Paper Citation Record · LEDGER

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs

As of 9 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 2 inbound Pith citation observations for arXiv:2507.05810.

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

pith.paper-citation-record.v1
2507.05810 v1

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:22:35.171101Z

measured 59 of 59 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T18:02:43.873742Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-19T14:42:37.643761Z

Reference resolution

57 of 57 outbound references displayed

verified exact3
verified fuzzy45
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 37e5a883-610e-4a96-8f17-40435fd2f165 · outbound

This paper cites Sanity checks for saliency maps.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Sanity checks for saliency maps

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:35.889022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fa657c61-5419-485c-b012-575ff851eb82 · outbound

This paper cites Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai.Information fu- sion, 58:82–115, 2020.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai.Information fu- sion, 58:82–115, 2020

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:35.875316Z

Source-reported events for the cited work

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Observation cb0ffcde-5394-4846-8c48-601a6d81e4a6 · outbound

This paper cites Concept Gradient: Concept-based Interpretation Without Linear Assumption.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Concept Gradient: Concept-based Interpretation Without Linear Assumption

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-08-06T19:22:35.307771Z

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Observation 459bbbae-0a85-4c18-990a-a791e87f29d3 · outbound

This paper cites Network dissection: Quantifying in- terpretability of deep visual representations.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Network dissection: Quantifying in- terpretability of deep visual representations

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:35.857897Z

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Observation 1ac71d5a-72fa-441c-b808-99d141b9a184 · outbound

This paper cites Under- standing the role of individual units in a deep neural net- work.Proceedings of the National Academy of Sciences, 117(48):30071–30078, 2020.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Under- standing the role of individual units in a deep neural net- work.Proceedings of the National Academy of Sciences, 117(48):30071–30078, 2020

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:35.843721Z

Source-reported events for the cited work

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Observation c14e29d3-4c8a-4c2d-a5b9-d1cb60f3f0fc · outbound

This paper cites Greybox XAI: A Neural-Symbolic learning framework to produce inter- pretable predictions for image classification.Knowledge- Based Systems, 258:109947, 2022.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Greybox XAI: A Neural-Symbolic learning framework to produce inter- pretable predictions for image classification.Knowledge- Based Systems, 258:109947, 2022

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:35.829486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e2f8e987-ba01-4567-b73f-a8f6b1aca81a · outbound

This paper cites Support-vector networks.Machine Learning, 20(3):273–297, 1995.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Support-vector networks.Machine Learning, 20(3):273–297, 1995

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:35.814983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 27f22578-712e-4113-84b5-1e5dbc555768 · outbound

This paper cites Imagenet: A large-scale hierarchical im- age database.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Imagenet: A large-scale hierarchical im- age database

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:35.801752Z

Source-reported events for the cited work

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Observation 8a843a7a-f3f8-4933-ab5d-aa534c1661ab · outbound

This paper cites Bert: Pre-training of deep bidirec- tional transformers for language understanding.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Bert: Pre-training of deep bidirec- tional transformers for language understanding

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:35.787996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 050a518d-4cc2-4b5b-87a8-7d0eb532b626 · outbound

This paper cites an unresolved cited work.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:22:35.775293Z

Source-reported events for the cited work

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Observation 3cb2295f-313a-4df8-825f-e982cc435e90 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale, 2021.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs An image is worth 16x16 words: Transformers for image recognition at scale, 2021

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T19:22:34.987512Z

Source-reported events for the cited work

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Observation edb98039-4776-41bd-aaca-6ea9d0e49e12 · outbound

This paper cites Explainable ai (xai): Core ideas, techniques, and solutions.ACM Computing Surveys, 55(9):1–33, 2023.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Explainable ai (xai): Core ideas, techniques, and solutions.ACM Computing Surveys, 55(9):1–33, 2023

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:35.753158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5d38dcd9-d363-4529-9990-8e98a8b0d5f3 · outbound

