Paper Citation Record · LEDGER

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement

As of 10 August 2026, this Paper Citation Record lists 70 of 70 outbound references and 2 inbound Pith citation observations for arXiv:2502.06775.

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

pith.paper-citation-record.v1
2502.06775 v2

Coverage vector

measured 70 of 70 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T14:27:38.584498Z

measured 72 of 72 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T16:35:01.079284Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T09:36:03.336342Z

Reference resolution

70 of 70 outbound references displayed

verified exact6
verified fuzzy35
unresolved29
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bebed348-6e69-46de-9517-4e2c6c85eee5 · outbound

This paper cites write newline.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement write newline

Reference 1

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Source-reported events for the cited work

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Observation f7596bf7-876f-4783-abd2-b47b175fcd57 · outbound

This paper cites Sanity checks for saliency maps.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Sanity checks for saliency maps

Reference 2

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Source-reported events for the cited work

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Observation 37a91761-c0eb-4bae-bd9e-e52a09bd2828 · outbound

This paper cites Learning sparsely used overcomplete dictionaries via alternating minimization.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Learning sparsely used overcomplete dictionaries via alternating minimization

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:27:39.546702Z

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Observation bbe95ccd-7ea3-424d-bfd9-682ec369be70 · outbound

This paper cites K-svd: An algorithm for designing overcomplete dictionaries for sparse representation.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement K-svd: An algorithm for designing overcomplete dictionaries for sparse representation

Reference 4

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Observation c7dc571b-8706-4ac0-b1a5-401b33885953 · outbound

This paper cites W., Anderson, T.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement W., Anderson, T

Reference 5

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Observation 8d5ebfc0-ef0a-4b85-b7e3-1bf0498e55ab · outbound

This paper cites Simple, efficient, and neural algorithms for sparse coding.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Simple, efficient, and neural algorithms for sparse coding

Reference 6

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Observation ec13edc9-30ec-4d9c-9716-8e27e851a2f1 · outbound

This paper cites How to explain individual classification decisions.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement How to explain individual classification decisions

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:27:39.490498Z

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Observation 0e395fcd-7b1c-4525-bc6c-1d96bc85b773 · outbound

This paper cites o m, M., L \.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement o m, M., L \

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:27:39.476605Z

Source-reported events for the cited work

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Observation a3ff0c7e-f819-405b-82b5-bbaeb896a40a · outbound

This paper cites What, indeed, is an achievable provable guarantee for learning-enabled safety-critical systems.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement What, indeed, is an achievable provable guarantee for learning-enabled safety-critical systems

Reference 9

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Observation 99d37520-ebb3-4f11-b57f-2ce121df6aeb · outbound

This paper cites Language models are few-shot learners.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Language models are few-shot learners

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:27:39.448264Z

Source-reported events for the cited work

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Observation 30bbe8fc-7399-44b4-a723-38be5e837f4a · outbound

This paper cites Semantic bottleneck for computer vision tasks.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Semantic bottleneck for computer vision tasks

Reference 11

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Source-reported events for the cited work

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Observation 9e69e1e2-519e-4d79-a5a3-f8641b3c6b93 · outbound

This paper cites D., Vidal, R., and Geman, D.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement D., Vidal, R., and Geman, D

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:27:39.417909Z

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Observation 937c058d-32b0-4b5d-b66f-d4e1d490da01 · outbound

This paper cites Variational Information Pursuit for Interpretable Predictions.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Variational Information Pursuit for Interpretable Predictions

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-08T14:27:38.866893Z

Source-reported events for the cited work

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Observation 8b6fbe1c-256e-4e40-a743-3a60ba1ae265 · outbound

This paper cites Information maximization perspective of orthogonal matching pursuit with applications to explainable ai.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Information maximization perspective of orthogonal matching pursuit with applications to explainable ai

Reference 14

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Observation 640a9b06-f594-4670-adcf-8a344972d3f7 · outbound

This paper cites F., Phan, V.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement F., Phan, V

Reference 15

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Source-reported events for the cited work

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Observation 20099c4e-b90b-4891-980c-09f86bc4d100 · outbound

This paper cites R., Oksuz, I., Puyol-Ant \'o n, E., Ruijsink, B., King, A.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement R., Oksuz, I., Puyol-Ant \'o n, E., Ruijsink, B., King, A

Reference 16

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Observation 2ebb5d8d-c476-4773-b340-f3c745d71d8a · outbound

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

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Imagenet: A large-scale hierarchical image database

Reference 17

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Source-reported events for the cited work

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Observation c65837e0-ed80-457f-b9ad-5ca81c3f3af3 · outbound

This paper cites Estimating Uncertainty in Multimodal Foundation Models using Public Internet Data.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Estimating Uncertainty in Multimodal Foundation Models using Public Internet Data

Reference 18

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Source-reported events for the cited work

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Observation aa2b7bb6-8e8d-411b-8695-036891893271 · outbound

This paper cites Rethinking domain adaptation and generalization in the era of clip.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Rethinking domain adaptation and generalization in the era of clip

Reference 19

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Source-reported events for the cited work

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Observation d93cbb2c-092a-4216-a759-f8578d03b0e7 · outbound

This paper cites Can we Constrain Concept Bottleneck Models to Learn Semantically Meaningful Input Features?.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Can we Constrain Concept Bottleneck Models to Learn Semantically Meaningful Input Features?

