Paper Citation Record · LEDGER

Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models

As of 9 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2507.15798.

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

pith.paper-citation-record.v1
2507.15798 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:28:08.989117Z

measured 39 of 39 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

39 of 39 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f2bd227e-86d8-4e76-b66a-f683a7788e27 · outbound

This paper cites Cloud computing and emerg- ing IT platforms: Vision, hype, and reality for delivering computing as the 5th utility.

Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models Cloud computing and emerg- ing IT platforms: Vision, hype, and reality for delivering computing as the 5th utility

Reference 1

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Observation 8bf89e82-39d7-4c1b-988f-c3df76d236f7 · outbound

This paper cites Toy Models of Superposition.

Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models Toy Models of Superposition

Reference 2

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This paper cites Learning Multiple Layers of Features from Tiny Images.

Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models Learning Multiple Layers of Features from Tiny Images

Reference 3

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This paper cites Imagenet: A large-scale hierar- chical image database.

Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models Imagenet: A large-scale hierar- chical image database

Reference 4

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Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models Softmax Linear Units

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Observation 44b374f4-cfd9-4f63-a739-3cdd6639ef3b · outbound

This paper cites Fake News Detection on Social Media using Geometric Deep Learning.

Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models Fake News Detection on Social Media using Geometric Deep Learning

Reference 6

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This paper cites Deep learning for social media analysis in crises situations.

Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models Deep learning for social media analysis in crises situations

Reference 7

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Observation 23c87b89-a134-404b-bd6e-9604da08a8dc · outbound

This paper cites A deep learning approach to drone moni- toring.

Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models A deep learning approach to drone moni- toring

Reference 8

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Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models Real-time drone detection using deep learn- ing approach

Reference 9

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This paper cites Overview of deep learning in medical imaging.

Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models Overview of deep learning in medical imaging

Reference 10

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This paper cites An overview of deep learning in medical imaging.

Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models An overview of deep learning in medical imaging

Reference 11

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Observation eae744cd-f551-434a-aa30-7402ce240341 · outbound

This paper cites AccurateYieldPredictionusingDeepLearning: challenges and recent developments on smart-viticulture.

Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models AccurateYieldPredictionusingDeepLearning: challenges and recent developments on smart-viticulture

Reference 12

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Observation f50cf937-cea4-4596-b09d-eea7cd9af634 · outbound

This paper cites Applying Knowledge Distillation on Pre-Trained Model for Early Grapevine Detection.

Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models Applying Knowledge Distillation on Pre-Trained Model for Early Grapevine Detection

Reference 13

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This paper cites Generative adversarial networks: introduction and outlook.

Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models Generative adversarial networks: introduction and outlook

Reference 14

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This paper cites Generative adversarial networks.

Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models Generative adversarial networks

Reference 15

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Observation 6bb3f506-6fc2-4006-9885-3b2a621fd3e2 · outbound

This paper cites Convolutional networks for images, speech, and time series.

Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models Convolutional networks for images, speech, and time series

Reference 16

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Observation 44529bd2-f33e-4de4-b3bc-0a405ba57aff · outbound

This paper cites Attention is all you need.

Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models Attention is all you need

Reference 17

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This paper cites Improvingtheperformanceoffogcomputingthroughtheuseofdatalocality.

Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models Improvingtheperformanceoffogcomputingthroughtheuseofdatalocality

Reference 18

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This paper cites Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing.

Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing

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This paper cites Deep Learning for Edge Computing Applications: A State-of-the-Art Survey.

Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models Deep Learning for Edge Computing Applications: A State-of-the-Art Survey

Reference 20

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Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models Edge AI: a survey

Reference 21

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Observation 4338331b-4f5b-41b4-8ca5-fc58cae69787 · outbound

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Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models Edge AI: A taxonomy, systematic review and future directions

Reference 22

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Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer

Reference 23

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Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models MobileViTv3: Mobile-Friendly Vision Transformer with Simple and Effective Fusion of Local, Global and Input Features

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Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 25

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Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models Mobilenetv2: Inverted residuals and linear bottlenecks

Reference 26

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Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models Searching for mobilenetv3

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This paper cites EfficientNet: Rethinking Model Scaling for Convolutional Neural Net- works.

Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models EfficientNet: Rethinking Model Scaling for Convolutional Neural Net- works

Reference 28

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Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models EfficientNetV2: Smaller Models and Faster Training

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Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models Mehta and M

Reference 30

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Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models A convnet for the 2020s

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Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models Rethinking bottleneck structure for effi- cient mobile network design

Reference 32

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This paper cites Efficientvit: Memory efficient vision transformer with cascaded group attention.

Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models Efficientvit: Memory efficient vision transformer with cascaded group attention

Reference 33

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Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models Sparse Attention with Linear Units

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Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models Exact solutions to the nonlinear dynamics of learning in deep linear neural networks

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Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models Levit: a visiontransformerinconvnet’sclothingforfasterinference

Reference 36

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Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models Ghostnet: More features from cheap operations

Reference 37

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Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models RepGhost: A Hardware-Efficient Ghost Module via Re-parameterization

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Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models Separable Self-attention for Mobile Vision Transformers

Reference 2022

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Pith citing papers

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