad.

Riemannian Deep Learning: Modules, Networks, and Geometries Essentials of Pad

Reference 10

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Observation 98e3695f-1df6-4c4f-b2cb-2b537b5a70b8 · outbound

This paper cites The theory and application of the Pad.

Riemannian Deep Learning: Modules, Networks, and Geometries The theory and application of the Pad

Reference 11

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Observation 268dafa9-4341-4a41-bf4e-7a28f9289ab5 · outbound

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Riemannian Deep Learning: Modules, Networks, and Geometries Unresolved cited work

Reference 12

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Observation db94a256-12ee-4f32-b155-4cc7ff734638 · outbound

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Riemannian Deep Learning: Modules, Networks, and Geometries Unresolved cited work

Reference 13

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Observation 61c5a2f5-8dd6-42eb-84bb-c7ac0650584c · outbound

This paper cites General theorems on the convergence of sequences of Pad.

Riemannian Deep Learning: Modules, Networks, and Geometries General theorems on the convergence of sequences of Pad

Reference 14

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Observation 739c74f8-4784-406e-8dc2-e73b9b18cf23 · outbound

This paper cites An Overview , author=.

Riemannian Deep Learning: Modules, Networks, and Geometries An Overview , author=

Reference 15

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Observation 6bd80af8-7cd3-44a0-8b5a-e4733ca61eb0 · outbound

This paper cites ICCV , year =.

Riemannian Deep Learning: Modules, Networks, and Geometries ICCV , year =

Reference 16

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Observation afe49a36-1bc0-4916-8a29-586d017a20ae · outbound

This paper cites CVPR , year =.

Riemannian Deep Learning: Modules, Networks, and Geometries CVPR , year =

Reference 17

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Observation 0b651cb8-4875-4a21-a83e-6a1310826f32 · outbound

This paper cites CVPR , year =.

Riemannian Deep Learning: Modules, Networks, and Geometries CVPR , year =

Reference 18

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Observation 7f56d33d-3f67-48ea-8b64-ef6200c92f68 · outbound

This paper cites CVPR , year =.

Riemannian Deep Learning: Modules, Networks, and Geometries CVPR , year =

Reference 19

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Observation 01384c7b-4eb4-48ff-9af7-1eec77f3485a · outbound

This paper cites IEEE TPAMI , year =.

Riemannian Deep Learning: Modules, Networks, and Geometries IEEE TPAMI , year =

Reference 20

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Observation 650825a4-6c02-4652-a15b-c5309423f4f3 · outbound

This paper cites Dang and K.

Riemannian Deep Learning: Modules, Networks, and Geometries Dang and K

Reference 21

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Observation de0b75c7-56b6-46ae-b13c-337853fe7b30 · outbound

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Riemannian Deep Learning: Modules, Networks, and Geometries Dang and K.M

Reference 22

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Riemannian Deep Learning: Modules, Networks, and Geometries IEEE TPAMI , year =

Reference 23

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Riemannian Deep Learning: Modules, Networks, and Geometries CVPR , year =

Reference 24

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Riemannian Deep Learning: Modules, Networks, and Geometries ICCV , year =

Reference 25

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Riemannian Deep Learning: Modules, Networks, and Geometries CVPR , year =

Reference 26

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Riemannian Deep Learning: Modules, Networks, and Geometries ECCV , year =

Reference 27

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Observation 8b3bafa7-a5d7-4369-b114-a977af060820 · outbound

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Riemannian Deep Learning: Modules, Networks, and Geometries CVPR , year =

Reference 28

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Riemannian Deep Learning: Modules, Networks, and Geometries 1981 , publisher=

Reference 29

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Riemannian Deep Learning: Modules, Networks, and Geometries CVPR , year =

Reference 30

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Riemannian Deep Learning: Modules, Networks, and Geometries Welinder and S

Reference 31

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Riemannian Deep Learning: Modules, Networks, and Geometries Fine-Grained Visual Classification of Aircraft

Reference 32

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Riemannian Deep Learning: Modules, Networks, and Geometries 2013 , address =

Reference 33

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Riemannian Deep Learning: Modules, Networks, and Geometries Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 34

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Riemannian Deep Learning: Modules, Networks, and Geometries Novel Dataset for Fine-Grained Image Categorization

Reference 35

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Riemannian Deep Learning: Modules, Networks, and Geometries 2003 , publisher=

Reference 36

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Riemannian Deep Learning: Modules, Networks, and Geometries ICCV , year =

Reference 37

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Riemannian Deep Learning: Modules, Networks, and Geometries CVPR , year =

Reference 38

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Riemannian Deep Learning: Modules, Networks, and Geometries CVPR , year =

Reference 39

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Riemannian Deep Learning: Modules, Networks, and Geometries CVPR , year =

Reference 40

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Riemannian Deep Learning: Modules, Networks, and Geometries CVPR , year =