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-08T14:27:38.826169Z

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Observation e54681fa-40ea-4594-9588-fc6fff97dbe0 · outbound

This paper cites and Jedynak, B.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement and Jedynak, B

Reference 21

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Observation bf75d359-7732-4df3-89fb-2130994156fc · outbound

This paper cites Interpretation of neural networks is fragile.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Interpretation of neural networks is fragile

Reference 22

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Observation c1a0d089-3c11-4b66-a790-fb097decdfad · outbound

This paper cites W., Gast, J., Ruiz, I.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement W., Gast, J., Ruiz, I

Reference 23

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Source-reported events for the cited work

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Observation 2e1c1c5a-1503-4bf6-b677-a05a29de462c · outbound

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

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Regression concept vectors for bidirectional explanations in histopathology

Reference 24

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Observation a9b7f218-eb0d-4d14-a255-54b496baa68a · outbound

This paper cites Identifying implicit social biases in vision-language models.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Identifying implicit social biases in vision-language models

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:27:39.285021Z

Source-reported events for the cited work

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Observation d66a3139-7b62-4d36-abc6-6e67b832af97 · outbound

This paper cites The elements of statistical learning: data mining, inference, and prediction, 2009.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement The elements of statistical learning: data mining, inference, and prediction, 2009

Reference 26

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Observation 9c80503a-f076-4183-9361-fc57fd6e2b5a · outbound

This paper cites Probabilistic Concept Bottleneck Models.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Probabilistic Concept Bottleneck Models

Reference 27

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This paper cites u tt, K. T., D \.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement u tt, K. T., D \

Reference 28

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Observation ad5c6f5e-9676-4f0e-af4a-1452eabb0662 · outbound

This paper cites W., Nguyen, T., Tang, Y.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement W., Nguyen, T., Tang, Y

Reference 29

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Observation 3e6c3d1a-9f47-4d23-b688-db1676480710 · outbound

This paper cites A., Levie, R., Bruna, J., and Kutyniok, G.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement A., Levie, R., Bruna, J., and Kutyniok, G

Reference 30

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Observation 4938cec4-9da6-417f-adaa-0cda6ffaaa14 · outbound

This paper cites Learning multiple layers of features from tiny images.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Learning multiple layers of features from tiny images

Reference 31

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This paper cites H., Nickisch, H., and Harmeling, S.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement H., Nickisch, H., and Harmeling, S

Reference 32

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Observation ea346232-bfd9-42ac-920a-bba4c7712d12 · outbound

This paper cites Deep learning.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Deep learning

Reference 33

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This paper cites Simple Alternating Minimization Provably Solves Complete Dictionary Learning.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Simple Alternating Minimization Provably Solves Complete Dictionary Learning

Reference 34

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Observation fbb5f9d6-0be8-4637-85d2-65cf18082e65 · outbound

This paper cites Personalized dictionary learning for heterogeneous datasets.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Personalized dictionary learning for heterogeneous datasets

Reference 35

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Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Task-driven dictionary learning

Reference 36

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Observation b6df3c71-5124-4347-9e49-be82d2fb145e · outbound

This paper cites Understanding approximate and unrolled dictionary learning for pattern recovery.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Understanding approximate and unrolled dictionary learning for pattern recovery

Reference 37

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This paper cites Interpretability is in the mind of the beholder: A causal framework for human-interpretable representation learning.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Interpretability is in the mind of the beholder: A causal framework for human-interpretable representation learning

Reference 38

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Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Do Concept Bottleneck Models Learn as Intended?

Reference 39

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Enhancing Performance of Explainable AI Models with Constrained Concept Refinement and Raghavan, P

Reference 40

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Observation 72b9b456-1226-4346-9dbb-056e63f72ec1 · outbound

This paper cites Label-Free Concept Bottleneck Models.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Label-Free Concept Bottleneck Models

Reference 41

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Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Unresolved cited work

Reference 42

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Enhancing Performance of Explainable AI Models with Constrained Concept Refinement C., Rezaiifar, R., and Krishnaprasad, P

Reference 43

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Observation 0f18570b-a2aa-4556-804c-9d2930a3ecda · outbound

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Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Unresolved cited work

Reference 44

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This paper cites W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al

Reference 45

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This paper cites Unveiling Glitches: A Deep Dive into Image Encoding Bugs within CLIP.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Unveiling Glitches: A Deep Dive into Image Encoding Bugs within CLIP

Reference 46

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This paper cites Analysis of fast structured dictionary learning.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Analysis of fast structured dictionary learning

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:27:39.084581Z

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Observation 8ffed260-6882-4ded-ad9d-72e6654d8e10 · outbound

This paper cites Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

Reference 48

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This paper cites and Nakamura, K.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement and Nakamura, K

Reference 49

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This paper cites R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D