This paper cites Visualizing higher-layer features of a deep network.University of Montreal, 1341(3):1, 2009.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Visualizing higher-layer features of a deep network.University of Montreal, 1341(3):1, 2009

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:35.739649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9324eb55-064c-45e9-bd07-24275aef66e9 · outbound

This paper cites A holistic approach to unify- ing automatic concept extraction and concept importance estimation.Advances in Neural Information Processing Systems, 36:54805–54818, 2023.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs A holistic approach to unify- ing automatic concept extraction and concept importance estimation.Advances in Neural Information Processing Systems, 36:54805–54818, 2023

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:35.727142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8ee67227-376c-4833-aca1-7bc496e676c1 · outbound

This paper cites Craft: Concept recursive activation factorization for explainability.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Craft: Concept recursive activation factorization for explainability

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:35.704390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0e08c241-2f3f-474d-a546-02e1ab2cce50 · outbound

This paper cites Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T19:22:35.003839Z

Source-reported events for the cited work

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Observation 2fbbce57-4cc0-4868-9b40-b7ba44a835aa · outbound

This paper cites Interpretable explana- tions of black boxes by meaningful perturbation.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Interpretable explana- tions of black boxes by meaningful perturbation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:35.694203Z

Source-reported events for the cited work

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Observation f7866416-f271-45a5-8abf-f34d096484ad · outbound

This paper cites Regression towards mediocrity in hered- itary stature.The Journal of the Anthropological Institute of Great Britain and Ireland, 15:246–263, 1886.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Regression towards mediocrity in hered- itary stature.The Journal of the Anthropological Institute of Great Britain and Ireland, 15:246–263, 1886

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:35.683010Z

Source-reported events for the cited work

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Observation abdd80c4-fe57-4489-9741-29c4f49d1b1c · outbound

This paper cites In- terpretation of neural networks is fragile.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs In- terpretation of neural networks is fragile

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:35.670232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 64e85839-3440-4a88-bdbf-c3409c1dfa6f · outbound

This paper cites Towards automatic concept-based explana- tions.Advances in neural information processing sys- tems, 32, 2019.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Towards automatic concept-based explana- tions.Advances in neural information processing sys- tems, 32, 2019

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:35.657746Z

Source-reported events for the cited work

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Observation f4d8a5fb-c927-4b05-a462-eb10ccd0efcd · outbound

This paper cites Addressing leakage in concept bottleneck models.Ad- vances in Neural Information Processing Systems, 35: 23386–23397, 2022.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Addressing leakage in concept bottleneck models.Ad- vances in Neural Information Processing Systems, 35: 23386–23397, 2022

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:35.644412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 09bbd284-13cb-4265-9387-97faeda576bb · outbound

This paper cites Deep residual learning for image recognition.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Deep residual learning for image recognition

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:35.631236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e72fb378-532c-490f-ad9b-0513178c2248 · outbound

This paper cites Nat- ural language descriptions of deep visual features.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Nat- ural language descriptions of deep visual features

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:35.619387Z

Source-reported events for the cited work

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Observation ac0093ab-3fde-4ab7-9e60-cebd9539f1f8 · outbound

This paper cites Rashid, Anisa Rula, Lukas Schmelzeisen, Juan Sequeda, Steffen Staab, and Antoine Zimmermann.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Rashid, Anisa Rula, Lukas Schmelzeisen, Juan Sequeda, Steffen Staab, and Antoine Zimmermann

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:35.607633Z

Source-reported events for the cited work

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Observation 67e6c53c-1b0b-4c54-8802-d25a23a2e722 · outbound

This paper cites Densely connected convolutional networks.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Densely connected convolutional networks

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:35.597591Z

Source-reported events for the cited work

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Observation 6cf176ef-459c-4914-ab7f-b69c7cd5dccc · outbound

This paper cites An intuitive explanation of sparse autoencoders for llm interpretability.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs An intuitive explanation of sparse autoencoders for llm interpretability

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:35.587486Z

Source-reported events for the cited work

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Observation 5a7c4740-4e70-448a-8d27-e61bceeffe1d · outbound

