Paper Citation Record · LEDGER

Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach

As of 15 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2506.14846.

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

pith.paper-citation-record.v1
2506.14846 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:32:57.679270Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

31 of 31 outbound references displayed

verified exact2
verified fuzzy6
unresolved19
parse uncertain0
malformed identifier1
metadata mismatch3

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c7069d87-d20a-493f-b1db-d7a44bd32005 · outbound

This paper cites Deep Residual Learning for Image Recognition.

Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach Deep Residual Learning for Image Recognition

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T00:32:57.541901Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13bebe9b-f8b8-4c91-ac21-6367680d7dd8 · outbound

This paper cites Densely Connected Convolutional Networks.

Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach Densely Connected Convolutional Networks

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T00:32:57.546901Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9501e7b5-b298-44ae-9347-e26b2435db97 · outbound

This paper cites Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs.

Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T00:32:57.552039Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4810d007-f72a-4255-9ba1-f65ed86834c4 · outbound

This paper cites Efficient learning of kernel sizes for convolution layers of CNNs,.

Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach Efficient learning of kernel sizes for convolution layers of CNNs,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:32:58.139245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2bc0d019-617b-460a-866b-1febe02c8846 · outbound

This paper cites Spectral leakage and rethinking the kernel size in CNNs,.

Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach Spectral leakage and rethinking the kernel size in CNNs,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:32:58.123261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d9bc9112-2c68-4aba-ad1f-febcc0b28a57 · outbound

This paper cites Hyperparameter analysis of wide-kernel CNN architectures in industrial fault detection,.

Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach Hyperparameter analysis of wide-kernel CNN architectures in industrial fault detection,

Reference 6

Resolution: malformed identifier; no resolver link, observed 2026-08-07T00:32:57.566345Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 078bbce5-6cbd-42f6-af21-9c082dbae32c · outbound

This paper cites Unveiling the impact of kernel size on convolutional neural networks,.

Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach Unveiling the impact of kernel size on convolutional neural networks,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:32:58.108450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ce2e51aa-f093-46e1-ad93-aa0f39404612 · outbound

This paper cites A comprehensive literature review on convolutional neural networks,.

Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach A comprehensive literature review on convolutional neural networks,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:32:58.094723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 17149060-31a7-4df7-bac2-52ca25b38e94 · outbound

This paper cites Optimization and acceleration of convolutional neural networks,.

Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach Optimization and acceleration of convolutional neural networks,

Reference 9

Resolution: verified exact; doi, observed 2026-08-07T00:32:57.714146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 620bc0a2-32fb-4c34-bac1-a0fdd71aaedc · outbound

This paper cites Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification.

Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T00:32:57.583608Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d4470bd-9dc1-4ebd-86cd-332b6855bf23 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T00:32:57.588506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4766f14-3d77-490e-a062-eeccd75be50d · outbound

This paper cites Understanding the Effective Receptive Field in Deep Convolutional Neural Networks.

Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach Understanding the Effective Receptive Field in Deep Convolutional Neural Networks

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T00:32:57.593038Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fceb1770-61b7-4284-9f43-515149cab5e8 · outbound

This paper cites Neural Architecture Search with Reinforcement Learning.

Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach Neural Architecture Search with Reinforcement Learning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T00:32:57.598529Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c216eb1-ccab-4e48-9bf5-161216aec9de · outbound

This paper cites ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design.

Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T00:32:57.603345Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05d5ad91-9b43-4a3d-8ce0-f6c6ab52d5d0 · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T00:32:57.607575Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3408f68e-419a-42bb-b6e4-8969e435be24 · outbound

This paper cites Squeeze-and-Excitation Networks.

Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach Squeeze-and-Excitation Networks

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T00:32:57.611814Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80a42e27-b243-4c50-a034-0b8612ab4205 · outbound

This paper cites Visualizing and Understanding Convolutional Networks.

Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach Visualizing and Understanding Convolutional Networks

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T00:32:57.616381Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58c65dba-b410-4c0d-87b2-16af20602594 · outbound

This paper cites EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.

Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T00:32:57.620632Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3de8894d-8543-4642-882d-69b231e0e88d · outbound

This paper cites Searching for MobileNetV3.

Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach Searching for MobileNetV3

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T00:32:57.625491Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14d4ff99-9018-4b71-a5e9-f8a5546709bc · outbound

This paper cites CondConv: Conditionally parameterized convolutions for efficient inference,.

Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach CondConv: Conditionally parameterized convolutions for efficient inference,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:32:58.080913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 016a1c3a-3e85-4451-9c3e-154ec32e2157 · outbound

This paper cites ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices.

Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T00:32:57.638962Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 541fa2f9-4dcf-46f8-b2d9-b6cbdb84b172 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T00:32:57.643269Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6360b6ed-4cc8-4d97-9b72-42fbbf4d2ba7 · outbound

This paper cites Leveraging Implicit Expert Knowledge for Non-Circular Machine Learning in Sepsis Prediction.

Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach Leveraging Implicit Expert Knowledge for Non-Circular Machine Learning in Sepsis Prediction

Reference 23

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T00:32:57.836056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 07d2a5f9-6dda-4a1d-9344-c60474671131 · outbound

This paper cites Dynamic ReLU.

Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach Dynamic ReLU

Reference 24

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T00:32:57.816584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 30911187-5d9a-45ef-92d2-d480a3365ae5 · outbound

This paper cites GhostNet: More Features from Cheap Operations.

Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach GhostNet: More Features from Cheap Operations

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T00:32:57.655920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 137d9606-a629-4d8d-97c3-c3632aca9aae · outbound

This paper cites FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search.

Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T00:32:57.661128Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22e34414-6f34-42a9-8db5-116cb5ee265a · outbound

This paper cites Factorized convolutional neural networks,.

Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach Factorized convolutional neural networks,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:32:58.066622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ab1161a4-4368-40e0-b150-a662eace353d · outbound

This paper cites Multi-Scale Context Aggregation by Dilated Convolutions.

Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach Multi-Scale Context Aggregation by Dilated Convolutions

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T00:32:57.670266Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6151b3bd-0507-49a2-83f3-4e6b7e76c0c0 · outbound

This paper cites MobileNetV2: Inverted Residuals and Linear Bottlenecks.

Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach MobileNetV2: Inverted Residuals and Linear Bottlenecks

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T00:32:57.674799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e3df12a-64a2-45b9-aad2-1abd71fa8f0b · outbound

This paper cites NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size.

Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-08-07T00:32:57.744405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f21224d1-e95c-47e9-8b85-876d5c1dbcc3 · outbound

This paper cites CondConv: Conditionally Parameterized Convolutions for Efficient Inference.

Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach CondConv: Conditionally Parameterized Convolutions for Efficient Inference

Reference 2019

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T00:32:57.881193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

No inbound Pith citation observations are available.