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Paper Citation Record · LEDGER

Efficiency and Scalability of Multi-Lane Capsule Networks (MLCN)

As of 16 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:1908.03935.

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

pith.paper-citation-record.v1
1908.03935 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T14:00:47.613372Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

20 of 20 outbound references displayed

  • verified exact2
  • verified fuzzy11
  • unresolved7
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f4cbef11-0beb-469c-9a29-093a4a36c31f · outbound

This paper cites GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism.

Efficiency and Scalability of Multi-Lane Capsule Networks (MLCN) GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-14T14:00:47.542814Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T14:00:47.542814Z digest=sha256:e6ce41012e74994848203b802ecb33e091961839a26b377d6944f813f8b5ac6d

Observation 0cbf2410-c8e1-4cf2-97e0-3bc902d21fe3 · outbound

This paper cites High performance training of deep neural networks using pipelined hardware acceleration and distributed memory,.

Efficiency and Scalability of Multi-Lane Capsule Networks (MLCN) High performance training of deep neural networks using pipelined hardware acceleration and distributed memory,

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:00:47.868806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T14:00:47.547850Z digest=sha256:fd6e45a8f89d8c66e976c9ecb0a31adda5cf88985d9b97798d103e8a1d717d9f

Observation f55b7c8e-5ed4-471e-be24-ab5f7cdcc455 · outbound

This paper cites Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis.

Efficiency and Scalability of Multi-Lane Capsule Networks (MLCN) Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis

Reference 3

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unresolved
no resolver link, observed 2026-08-14T14:00:47.552547Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T14:00:47.552547Z digest=sha256:80822cdb1289817a25a48ba3a6126fb2d122d41c85f4f51fa6e7ab2d58609809

Observation e0776425-1d62-4854-b5a3-ec26dacb854b · outbound

This paper cites Beyond Data and Model Parallelism for Deep Neural Networks.

Efficiency and Scalability of Multi-Lane Capsule Networks (MLCN) Beyond Data and Model Parallelism for Deep Neural Networks

Reference 4

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unresolved
no resolver link, observed 2026-08-14T14:00:47.557013Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T14:00:47.557013Z digest=sha256:1be7665edc92de5b77c84d8cecdd6f31ee06b0e7bc291914942137d6d7072d21

Observation 976a86fe-1da9-4a59-8cab-5f4800a0b0b2 · outbound

This paper cites The multi-lane capsule network,.

Efficiency and Scalability of Multi-Lane Capsule Networks (MLCN) The multi-lane capsule network,

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:00:47.857736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T14:00:47.561406Z digest=sha256:8dccd078524fe2d08439827c8f3ad7005e0684642d05a9e7b3fea64c1e5f6a75

Observation eb84a331-bb31-45ef-b7a4-aa3c7416cbdc · outbound

This paper cites Xception: Deep learning with depthwise separable convolu- tions,.

Efficiency and Scalability of Multi-Lane Capsule Networks (MLCN) Xception: Deep learning with depthwise separable convolu- tions,

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-14T14:00:47.565330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T14:00:47.565330Z digest=sha256:bc09d5944692f6bb8827bacd4896bbe9ae248531b233c5037f7995784c106f8d

Observation c9ba4549-f0a7-4d2b-b924-64eb3d5d0fa2 · outbound

This paper cites Inception-v4, inception-resnet and the impact of residual connections on learning,.

Efficiency and Scalability of Multi-Lane Capsule Networks (MLCN) Inception-v4, inception-resnet and the impact of residual connections on learning,

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:00:47.839465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T14:00:47.569600Z digest=sha256:969ab1d584ba833f82469f332b2195f0dfc857d87e252f7d60b916a86b413df1

Observation fcde6916-bab4-4e59-9490-bcd4a49fa3ba · outbound

This paper cites Transforming auto- encoders,.

Efficiency and Scalability of Multi-Lane Capsule Networks (MLCN) Transforming auto- encoders,

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:00:47.828732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T14:00:47.573112Z digest=sha256:23af4ed749ee37def70121a40f0015bc75f14231969620818e79af5dd4680fec

Observation eda89c74-3a84-4eef-8c16-1012428b5ced · outbound

This paper cites Dynamic routing between capsules,.

Efficiency and Scalability of Multi-Lane Capsule Networks (MLCN) Dynamic routing between capsules,

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:00:47.818943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T14:00:47.576555Z digest=sha256:08d55bcaec22deddc11a2eb10144126fd57b6aa1dc6666dc53d17212e82bc984

Observation d3ddf990-a972-426e-a14f-421023f004a4 · outbound

This paper cites Improved Explainability of Capsule Networks: Relevance Path by Agreement.

Efficiency and Scalability of Multi-Lane Capsule Networks (MLCN) Improved Explainability of Capsule Networks: Relevance Path by Agreement

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-14T14:00:47.580274Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T14:00:47.580274Z digest=sha256:99ea2d014829f2f09f80364f961e5b67ce721598ca51dc953b7b439e120f8f0c

Observation dc2a1555-35d5-462b-acfb-10f13c8c5e4a · outbound

This paper cites Capsulegan: Generative adversarial capsule network,.

