Paper Citation Record · LEDGER

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks

As of 12 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 1 inbound Pith citation observation for arXiv:2505.17883.

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

pith.paper-citation-record.v1
2505.17883 v1

Coverage vector

measured 60 of 60 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:43:37.886510Z

measured 61 of 61 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-12T05:20:39.978351Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T05:21:23.775165Z

Reference resolution

60 of 60 outbound references displayed

verified exact6
verified fuzzy24
unresolved30
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f82cd527-c9b7-4f68-b415-647aab863dd5 · outbound

This paper cites write newline.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks write newline

Reference 1

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Observation 3ac98076-51a9-44e8-8d3f-2c4d2357ddac · outbound

This paper cites Understanding intermediate layers using linear classifier probes.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Understanding intermediate layers using linear classifier probes

Reference 2

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Observation b2067852-949a-493f-9c90-d5c790fd02a6 · outbound

This paper cites Perceptual symbol systems.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Perceptual symbol systems

Reference 3

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Observation 5ee2dfa7-1116-41c6-a91f-d6576da45c9b · outbound

This paper cites Network dissection: Quantifying interpretability of deep visual representations.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Network dissection: Quantifying interpretability of deep visual representations

Reference 4

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Observation 9db0a976-76d4-4229-b621-9c1aa9effff1 · outbound

This paper cites Understanding the role of individual units in a deep neural network.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Understanding the role of individual units in a deep neural network

Reference 5

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Observation 61c5f955-258c-402f-9ebb-bc9929838be0 · outbound

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FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Unresolved cited work

Reference 6

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Observation 017592d9-623f-4ad2-9149-9d4157ea2070 · outbound

This paper cites L., Anil, C., Denison, C., Askell, A., et al.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks L., Anil, C., Denison, C., Askell, A., et al

Reference 7

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Observation 6a3de1c8-bea1-4ddf-9fdb-4678d61d4521 · outbound

This paper cites and Lin, C.-J.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks and Lin, C.-J

Reference 8

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Observation 9a0076ef-0ae1-4ea4-86f7-effe6dc1f042 · outbound

This paper cites Training a support vector machine in the primal.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Training a support vector machine in the primal

Reference 9

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Observation b7d02a9f-2c7b-485c-9cc0-8c2db60d1671 · outbound

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

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks An image is worth 16x16 words: Transformers for image recognition at scale

Reference 10

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Observation 6c1aaa34-2ccd-4fe1-8409-1063afb91dcf · outbound

This paper cites Toy Models of Superposition.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Toy Models of Superposition

Reference 11

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Observation b9904237-6219-4a13-8812-b8319ae79764 · outbound

This paper cites Liblinear: A library for large linear classification.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Liblinear: A library for large linear classification

Reference 12

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Observation 03e7de85-47a7-45f2-ae81-64cab947dd59 · outbound

This paper cites Eva: Exploring the limits of masked visual representation learning at scale.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Eva: Exploring the limits of masked visual representation learning at scale

Reference 13

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Observation 2811e9f9-bebe-48b7-ab39-1fb902cac595 · outbound

This paper cites Eva-02: A visual representation for neon genesis.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Eva-02: A visual representation for neon genesis

Reference 14

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Observation f897b1bb-8e52-44ed-872f-813285e4c05a · outbound

This paper cites Y., and Kim, B.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Y., and Kim, B

Reference 15

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Observation a216dc70-95a7-4594-ade8-1a266971e368 · outbound

This paper cites Distilling blackbox to interpretable models for efficient transfer learning.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Distilling blackbox to interpretable models for efficient transfer learning

Reference 16

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Observation e61db2ae-0094-4cce-9d94-d3a6faf3b9d8 · outbound

This paper cites Decoding the thought vector, 2016.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Decoding the thought vector, 2016

Reference 17

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Observation daefa2cd-5395-407f-bfcd-b0a477d18d67 · outbound

This paper cites Regression concept vectors for bidirectional explanations in histopathology.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Regression concept vectors for bidirectional explanations in histopathology

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:43:45.174058Z

Source-reported events for the cited work

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Observation 34eb6cbb-c594-40b3-97d3-59889c63ac23 · outbound

This paper cites Concept distillation: leveraging human-centered explanations for model improvement.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Concept distillation: leveraging human-centered explanations for model improvement

Reference 19

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Observation 0e60f6cf-3eb5-43a8-9149-2faf4d5b959a · outbound

This paper cites Deep residual learning for image recognition.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Deep residual learning for image recognition

Reference 20

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Observation b7895419-c4f0-4566-ba6e-736695c22ae2 · outbound

This paper cites On the proliferation of support vectors in high dimensions.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks On the proliferation of support vectors in high dimensions

Reference 21

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Observation 192e48fe-3897-4a0e-9e2d-f7e5f38d7e21 · outbound

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FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Unresolved cited work

Reference 22

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Observation b3ebaf48-5ddb-478b-b7b2-0f1619437a7f · outbound

This paper cites LG-CAV: Train Any Concept Activation Vector with Language Guidance.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks LG-CAV: Train Any Concept Activation Vector with Language Guidance

Reference 23

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Observation b491b28c-2bf6-4682-bb6b-e779d41fbd31 · outbound

This paper cites Timm leaderboard, 2025.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Timm leaderboard, 2025

Reference 24

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Observation 7b0e3785-65d9-42da-8886-edb23a52b164 · outbound

This paper cites Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison

Reference 25

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Observation c5439913-9b85-4544-a531-20ef23670578 · outbound

This paper cites MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs

Reference 26

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This paper cites Visualizing and Understanding Recurrent Networks.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Visualizing and Understanding Recurrent Networks

Reference 27

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Observation 0881812f-d35e-4b7f-832e-d44ebf7a40f9 · outbound