Reference 50

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Observation 64595cc1-5dd0-4642-8c4d-01e33819a196 · outbound

This paper cites Investigating the Limitation of CLIP Models: The Worst-Performing Categories.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Investigating the Limitation of CLIP Models: The Worst-Performing Categories

Reference 51

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Observation 55cd5fb1-c7cf-4f25-9851-34a76fd9a723 · outbound

This paper cites Learning important features through propagating activation differences.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Learning important features through propagating activation differences

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:27:39.041199Z

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

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 53

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This paper cites Fooling lime and shap: Adversarial attacks on post hoc explanation methods.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Fooling lime and shap: Adversarial attacks on post hoc explanation methods

Reference 54

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Observation f2b4ce80-98fd-4f6b-8488-8dc62a1743df · outbound

This paper cites SmoothGrad: removing noise by adding noise.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement SmoothGrad: removing noise by adding noise

Reference 55

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Observation 58dd4b1d-bb1e-4b6b-9232-47eb61f1df9a · outbound

This paper cites Fooling network interpretation in image classification.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Fooling network interpretation in image classification

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:27:39.012663Z

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Observation c6d0a795-4e29-420c-afd1-e6f288751ade · outbound

This paper cites Complete dictionary recovery over the sphere i: Overview and the geometric picture.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Complete dictionary recovery over the sphere i: Overview and the geometric picture

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:27:38.998348Z

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This paper cites Stable and Interpretable Unrolled Dictionary Learning.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Stable and Interpretable Unrolled Dictionary Learning

Reference 58

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Observation a427d25a-bd5a-4ded-9aa3-1ddf98bcbfcc · outbound

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Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Unresolved cited work

Reference 59

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Observation bb672d41-3196-4d8e-a114-8f62878bc6e0 · outbound

This paper cites Attention is all you need.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Attention is all you need

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-08T14:27:38.541035Z

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Observation 6fda782f-381a-403a-91bd-4a96f97e4b95 · outbound

This paper cites High-dimensional probability: An introduction with applications in data science, volume 47.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement High-dimensional probability: An introduction with applications in data science, volume 47

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-08T14:27:38.545305Z

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Observation fcd37ecc-5a34-4ed2-b9ea-58f6373f7fb2 · outbound

This paper cites The caltech-ucsd birds-200-2011 dataset.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement The caltech-ucsd birds-200-2011 dataset

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-08T14:27:38.549451Z

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Observation d7b412a4-271d-423f-9171-dfef20f0cf40 · outbound

This paper cites Attention Head Purification: A New Perspective to Harness CLIP for Domain Generalization.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Attention Head Purification: A New Perspective to Harness CLIP for Domain Generalization

Reference 63

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Observation 47441bf4-9093-43e5-a143-7d84011e7938 · outbound

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Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:27:38.942729Z

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Observation 5d6ec5f2-de9d-4789-a55f-41e7b0e7ca8c · outbound

This paper cites Neural-symbolic vqa: Disentangling reasoning from vision and language understanding.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Neural-symbolic vqa: Disentangling reasoning from vision and language understanding

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:27:38.928843Z

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Observation 21b382ce-3bb1-47c0-ab40-b41207592b22 · outbound

This paper cites Post-hoc Concept Bottleneck Models.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Post-hoc Concept Bottleneck Models

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-08T14:27:38.566732Z

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Observation 0bff5b11-d6ce-4799-8e35-5b545afaab4e · outbound

This paper cites E., Barbiero, P., Ciravegna, G., Marra, G., Giannini, F., Diligenti, M., Precioso, F., Melacci, S., Weller, A., Lio, P., et al.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement E., Barbiero, P., Ciravegna, G., Marra, G., Giannini, F., Diligenti, M., Precioso, F., Melacci, S., Weller, A., Lio, P., et al

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:27:38.914606Z

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Observation 6e2458a8-6310-4cd0-8487-ddc424096bb6 · outbound

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Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Unresolved cited work

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-08T14:27:38.575543Z

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Observation 99738fc8-976d-44e4-8c3c-3cdef8548c8d · outbound

This paper cites Places: A 10 million image database for scene recognition.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Places: A 10 million image database for scene recognition

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-08T14:27:38.579989Z

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Observation e74a1083-81bc-45b8-9b10-55af44893a9f · outbound

This paper cites Interpretable basis decomposition for visual explanation.

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement Interpretable basis decomposition for visual explanation

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:27:38.882200Z

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Pith citing papers

Observation 63ff9ebb-5569-4bdd-a4e3-d950597f6802 · inbound

AI Achieves a Perfect LSAT Score cites this paper.

AI Achieves a Perfect LSAT Score Enhancing Performance of Explainable AI Models with Constrained Concept Refinement

Reference 38

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation afadcfd1-c923-4a16-9326-a899ce715fad · inbound

A Periodic Space of Distributed Computing: Vision & Framework cites this paper.

A Periodic Space of Distributed Computing: Vision & Framework Enhancing Performance of Explainable AI Models with Constrained Concept Refinement

Reference 89

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:36:03.341061Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.