Paper Citation Record · LEDGER

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks

As of 8 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2506.07188.

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

pith.paper-citation-record.v1
2506.07188 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:46:32.476804Z

measured 40 of 40 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

40 of 40 outbound references displayed

verified exact1
verified fuzzy33
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ba4b8fda-7ad8-4ace-a8a6-fc2f1ddf36d2 · outbound

This paper cites Towards explainable semantic segmentation for autonomous driving systems by multi-scale variational attention.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Towards explainable semantic segmentation for autonomous driving systems by multi-scale variational attention

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:33.076678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 45501c8a-234c-4be9-87ad-a180054c8ff9 · outbound

This paper cites ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T05:46:32.316769Z

Source-reported events for the cited work

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Observation 196d43cf-96d5-47f8-8bce-85428a6accdf · outbound

This paper cites Decoupled greedy learning of cnns.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Decoupled greedy learning of cnns

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:33.062929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation aa5220d6-1c07-4f19-9f9a-d05af216a60b · outbound

This paper cites Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, Xin Zhang, Jake Zhao, and Karol Zieba.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, Xin Zhang, Jake Zhao, and Karol Zieba

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:33.049409Z

Source-reported events for the cited work

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Observation 9746f565-b501-47bb-9a74-728b983ab0c9 · outbound

This paper cites Cambridge University Press, 2004.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Cambridge University Press, 2004

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:33.035721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 559a4405-cd3f-4f6f-98dd-8e7c6069f115 · outbound

This paper cites an unresolved cited work.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:46:33.021675Z

Source-reported events for the cited work

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Observation 24bc4e7a-2d7d-4f7c-96bd-181eea1ab73a · outbound

This paper cites Vatld: A visual analytics system to assess, understand and im- prove traffic light detection.IEEE Transactions on Visu- alization and Computer Graphics, 27(2):261–271, 2021.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Vatld: A visual analytics system to assess, understand and im- prove traffic light detection.IEEE Transactions on Visu- alization and Computer Graphics, 27(2):261–271, 2021

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:33.008374Z

Source-reported events for the cited work

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Observation 502139fd-8d88-427d-b258-ceb1b9d423ef · outbound

This paper cites Emma: End-to- end multimodal model for autonomous driving, 2024.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Emma: End-to- end multimodal model for autonomous driving, 2024

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:32.994243Z

Source-reported events for the cited work

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Observation 025e08f2-6db3-4862-b4fc-623b3240ba84 · outbound

This paper cites Decoupled neural interfaces using synthetic gradients, 2017.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Decoupled neural interfaces using synthetic gradients, 2017

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:32.980730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 29803c24-ff9a-4d0f-9642-1333bb5d69bf · outbound

This paper cites Layercam: Exploring hierarchical class activation maps for localization.IEEE Transactions on Image Processing, 30:5875–5888, 2021.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Layercam: Exploring hierarchical class activation maps for localization.IEEE Transactions on Image Processing, 30:5875–5888, 2021

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:32.966551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e942e900-db93-45ca-bd27-78e0c39928b4 · outbound

This paper cites Intention-aware interactive transformer for real-time ve- hicle trajectory prediction in dense traffic.Transportation Research Record, 2677(3):946–960, 2023.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Intention-aware interactive transformer for real-time ve- hicle trajectory prediction in dense traffic.Transportation Research Record, 2677(3):946–960, 2023

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:32.952696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cace9a1c-24c5-46bb-a38a-334f7e99031d · outbound

This paper cites Diffstack: A differentiable and modular control stack for autonomous vehicles.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Diffstack: A differentiable and modular control stack for autonomous vehicles

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:32.938215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0f3c9b6b-5170-4543-8e5c-aba49647f6d7 · outbound

This paper cites Boosting monocular 3d object detection with object-centric auxiliary depth supervision.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Boosting monocular 3d object detection with object-centric auxiliary depth supervision

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:32.923674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8a51f9e2-d550-4f24-8a07-78fda4e39941 · outbound

This paper cites Explainable action prediction through self-supervision on scene graphs.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Explainable action prediction through self-supervision on scene graphs

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:32.910446Z

Source-reported events for the cited work

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Observation e803a62f-e665-4fe5-bb7c-8e4fe494f9cb · outbound

This paper cites an unresolved cited work.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Unresolved cited work

Reference 15

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:46:32.896279Z

Source-reported events for the cited work

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Observation 877e1b8f-5cf5-421f-abb4-ec76695dafe2 · outbound

This paper cites Albrecht.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Albrecht

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:32.882792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f9668c65-929f-4a5e-a0eb-dcd950d5a353 · outbound

This paper cites Exploring intermediate representation for monocular vehicle pose estimation.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Exploring intermediate representation for monocular vehicle pose estimation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:32.869341Z

Source-reported events for the cited work

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Observation 0b08dc72-7a5b-4c74-afa4-9da73887de60 · outbound

This paper cites Od-xai: Explainable ai-based semantic object detection for au- tonomous vehicles.Applied Sciences, 12(11), 2022.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Od-xai: Explainable ai-based semantic object detection for au- tonomous vehicles.Applied Sciences, 12(11), 2022

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:32.853492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation df1bff7e-7bc8-40c9-8a6a-69073f879789 · outbound

This paper cites Deep learning techniques: an overview.Advanced Machine Learning Technologies and Applications: Proceedings of AMLTA 2020, pages 599–608, 2021.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Deep learning techniques: an overview.Advanced Machine Learning Technologies and Applications: Proceedings of AMLTA 2020, pages 599–608, 2021