Reference 41

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Riemannian Deep Learning: Modules, Networks, and Geometries ECCV , year =

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Riemannian Deep Learning: Modules, Networks, and Geometries ECCV , year =

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Observation 6efed181-acf1-4b05-b2a0-ab3e7a6c2e72 · outbound

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Riemannian Deep Learning: Modules, Networks, and Geometries NeurIPS , year =

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Riemannian Deep Learning: Modules, Networks, and Geometries ECCV , year =

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Riemannian Deep Learning: Modules, Networks, and Geometries SVD Based Image Processing Applications: State of The Art, Contributions and Research Challenges

Reference 46

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Riemannian Deep Learning: Modules, Networks, and Geometries 2007 , publisher=

Reference 47

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Riemannian Deep Learning: Modules, Networks, and Geometries Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 48

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Riemannian Deep Learning: Modules, Networks, and Geometries ECCV , year =

Reference 49

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Riemannian Deep Learning: Modules, Networks, and Geometries ICCV , year =

Reference 50

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Observation 14f0c08b-0b33-4e7f-8335-3b46319a7cb7 · outbound

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Riemannian Deep Learning: Modules, Networks, and Geometries ICCV , year =

Reference 51

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Observation 0def034f-3413-42d5-9be8-7e1063b53476 · outbound

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Riemannian Deep Learning: Modules, Networks, and Geometries CVPR , year =

Reference 52

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Observation ab995709-7cdb-47d6-888c-e4af7b81dadd · outbound

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Riemannian Deep Learning: Modules, Networks, and Geometries Striving for Simplicity: The All Convolutional Net

Reference 53

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Observation e4a51608-d537-476a-80cb-cbf54d80792a · outbound

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Riemannian Deep Learning: Modules, Networks, and Geometries ICCV , year =

Reference 54

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Riemannian Deep Learning: Modules, Networks, and Geometries BMVC , year=

Reference 55

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Riemannian Deep Learning: Modules, Networks, and Geometries NeurIPS , year =

Reference 56

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Riemannian Deep Learning: Modules, Networks, and Geometries CVIU , volume=

Reference 57

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Observation 724eb013-4570-481f-855e-e94e05e58ea8 · outbound

This paper cites CVPR , year =.

Riemannian Deep Learning: Modules, Networks, and Geometries CVPR , year =

Reference 58

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This paper cites CVPR , year =.

Riemannian Deep Learning: Modules, Networks, and Geometries CVPR , year =

Reference 59

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Observation 5dc6a405-f8a2-44f5-a136-24dc0c2f3040 · outbound

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Riemannian Deep Learning: Modules, Networks, and Geometries Psychometrika , volume=

Reference 60

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Observation ba8214ef-5ccc-4526-a5fc-273e202d8e58 · outbound

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Riemannian Deep Learning: Modules, Networks, and Geometries Delving Deeper into the Whorl of Flower Segmentation

Reference 61

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Riemannian Deep Learning: Modules, Networks, and Geometries Mathematische Nachrichten , volume=

Reference 62

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Observation f56136fd-8e9a-46fd-b325-ce6a1a2424a9 · outbound

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Riemannian Deep Learning: Modules, Networks, and Geometries Monatshefte f

Reference 63

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Riemannian Deep Learning: Modules, Networks, and Geometries ICCV , year =

Reference 64

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Riemannian Deep Learning: Modules, Networks, and Geometries 2015 , URL =

Reference 65

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Riemannian Deep Learning: Modules, Networks, and Geometries The Annals of Applied Statistics , volume=

Reference 66

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Riemannian Deep Learning: Modules, Networks, and Geometries SIAM journal on matrix analysis and applications , volume=

Reference 67

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Riemannian Deep Learning: Modules, Networks, and Geometries IJCV , year =

Reference 68

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Observation ce1033e8-595b-415a-9df1-8c365dc12455 · outbound

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Reference 69

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Riemannian Deep Learning: Modules, Networks, and Geometries NeurIPS , year =

Reference 70

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Observation 7b6c12d4-0a76-4d5d-90b0-e7efb9541f1a · outbound

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Riemannian Deep Learning: Modules, Networks, and Geometries WebVision Database: Visual Learning and Understanding from Web Data

Reference 71

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Observation d44f667c-18a8-4079-93ea-3529d5273f32 · outbound

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Riemannian Deep Learning: Modules, Networks, and Geometries Are we done with ImageNet?