This paper cites Seven-point checklist and skin lesion classification using multitask multimodal neural nets.IEEE Journal of Biomedical and Health In- formatics, 23(2):538–546, 2019.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Seven-point checklist and skin lesion classification using multitask multimodal neural nets.IEEE Journal of Biomedical and Health In- formatics, 23(2):538–546, 2019

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:35.577148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 334329fd-df89-410b-ae11-ee05b391bb81 · outbound

This paper cites CLIP-QDA: An Explainable Concept Bottleneck Model.Transactions on Machine Learning Research, 2023.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs CLIP-QDA: An Explainable Concept Bottleneck Model.Transactions on Machine Learning Research, 2023

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:35.564879Z

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Observation 016e3ed6-874c-47b5-8b5f-d8d8bdf2198a · outbound

This paper cites Explainability for Vision Foundation Models: A Survey.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Explainability for Vision Foundation Models: A Survey

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-08-06T19:22:35.283207Z

Source-reported events for the cited work

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Observation b04d6634-39c3-4097-8652-bee399d0aba9 · outbound

This paper cites Undoing the dam- age of dataset bias.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Undoing the dam- age of dataset bias

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:35.553542Z

Source-reported events for the cited work

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Observation 8e30e703-6046-444c-be3c-2ee5fdeb0e30 · outbound

This paper cites Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV).

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)

Reference 31

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Source-reported events for the cited work

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Observation d8b4be55-74d0-42c4-b980-eeb130edde50 · outbound

This paper cites Inter- pretability beyond feature attribution: Quantitative test- ing with concept activation vectors (tcav), 2018.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Inter- pretability beyond feature attribution: Quantitative test- ing with concept activation vectors (tcav), 2018

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:35.542672Z

Source-reported events for the cited work

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Observation 1d809e00-1949-4db4-947a-005e35f89966 · outbound

This paper cites Concept bottleneck models.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Concept bottleneck models

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:35.531802Z

Source-reported events for the cited work

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Observation 54530016-bf4a-4c10-889d-4caa71aba7eb · outbound

This paper cites Imagenet classification with deep convolutional neu- ral networks.Communications of the ACM, 60(6):84–90,.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Imagenet classification with deep convolutional neu- ral networks.Communications of the ACM, 60(6):84–90,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:35.520688Z

Source-reported events for the cited work

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Observation 29926d92-9c9e-4806-80fc-79496df8df9e · outbound

This paper cites Monumai: Dataset, deep learning pipeline and citizen science based app for monumental heritage taxonomy and classification.Neurocomputing, 420:266– 280, 2021.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Monumai: Dataset, deep learning pipeline and citizen science based app for monumental heritage taxonomy and classification.Neurocomputing, 420:266– 280, 2021

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:35.510333Z

Source-reported events for the cited work

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Observation 68a6465a-8dcf-4b7d-ac69-92aeded309ec · outbound

This paper cites Promises and Pitfalls of Black-Box Concept Learning Models.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Promises and Pitfalls of Black-Box Concept Learning Models

Reference 36

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Source-reported events for the cited work

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Observation fb034595-95d7-4694-b571-a297d1834d6e · outbound

This paper cites Progress measures for grokking via mechanistic interpretability.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Progress measures for grokking via mechanistic interpretability

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:35.499842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e7ba7b52-a33a-4bc4-a5fe-16c638c60b5b · outbound

This paper cites Clip-dissect: Automatic description of neuron representations in deep vision networks.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Clip-dissect: Automatic description of neuron representations in deep vision networks

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:35.489724Z

Source-reported events for the cited work

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Observation 14db8bff-d5db-4faa-bc42-8bb72a4def77 · outbound

This paper cites Nguyen, and Tsui-Wei Weng.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Nguyen, and Tsui-Wei Weng

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:35.478805Z

Source-reported events for the cited work

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Observation f7b125df-b7aa-475c-b8a4-e46fb4b3fd9d · outbound