Efficiency and Scalability of Multi-Lane Capsule Networks (MLCN) Capsulegan: Generative adversarial capsule network,

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:00:47.808649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T14:00:47.584073Z digest=sha256:a2312c631c7aa8d89d90b07128de4afb6e50d204c0b886e0123827471161d346

Observation 365a411c-6250-474a-9b1d-9469c92d2c18 · outbound

This paper cites Compositional Coding Capsule Network with K-Means Routing for Text Classification.

Efficiency and Scalability of Multi-Lane Capsule Networks (MLCN) Compositional Coding Capsule Network with K-Means Routing for Text Classification

Reference 12

Resolution
verified exact
local_arxiv, observed 2026-08-14T14:00:47.680475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T14:00:47.587585Z digest=sha256:b419839e71409be8905012b3165dbe3bbac07c71c757df37d7e353215c3de695

Observation 315fa440-04a7-436e-bd13-eb5f36f9652c · outbound

This paper cites Capsule networks against medical imaging data challenges,.

Efficiency and Scalability of Multi-Lane Capsule Networks (MLCN) Capsule networks against medical imaging data challenges,

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:00:47.797486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T14:00:47.590927Z digest=sha256:7e96a2b42b2f8da2b3e1771f1fdec2cbdd604a732b7c607e0636a7fb91714e71

Observation 346d7e56-8c1f-4552-b2f2-07c3f48e92d4 · outbound

This paper cites Fast CapsNet for Lung Cancer Screening.

Efficiency and Scalability of Multi-Lane Capsule Networks (MLCN) Fast CapsNet for Lung Cancer Screening

Reference 14

Resolution
verified exact
local_arxiv, observed 2026-08-14T14:00:47.663933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T14:00:47.594010Z digest=sha256:eb396630d5a09d43c3b656b762a4ad4e8fc2b8b5ae8a35709194ee468df40ede

Observation 34fb8847-61ec-4c92-8542-4198bae684a1 · outbound

This paper cites A capsule network for traffic speed prediction in complex road networks,.

Efficiency and Scalability of Multi-Lane Capsule Networks (MLCN) A capsule network for traffic speed prediction in complex road networks,

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:00:47.784464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T14:00:47.597543Z digest=sha256:23a4d7128e83f103e6e1525479db55270db268ff5ab2770d5df4eda21aceca3f

Observation 71b4f356-de62-412c-8530-3bcf8bbbaae5 · outbound

This paper cites CapsNet comparative performance evaluation for image classification.

Efficiency and Scalability of Multi-Lane Capsule Networks (MLCN) CapsNet comparative performance evaluation for image classification

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-14T14:00:47.600716Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T14:00:47.600716Z digest=sha256:aec8c57c9956a2a0dd59a99dd3d5c5dac0afb0897c41830b457a7b5e13f9fa9e

Observation c7d63477-8d19-4598-af45-7e57e09939ec · outbound

This paper cites Ms-capsnet: A novel multi-scale capsule network,.

Efficiency and Scalability of Multi-Lane Capsule Networks (MLCN) Ms-capsnet: A novel multi-scale capsule network,

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-14T14:00:47.603947Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T14:00:47.603947Z digest=sha256:b3ce17d02f69bb8aa22f85d613ad30ed1a44b68975cdbc20780c1724d5cd8606

Observation 1edde92c-cdcf-4525-bef5-a3eb7836bee9 · outbound

This paper cites Path capsule networks,.

Efficiency and Scalability of Multi-Lane Capsule Networks (MLCN) Path capsule networks,

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:00:47.765408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T14:00:47.607196Z digest=sha256:0c5ba0b407e9e5ebcc09a5d1beb8257a67f4726560c40bd064c50982fb15d178

Observation 32abba1a-2fbc-443a-afbd-d5440f3aa590 · outbound

This paper cites Computing science: The easiest hard problem,.

Efficiency and Scalability of Multi-Lane Capsule Networks (MLCN) Computing science: The easiest hard problem,

Reference 19

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verified fuzzy
raw_fallback, observed 2026-08-14T14:00:47.753038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T14:00:47.610293Z digest=sha256:b797991eeb1ba1a6c7caeab56a5b937a96296d2c4b2691148cfd2768cf9e6aa1

Observation d054b754-f602-4baf-9aba-b5c772a87677 · outbound

This paper cites Multi-way number partitioning,.

Efficiency and Scalability of Multi-Lane Capsule Networks (MLCN) Multi-way number partitioning,

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:00:47.739829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T14:00:47.613372Z digest=sha256:779d8f47e63866612778b6eefaac4e46da078d06f42c6c1c6f705c5d42f83dc6

Pith citing papers

No inbound Pith citation observations are available.