Reference 72

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Observation 9e5462e1-2ef0-4c48-997d-38864a91d140 · outbound

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Riemannian Deep Learning: Modules, Networks, and Geometries Communications of the ACM , volume=

Reference 73

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Riemannian Deep Learning: Modules, Networks, and Geometries CVPR , year =

Reference 74

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Observation 207c21e0-b70b-4d37-852f-706ee8caffb5 · outbound

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Riemannian Deep Learning: Modules, Networks, and Geometries NeurIPS , year =

Reference 75

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Observation 17d7840e-6fc1-47b8-8eb0-5a8ef679874c · outbound

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Riemannian Deep Learning: Modules, Networks, and Geometries CVPR , year =

Reference 76

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Observation 3f9e39a9-73ac-4892-9d9a-8f6317ba211b · outbound

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Riemannian Deep Learning: Modules, Networks, and Geometries ZAMM-Journal of Applied Mathematics and Mechanics/Zeitschrift f

Reference 77

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Observation 91ce2a76-24ab-477b-9d41-e7325d6f593f · outbound

This paper cites SoT: Delving Deeper into Classification Head for Transformer.

Riemannian Deep Learning: Modules, Networks, and Geometries SoT: Delving Deeper into Classification Head for Transformer

Reference 78

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Observation 315b49c3-ccea-4098-92af-38cd0c75db8a · outbound

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Riemannian Deep Learning: Modules, Networks, and Geometries ICML , year =

Reference 79

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Observation ade1eecf-aa5a-4842-8f59-72e915151c02 · outbound

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Riemannian Deep Learning: Modules, Networks, and Geometries ICCV , year =

Reference 80

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Observation 8b130868-fede-4988-9122-1a3a07cfa6d0 · outbound

This paper cites CVPR , year =.

Riemannian Deep Learning: Modules, Networks, and Geometries CVPR , year =

Reference 81

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Observation 48344176-009e-494a-8d7d-125557e5588f · outbound

This paper cites Keep it Simple: Image Statistics Matching for Domain Adaptation.

Riemannian Deep Learning: Modules, Networks, and Geometries Keep it Simple: Image Statistics Matching for Domain Adaptation

Reference 82

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Observation 44e1377b-0ff2-4b34-8d26-60639d236330 · outbound

This paper cites CVPR , year =.

Riemannian Deep Learning: Modules, Networks, and Geometries CVPR , year =

Reference 83

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source=arxiv_source observed=2026-08-01T12:55:19.574351Z digest=sha256:0a721c8ca613329515fc212150de4b7938d8c98123132ac79f83cfb304f0362b

Observation bdf14898-8f77-486c-a608-afb0b59f24cd · outbound

This paper cites IEEE transactions on automatic control , volume=.

Riemannian Deep Learning: Modules, Networks, and Geometries IEEE transactions on automatic control , volume=

Reference 84

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source=arxiv_source observed=2026-08-01T12:55:19.644800Z digest=sha256:d0ec20ef1435e215756ae2488d8e9b961abb6127f4a1795b0d229677f2e8bac2

Observation 71641d77-6ec3-48f5-8a4b-6094d8732f4e · outbound

This paper cites International Journal of Control , volume=.

Riemannian Deep Learning: Modules, Networks, and Geometries International Journal of Control , volume=

Reference 85

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source=arxiv_source observed=2026-08-01T12:55:19.748310Z digest=sha256:30d877f78bf5a0702c0318c19b44120ee5025ca08c168bacfca0f234db945877

Observation 5b351db9-1831-41d6-b448-a7b257aa3a03 · outbound

This paper cites Journal of Scientific Computing , volume=.

Riemannian Deep Learning: Modules, Networks, and Geometries Journal of Scientific Computing , volume=

Reference 86

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source=arxiv_source observed=2026-08-01T12:55:19.803490Z digest=sha256:3044dcd460c6738fdb96e8960d40e46208e3782cbf267860405478004cbba4a2

Observation 6aef82a1-b13c-44f3-abb4-6e97b64bdf78 · outbound

This paper cites ICLR , year =.

Riemannian Deep Learning: Modules, Networks, and Geometries ICLR , year =

Reference 87

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source=arxiv_source observed=2026-08-01T12:55:19.871386Z digest=sha256:13b5c8056ecf194e58986dc08dcb5d16aed395cd15d14c1f6b1b11d2570d195b

Observation 0fdc8759-ea9a-4a14-9c02-8ab345489e47 · outbound

This paper cites Neurocomputing , year=.

Riemannian Deep Learning: Modules, Networks, and Geometries Neurocomputing , year=

Reference 88

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source=arxiv_source observed=2026-08-01T12:55:19.980226Z digest=sha256:565da36a75f1adf7128224a6543204b29f115c9dcfc75718cf082fa7a9be928a

Observation 60ba7b56-3400-4102-bade-221450d2e34e · outbound

This paper cites CVPR , year =.

Riemannian Deep Learning: Modules, Networks, and Geometries CVPR , year =

Reference 89

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source=arxiv_source observed=2026-08-01T12:55:20.075623Z digest=sha256:226ed4457009d533de88631366acef708784b3d395a4b833fca49dd5e68fd72d

Observation 35d1a219-e2c0-46f6-acbf-a10d284ec460 · outbound

This paper cites Master's thesis, University of Tront , year=.

