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Paper Citation Record · LEDGER

Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts

As of 16 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2411.09199.

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

pith.paper-citation-record.v1
2411.09199 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T21:02:00.796641Z

measured 36 of 36 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

36 of 36 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 851d3ca5-39f6-4114-b1f5-ca5d3cfd06cb · outbound

This paper cites How does loss function affect generalization per- formance of deep learning? application to human age esti- mation.

Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts How does loss function affect generalization per- formance of deep learning? application to human age esti- mation

Reference 1

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Observation 65761ead-f10a-4054-80bc-b6a98652f7ce · outbound

This paper cites Rage: Robust age estimation through subject anchoring with consistency regularisation.

Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts Rage: Robust age estimation through subject anchoring with consistency regularisation

Reference 2

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Observation ea629c12-1a60-47eb-aa5e-a8192599a190 · outbound

This paper cites Theoretical un- derstanding of the information flow on continual learning performance.

Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts Theoretical un- derstanding of the information flow on continual learning performance

Reference 3

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Observation 30f783be-d521-48f8-9ecd-9e015061f452 · outbound

This paper cites Invariant Risk Minimization.

Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts Invariant Risk Minimization

Reference 4

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Observation 43e4e9a6-0e45-4dc1-8000-1076262a4d50 · outbound

This paper cites Deeppicar: A low-cost deep neural network- based autonomous car.

Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts Deeppicar: A low-cost deep neural network- based autonomous car

Reference 5

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Observation e6204d6e-cc98-4340-a920-c421622a8307 · outbound

This paper cites The elastic lottery ticket hypothesis.

Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts The elastic lottery ticket hypothesis

Reference 6

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Observation dafb0a31-ef23-4dd0-b3e4-cc76624bc559 · outbound

This paper cites Urban artificial intelligence: From au- tomation to autonomy in the smart city.

Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts Urban artificial intelligence: From au- tomation to autonomy in the smart city

Reference 7

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Observation 6ea90886-80b5-455f-8d26-7bba41fb6cf2 · outbound

This paper cites A winning hand: Compress- ing deep networks can improve out-of-distribution robust- ness.

Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts A winning hand: Compress- ing deep networks can improve out-of-distribution robust- ness

Reference 8

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Observation c77e2fdc-531c-4643-b7fd-6f2911425a1d · outbound

This paper cites Depgraph: Towards any structural pruning.

Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts Depgraph: Towards any structural pruning

Reference 9

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Observation 4bf44344-61e9-4002-b054-54cebd5847b7 · outbound

This paper cites Pruning Neural Networks at Initialization: Why are We Missing the Mark?.

Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts Pruning Neural Networks at Initialization: Why are We Missing the Mark?

Reference 10

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Observation c4603296-1862-4cd3-a21c-1c6c83e29f31 · outbound

This paper cites Mit ad- vanced vehicle technology study: Large-scale naturalistic driving study of driver behavior and interaction with automa- tion.

Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts Mit ad- vanced vehicle technology study: Large-scale naturalistic driving study of driver behavior and interaction with automa- tion

Reference 11

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Observation bec35519-5992-43eb-b82b-9e4046cb22d9 · outbound

This paper cites The State of Sparsity in Deep Neural Networks.

Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts The State of Sparsity in Deep Neural Networks

Reference 12

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Observation f0702398-0013-4f2a-bb5e-9159605885f5 · outbound

This paper cites A unified view of label shift estimation.Ad- vances in Neural Information Processing Systems, 33:3290– 3300, 2020.

Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts A unified view of label shift estimation.Ad- vances in Neural Information Processing Systems, 33:3290– 3300, 2020

Reference 13

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Observation 5596f902-2f3a-47f4-b85d-aaf1297a88da · outbound

This paper cites A survey of uncertainty in deep neural networks.

Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts A survey of uncertainty in deep neural networks

Reference 14

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Observation ea678905-1507-4836-9d02-9511be8be601 · outbound

This paper cites Ghostnet: More features from cheap operations.

Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts Ghostnet: More features from cheap operations

Reference 15

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Observation 288e2ace-ab9b-4e48-872a-cd864ea2356f · outbound

This paper cites Deep residual learning for image recognition.

Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts Deep residual learning for image recognition

Reference 16

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Observation f8c3b9bc-bddc-4eea-9b2c-dd25859e4277 · outbound

This paper cites Benchmarking neu- ral network robustness to common corruptions and perturba- tions.

Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts Benchmarking neu- ral network robustness to common corruptions and perturba- tions

Reference 17

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Observation 38dcd00b-bc63-4ad9-b729-387f78243291 · outbound

This paper cites What Do Compressed Deep Neural Networks Forget?.

Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts What Do Compressed Deep Neural Networks Forget?

Reference 18

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Observation 877d72d4-4383-4dbe-824f-42c71009345c · outbound

This paper cites Wilds: A benchmark of in-the- wild distribution shifts.

Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts Wilds: A benchmark of in-the- wild distribution shifts

Reference 19

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Observation c91c88fb-7ec7-4d65-a13f-56864baab356 · outbound

This paper cites Out-of-distribution general- ization via risk extrapolation (rex).

Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts Out-of-distribution general- ization via risk extrapolation (rex)

Reference 20

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Observation 5e3a553d-cd40-400e-8fb0-80ce04f12250 · outbound

This paper cites Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution.

Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution

Reference 21

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Observation 41ed44d2-c8ec-4876-9504-11d4b53ebbfa · outbound

This paper cites Layer-adaptive sparsity for the Magnitude-based Pruning.

Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts Layer-adaptive sparsity for the Magnitude-based Pruning

Reference 22

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Observation 24a94938-3c12-4a9a-9d95-89ae95690471 · outbound

This paper cites SNIP: Single-shot Network Pruning based on Connection Sensitivity.

Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts SNIP: Single-shot Network Pruning based on Connection Sensitivity

Reference 23

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Observation 03bce534-9150-485e-948c-6ada4b629b77 · outbound

This paper cites Ea- gleeye: Fast sub-net evaluation for efficient neural network pruning.

Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts Ea- gleeye: Fast sub-net evaluation for efficient neural network pruning

Reference 24

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Observation cb45b2a7-3142-43c2-afa4-0a3e9d99ba63 · outbound

This paper cites Do we actually need dense over- parameterization? in-time over-parameterization in sparse training.

Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts Do we actually need dense over- parameterization? in-time over-parameterization in sparse training

Reference 25

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Observation 33a051da-3872-4474-8f15-c8d8dac92115 · outbound

This paper cites Towards robust neural networks via random self- ensemble.

Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts Towards robust neural networks via random self- ensemble

Reference 26

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Observation 548c58b9-3bea-4ec7-994e-5c0fbc176631 · outbound

This paper cites huyvnphan/pytorch cifar10, January 2021.

Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts huyvnphan/pytorch cifar10, January 2021

Reference 27

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

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Observation 7dd80c26-263f-48b2-b6a6-544f9f987ce4 · outbound

This paper cites Understanding and Mitigating the Tradeoff between Robustness and Accuracy, 2022.

Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts Understanding and Mitigating the Tradeoff between Robustness and Accuracy, 2022

Reference 28

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Observation c9c41ee8-a003-4100-b3ec-a394296c084b · outbound

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

Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 29

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Observation 2b35c223-73e0-4ba1-b05d-5bae1b34788c · outbound

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Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts Neural machine translation: A review

Reference 30

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

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Observation 5cfb2166-008b-42b8-b48d-33ae3a4b909f · outbound

This paper cites Test-time training with self- supervision for generalization under distribution shifts.

Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts Test-time training with self- supervision for generalization under distribution shifts

Reference 31

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Observation 0fa20305-47df-4d99-9e39-e395ccd65bb5 · outbound

This paper cites Pruning neural networks without any data by iter- atively conserving synaptic flow.

Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts Pruning neural networks without any data by iter- atively conserving synaptic flow

Reference 32

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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.

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Observation 218bad80-f0f1-4414-a2cd-7c7b8b6047bc · outbound

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Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts Neural Pruning via Growing Regularization

Reference 33

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Observation 174a10bb-a48d-406e-b813-e44c0b13d388 · outbound

This paper cites Learning structured sparsity in deep neural net- works.

Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts Learning structured sparsity in deep neural net- works

Reference 34

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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=2026-08-12T21:02:00.785982Z digest=sha256:3a775dbb8b62e516660f6f069d11e41f11fa844d83ed21adda4e981faae073dd

Observation d9abd18e-3ed6-46a0-b855-3d6879575289 · outbound

This paper cites User scheduling for het- erogeneous multiuser mimo systems: A subspace viewpoint.

Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts User scheduling for het- erogeneous multiuser mimo systems: A subspace viewpoint

Reference 35

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raw_fallback, observed 2026-08-12T21:02:01.027684Z

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source=pdf_text observed=2026-08-12T21:02:00.791152Z digest=sha256:64781f656378e94283086c8fe0f944aa0af510db4759ec0b4c73eeb29d9da8c8

Observation 07ba2e5e-082f-4648-b19e-c56ce0e039b4 · outbound

This paper cites A fre- quency pattern mining model based on deep neural network for real-time classification of heart conditions.

Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts A fre- quency pattern mining model based on deep neural network for real-time classification of heart conditions

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:02:01.008762Z

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source=pdf_text observed=2026-08-12T21:02:00.796641Z digest=sha256:dc88f8fc920a0c4c686342065bad8e0a76a8f6a472c5677341eb0578bec80af2

Pith citing papers

No inbound Pith citation observations are available.