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

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)

Reference 28

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Observation 98aeef18-11a7-46a7-abb4-3bb90d93313e · outbound

This paper cites A convnet for the 2020s.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks A convnet for the 2020s

Reference 29

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Observation 6610fb77-8af5-4fdc-8acb-1796eb0aa6c5 · outbound

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FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Decoupled Weight Decay Regularization

Reference 30

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This paper cites Text2concept: Concept activation vectors directly from text.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Text2concept: Concept activation vectors directly from text

Reference 31

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Observation 3e8adfa5-0ed7-456e-a67b-a6e0f7168554 · outbound

This paper cites Classification vs regression in overparameterized regimes: Does the loss function matter? Journal of Machine Learning Research, 22 0 (222): 0 1--69, 2021.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Classification vs regression in overparameterized regimes: Does the loss function matter? Journal of Machine Learning Research, 22 0 (222): 0 1--69, 2021

Reference 32

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Observation 2603edbc-7c3e-47f6-86e0-52534daf5a17 · outbound

This paper cites Explaining Explainability: Recommendations for Effective Use of Concept Activation Vectors.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Explaining Explainability: Recommendations for Effective Use of Concept Activation Vectors

Reference 33

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This paper cites CLIP-Dissect: Automatic Description of Neuron Representations in Deep Vision Networks.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks CLIP-Dissect: Automatic Description of Neuron Representations in Deep Vision Networks

Reference 34

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This paper cites Linear Explanations for Individual Neurons.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Linear Explanations for Individual Neurons

Reference 35

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Observation 16a35852-b399-4539-b7ca-048f96a54825 · outbound

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FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Feature visualization

Reference 36

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This paper cites Zoom in: An introduction to circuits.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Zoom in: An introduction to circuits

Reference 37

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This paper cites J., Wiegand, T., Samek, W., and Lapuschkin, S.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks J., Wiegand, T., Samek, W., and Lapuschkin, S

Reference 38

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FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Pytorch: An imperative style, high-performance deep learning library

Reference 39

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FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Scikit-learn: Machine learning in python

Reference 40

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This paper cites Investigating neural network training on a feature level using conditional independence.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Investigating neural network training on a feature level using conditional independence

Reference 41

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This paper cites Robust Semantic Interpretability: Revisiting Concept Activation Vectors.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Robust Semantic Interpretability: Revisiting Concept Activation Vectors

Reference 42

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FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Unresolved cited work

Reference 43

Resolution: verified exact; doi, observed 2026-08-07T14:43:38.332420Z

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FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al

Reference 44

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FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Imagenet large scale visual recognition challenge

Reference 45

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Observation 6dc13274-3ee2-4d4d-b291-29effe4d3cc5 · outbound

This paper cites Best of both worlds: local and global explanations with human-understandable concepts.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Best of both worlds: local and global explanations with human-understandable concepts

Reference 46

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This paper cites On the relationship between the support vector machine for classification and sparsified fisher's linear discriminant.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks On the relationship between the support vector machine for classification and sparsified fisher's linear discriminant

Reference 47

Resolution: verified exact; doi, observed 2026-08-07T14:43:38.138259Z

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FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Opening the Black Box of Deep Neural Networks via Information

Reference 48

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FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Unresolved cited work

Reference 49

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This paper cites Using causal analysis for conceptual deep learning explanation.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Using causal analysis for conceptual deep learning explanation

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:43:41.436468Z

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Observation 01ee34d4-d230-475b-b36d-2b9313efdde7 · outbound

This paper cites Intriguing properties of neural networks.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Intriguing properties of neural networks

Reference 51

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FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Going deeper with convolutions

Reference 52

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Observation 51a44a44-cc4e-4ebd-897e-5556d9def872 · outbound

This paper cites Rethinking the inception architecture for computer vision.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Rethinking the inception architecture for computer vision

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T14:43:37.020736Z

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Observation 84424c3f-88c5-42a7-bd56-d334cbca5259 · outbound

This paper cites Scaling monosemanticity: Extracting interpretable features from claude 3 sonnet.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Scaling monosemanticity: Extracting interpretable features from claude 3 sonnet

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:43:41.057834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 65f1f4fe-d36c-4df3-9bce-148b0befd6b7 · outbound

This paper cites Statistical learning theory.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Statistical learning theory

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:43:40.639977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation aea18bc0-f4bb-40bb-a5b6-7783d294f033 · outbound

This paper cites Pytorch image models.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Pytorch image models

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T14:43:37.397848Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20bd0261-1c26-4c29-b1b7-4306b51c55d1 · outbound

This paper cites HuggingFace's Transformers: State-of-the-art Natural Language Processing.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks HuggingFace's Transformers: State-of-the-art Natural Language Processing

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T14:43:37.472269Z

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Observation dc6b4c49-8ec0-47b0-bb08-aec7121f0410 · outbound

This paper cites On completeness-aware concept-based explanations in deep neural networks.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks On completeness-aware concept-based explanations in deep neural networks

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:43:40.247087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation aebaa413-9796-44e9-ac44-1585a54d355d · outbound

This paper cites A., Shechtman, E., and Wang, O.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks A., Shechtman, E., and Wang, O

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T14:43:37.723166Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ce49a2a-b248-4193-b965-f3c381ddba88 · outbound

This paper cites A., and Rubinstein, B.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks A., and Rubinstein, B

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T14:43:37.886510Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 93aa55a9-ab60-40ad-be97-7bdb8ec15ceb · inbound

E-TCAV: Formalizing Penultimate Proxies for Efficient Concept Based Interpretability cites this paper.

E-TCAV: Formalizing Penultimate Proxies for Efficient Concept Based Interpretability FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-12T05:21:23.781940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.