Riemannian Deep Learning: Modules, Networks, and Geometries Master's thesis, University of Tront , year=

Reference 90

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source=arxiv_source observed=2026-08-01T12:55:20.134449Z digest=sha256:6da22301685623ef20712e7d14da3ba220e594e15ce382d11269bbb1981d7f64

Observation 0ca494bc-8254-4e43-ac87-51f85f59a84b · outbound

This paper cites NeurIPS , year =.

Riemannian Deep Learning: Modules, Networks, and Geometries NeurIPS , year =

Reference 91

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source=arxiv_source observed=2026-08-01T12:55:20.191145Z digest=sha256:897b8041b327dd3a5d2bb755d379d2a9bcbd56309e9f16320efd5123ddb20ce5

Observation 4d7b5656-3250-4d48-a751-c2ceb1523d4f · outbound

This paper cites ICLR , year =.

Riemannian Deep Learning: Modules, Networks, and Geometries ICLR , year =

Reference 92

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source=arxiv_source observed=2026-08-01T12:55:20.240410Z digest=sha256:6491fb5fe87e89f87ccae0b08b854d3a7e75fd4d70412781588b81b8ec0614c7

Observation b400c82d-565f-4655-b3a9-b442762bc34b · outbound

This paper cites CVPR , year =.

Riemannian Deep Learning: Modules, Networks, and Geometries CVPR , year =

Reference 93

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source=arxiv_source observed=2026-08-01T12:55:20.298979Z digest=sha256:5d9d62c5fa1f9813f1e51d6a03d0cf47a4a3746874e2801a2d89b73cc2bab753

Observation 0bae9be2-26db-455e-8f7c-4f73c2cc36a5 · outbound

This paper cites CVPR , year =.

Riemannian Deep Learning: Modules, Networks, and Geometries CVPR , year =

Reference 94

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source=arxiv_source observed=2026-08-01T12:55:20.358220Z digest=sha256:907d1fbd9a79906501bd4050263b971aa10d47de5af569e4e0997aba71c40e42

Observation 5cd6da6f-9e1d-4b77-adbf-dd5bf37ff68f · outbound

This paper cites Spurious poles in Pad.

Riemannian Deep Learning: Modules, Networks, and Geometries Spurious poles in Pad

Reference 95

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source=arxiv_source observed=2026-08-01T12:55:20.411439Z digest=sha256:5194ccc143e2395d79b40920d16959f2aa05e9708f3f7e0cd0d0241da0dcff7a

Observation 795e6358-c2f8-4935-ae41-82bca219be5a · outbound

This paper cites Defects and the convergence of Pad.

Riemannian Deep Learning: Modules, Networks, and Geometries Defects and the convergence of Pad

Reference 96

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source=arxiv_source observed=2026-08-01T12:55:20.467927Z digest=sha256:e30270bbd763ec36e3a88e0a4dc38037600249e33cfcdea17bab2a687a6f065a

Observation 6e52d85b-c0c6-4078-9b76-f869274c625f · outbound

This paper cites and Jhuang, H.

Riemannian Deep Learning: Modules, Networks, and Geometries and Jhuang, H

Reference 97

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source=arxiv_source observed=2026-08-01T12:55:20.522289Z digest=sha256:f8f676139b4ef09c8db57ce00b988294d617abe535ef943a6eb9f2aaa2a37c90

Observation c28ddc87-b6dd-4d41-b7c4-5b3df4b571c9 · outbound

This paper cites UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild.

Riemannian Deep Learning: Modules, Networks, and Geometries UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 98

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source=arxiv_source observed=2026-08-01T12:55:20.582698Z digest=sha256:ccdb58de2f4652e0f08fceddf06afed2d15304e4e441408cb4ac7761a1b8856d

Observation 9d40612d-910a-45fa-99e7-0923ab6ca5ed · outbound

This paper cites CVPR , year =.

Riemannian Deep Learning: Modules, Networks, and Geometries CVPR , year =

Reference 99

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source=arxiv_source observed=2026-08-01T12:55:20.657842Z digest=sha256:74d5bd61f2c3c1754c7263dc7e32e749e8e8f1683eb927c2bf1de7fbba8c8307

Observation 6e8d448e-52da-4b54-bce2-eed51f1aac13 · outbound

This paper cites Numerical Algorithms , volume=.

Riemannian Deep Learning: Modules, Networks, and Geometries Numerical Algorithms , volume=

Reference 100

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source=arxiv_source observed=2026-08-01T12:55:20.750796Z digest=sha256:38772ab735246c834ceba5e4882e5da6e8728e8bbc02bf669524b4ad2bcc6588

Pith citing papers

No inbound Pith citation observations are available.