This paper cites Feature visualization.Distill, 2(11):e7, 2017.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Feature visualization.Distill, 2(11):e7, 2017

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:35.467213Z

Source-reported events for the cited work

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Observation fd65fc2d-c52b-421d-9347-2e700ab3de08 · outbound

This paper cites Zoom in: An introduction to circuits.Distill, 2020.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Zoom in: An introduction to circuits.Distill, 2020

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:35.455038Z

Source-reported events for the cited work

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Observation 5210fe21-a686-48d4-b2e0-a5c41a3a1771 · outbound

This paper cites Discover: making vision networks interpretable via competition and dissection.Advances in Neural Information Process- ing Systems, 36:27063–27078, 2023.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Discover: making vision networks interpretable via competition and dissection.Advances in Neural Information Process- ing Systems, 36:27063–27078, 2023

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:35.444311Z

Source-reported events for the cited work

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Observation a7bf7216-d7eb-4bb4-ab2b-b57f55b363e2 · outbound

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Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Unresolved cited work

Reference 43

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Observation 8a7be78b-fb48-4589-b09f-4f0c76aa5875 · outbound

This paper cites Learning transferable visual models from natural language supervision.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Learning transferable visual models from natural language supervision

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:35.421390Z

Source-reported events for the cited work

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Observation bbf596fe-2b41-4920-91f6-4e2c45f27435 · outbound

This paper cites Learning Transferable Visual Models From Natural Language Supervision.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Learning Transferable Visual Models From Natural Language Supervision

Reference 45

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Source-reported events for the cited work

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Observation 17899452-d702-4cc5-bd4e-5a5baaed8d25 · outbound

This paper cites Explainable ai (xai): A systematic meta-survey of current challenges and future opportunities.Knowledge-based systems, 263: 110273, 2023.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Explainable ai (xai): A systematic meta-survey of current challenges and future opportunities.Knowledge-based systems, 263: 110273, 2023

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:35.410765Z

Source-reported events for the cited work

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Observation e8a51326-e62d-4781-a90c-d7f3182417aa · outbound

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

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-06T19:22:35.128485Z

Source-reported events for the cited work

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

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 48

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This paper cites Don’t judge an object by its context: Learning to overcome contextual bias.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Don’t judge an object by its context: Learning to overcome contextual bias

Reference 49

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This paper cites Vlg- cbm: Training concept bottleneck models with vision- language guidance.Advances in Neural Information Pro- cessing Systems, 37:79057–79094, 2024.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Vlg- cbm: Training concept bottleneck models with vision- language guidance.Advances in Neural Information Pro- cessing Systems, 37:79057–79094, 2024

Reference 50

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This paper cites Going deeper with convolutions.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Going deeper with convolutions

Reference 51

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This paper cites Efficientnet: Rethinking model scaling for convolutional neural networks.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Efficientnet: Rethinking model scaling for convolutional neural networks

Reference 52

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This paper cites Unbiased look at dataset bias.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Unbiased look at dataset bias

Reference 53

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This paper cites Knowledge graphs for empirical concept retrieval.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Knowledge graphs for empirical concept retrieval

Reference 54

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This paper cites Aggregated residual transformations for deep neural networks.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Aggregated residual transformations for deep neural networks

Reference 55

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This paper cites Language in a bottle: Language model guided concept bottlenecks for interpretable image classification.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Language in a bottle: Language model guided concept bottlenecks for interpretable image classification

Reference 56

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This paper cites Visualizing and understanding convolutional networks.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Visualizing and understanding convolutional networks

Reference 57

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Pith citing papers

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A Geometric Unification of Concept Learning with Concept Cones cites this paper.

A Geometric Unification of Concept Learning with Concept Cones Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs

Reference 12

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Multi-Turn Neural Transparency: Surfacing Neural Activations Improves User Calibration to LLM Behavioral Drift cites this paper.

Multi-Turn Neural Transparency: Surfacing Neural Activations Improves User Calibration to LLM Behavioral Drift Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs

Reference 8

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