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:32.837392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8cf7f608-0438-41ef-9125-8916a7dbfd24 · outbound

This paper cites Layerwise Knowledge Extraction from Deep Convolutional Networks.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Layerwise Knowledge Extraction from Deep Convolutional Networks

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:46:32.534225Z

Source-reported events for the cited work

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Observation b931a72a-3569-41ea-b148-c53709bc6c7b · outbound

This paper cites Clip-bevformer: Enhancing multi-view image-based bev detector with ground truth flow, 2024.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Clip-bevformer: Enhancing multi-view image-based bev detector with ground truth flow, 2024

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:32.822205Z

Source-reported events for the cited work

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Observation 8b907baa-f2dc-4262-aa78-8d8a74005d24 · outbound

This paper cites Decoupled neural network training with re-computation and weight predic- tion.PloS one, 18(2):e0276427, 2023.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Decoupled neural network training with re-computation and weight predic- tion.PloS one, 18(2):e0276427, 2023

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:32.808295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation df8b642f-c5fc-47c6-8224-02482cb93f6d · outbound

This paper cites Roth, and Horst Bischof.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Roth, and Horst Bischof

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:32.793604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a8946995-7f12-40d4-81fc-d2fd3fef03d0 · outbound

This paper cites Roth, and Horst Bischof.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Roth, and Horst Bischof

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:32.778450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bee5eb4d-102c-4a88-a9ba-a01783436dbd · outbound

This paper cites Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:32.763896Z

Source-reported events for the cited work

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Observation a2206485-947a-4d5e-bf94-79bcd7ec01f5 · outbound

This paper cites Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T05:46:32.416398Z

Source-reported events for the cited work

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Observation d5039d3c-13ea-4629-8a15-f3889871f083 · outbound

This paper cites A survey of end-to-end driving: Architectures and training meth- ods.IEEE Transactions on Neural Networks and Learning Systems, 33(4):1364–1384, 2022.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks A survey of end-to-end driving: Architectures and training meth- ods.IEEE Transactions on Neural Networks and Learning Systems, 33(4):1364–1384, 2022

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:32.749476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1bd42a1c-ec93-434e-8b4d-aa2b75191511 · outbound

This paper cites Lane-change inten- tion prediction of surrounding vehicles using bilstm-crf models with rule embedding.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Lane-change inten- tion prediction of surrounding vehicles using bilstm-crf models with rule embedding

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:32.735173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fd8ad4c7-0f0e-4015-b2a7-6710b1b5a38f · outbound

This paper cites Dataset distillation with neural characteristic function: A minmax perspective, 2025.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Dataset distillation with neural characteristic function: A minmax perspective, 2025

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:32.720733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 98557afa-9ef5-4824-adb1-30104b90cac6 · outbound

This paper cites Wong, Zhenguo Li, and Hengshuang Zhao.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Wong, Zhenguo Li, and Hengshuang Zhao

Reference 30

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Source-reported events for the cited work

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Observation dcffa64d-13a2-45da-91ae-27cd651db715 · outbound

This paper cites Bev- former v2: Adapting modern image backbones to bird’s- eye-view recognition via perspective supervision.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Bev- former v2: Adapting modern image backbones to bird’s- eye-view recognition via perspective supervision

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:32.695324Z

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Observation e1e5661e-b8ac-4a04-be27-ca0edc4b953e · outbound

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Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Unresolved cited work

Reference 32

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:46:32.681605Z

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Observation dcde9dbb-98d8-4faa-bd0c-8533832266ac · outbound

This paper cites Sun-glare region recognition using visual explanations for traffic light detection.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Sun-glare region recognition using visual explanations for traffic light detection

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:32.668007Z

Source-reported events for the cited work

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Observation 1d48fa74-137f-487a-8633-15ac2333fb5d · outbound

This paper cites Understanding neural networks through deep visualization, 2015.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Understanding neural networks through deep visualization, 2015

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:32.654097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fb250792-690e-41fc-a616-6608667091c3 · outbound

This paper cites Visualizing and un- derstanding convolutional networks, 2013.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Visualizing and un- derstanding convolutional networks, 2013

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:32.639372Z

Source-reported events for the cited work

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Observation baf38cb4-be14-41de-8625-b3dcef5d2bc1 · outbound

This paper cites Fea- ture map convergence evaluation for functional module, 2024.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Fea- ture map convergence evaluation for functional module, 2024

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:32.623860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3483a441-df0d-4e54-b67f-23f989b848f9 · outbound

This paper cites Shap- cam: Visual explanations for convolutional neural net- works based on shapley value.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Shap- cam: Visual explanations for convolutional neural net- works based on shapley value

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:32.609513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 33d319e7-4334-49ff-bf40-ef7100d8499a · outbound

This paper cites Genad: Generative end-to-end autonomous driving.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Genad: Generative end-to-end autonomous driving

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:32.594626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 18e19640-f038-4106-9335-ec37e71263e0 · outbound

This paper cites Learning deep features for dis- criminative localization.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Learning deep features for dis- criminative localization

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:32.580039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c50fda4e-52d3-4a07-b8ca-c4ac319fc251 · outbound

This paper cites Fully decoupled neural network learning using delayed gradients.IEEE transactions on neural networks and learning systems, 33(10):6013–6020, 2021.

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Fully decoupled neural network learning using delayed gradients.IEEE transactions on neural networks and learning systems, 33(10):6013–6020, 2021

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:32.565879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

No inbound Pith citation observations are available.