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Paper Citation Record · LEDGER

CENDRe: Concept Extraction with Natural Domain Representations

As of 20 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 0 inbound Pith citation observations for arXiv:2607.29621.

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

pith.paper-citation-record.v1
2607.29621 v1

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T03:25:35.805163Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

53 of 53 outbound references displayed

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Outbound references

Observation a8b98936-11c3-4c3a-b734-2d85106b3889 · outbound

This paper cites an unresolved cited work.

CENDRe: Concept Extraction with Natural Domain Representations Unresolved cited work

Reference 1

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Observation 219c9612-7a20-48ab-ad56-14b621e20fbc · outbound

This paper cites Clevr-xai: A benchmark dataset for the ground truth evaluation of neural network explanations.Information Fusion, 81:14–40, 2022.

CENDRe: Concept Extraction with Natural Domain Representations Clevr-xai: A benchmark dataset for the ground truth evaluation of neural network explanations.Information Fusion, 81:14–40, 2022

Reference 2

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Observation dc760be3-8295-4358-938f-c9ecbe63dc23 · outbound

This paper cites Princeton university press, 2021.

CENDRe: Concept Extraction with Natural Domain Representations Princeton university press, 2021

Reference 3

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Observation ed439db8-d8cc-460d-b3d4-afc6644fc387 · outbound

This paper cites Random search for hyper-parameter optimization.Journal of machine learning research, 13(2), 2012.

CENDRe: Concept Extraction with Natural Domain Representations Random search for hyper-parameter optimization.Journal of machine learning research, 13(2), 2012

Reference 4

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Observation 3f42136d-8e77-44e1-9816-b57538bc4154 · outbound

This paper cites FreqRISE: Explaining time series using frequency masking.

CENDRe: Concept Extraction with Natural Domain Representations FreqRISE: Explaining time series using frequency masking

Reference 5

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local_arxiv, observed 2026-08-03T03:28:22.745552Z

Source-reported events for the cited work

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Observation 16404d83-830c-48af-a093-546ac04601a2 · outbound

This paper cites Density-based clustering based on hierarchical density estimates.

CENDRe: Concept Extraction with Natural Domain Representations Density-based clustering based on hierarchical density estimates

Reference 6

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Observation adb00d81-9a8a-4ee8-943d-e457ec8f2a14 · outbound

This paper cites An effective screening design for sensitivity analysis of large models.Environmental modelling & software, 22(10):1509–1518, 2007.

CENDRe: Concept Extraction with Natural Domain Representations An effective screening design for sensitivity analysis of large models.Environmental modelling & software, 22(10):1509–1518, 2007

Reference 7

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Observation 3bd7c59f-c02a-4045-a7bb-558718126b54 · outbound

This paper cites This looks like that: deep learning for interpretable image recognition.Advances in neural information processing systems, 32, 2019.

CENDRe: Concept Extraction with Natural Domain Representations This looks like that: deep learning for interpretable image recognition.Advances in neural information processing systems, 32, 2019

Reference 8

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Observation 62ea50ec-a6be-40e4-a931-24e4927a30e0 · outbound

This paper cites Time is not enough: Time- frequency based explanation for time-series black-box models.

CENDRe: Concept Extraction with Natural Domain Representations Time is not enough: Time- frequency based explanation for time-series black-box models

Reference 9

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Observation 3b682ec3-e41f-41df-b3c5-3395dcb4015d · outbound

This paper cites Towards prototype-based self-explainable graph neural network.

CENDRe: Concept Extraction with Natural Domain Representations Towards prototype-based self-explainable graph neural network

Reference 10

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Observation 63d4ecb4-5765-4447-9529-be11a2d7c719 · outbound

This paper cites The ucr time series classification archive, October 2018.

CENDRe: Concept Extraction with Natural Domain Representations The ucr time series classification archive, October 2018

Reference 11

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Observation ff8995d1-79c5-492c-9f7c-9a288519f083 · outbound

This paper cites Explaining Deep Neural Networks for Bearing Fault Detection with Vibration Concepts.

CENDRe: Concept Extraction with Natural Domain Representations Explaining Deep Neural Networks for Bearing Fault Detection with Vibration Concepts

Reference 12

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Observation 63105423-39c8-4d5e-bfad-5bfcdec6f36e · outbound

This paper cites Deformable protopnet: An interpretable image classifier using deformable prototypes.

CENDRe: Concept Extraction with Natural Domain Representations Deformable protopnet: An interpretable image classifier using deformable prototypes

Reference 13

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Observation 1ea15cd7-b1b5-4093-8a81-37f3fbbc9940 · outbound

This paper cites A multi-scale cnn for transfer learning in semg-based hand gesture recognition for prosthetic devices.Sensors, 24(22):7147, 2024.

CENDRe: Concept Extraction with Natural Domain Representations A multi-scale cnn for transfer learning in semg-based hand gesture recognition for prosthetic devices.Sensors, 24(22):7147, 2024

Reference 14

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Observation ab36b54e-2514-4e44-96d7-955128343b99 · outbound

This paper cites Towards Automatic Concept- based Explanations.

CENDRe: Concept Extraction with Natural Domain Representations Towards Automatic Concept- based Explanations

Reference 15

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Observation 48eb1d74-d195-420a-a4f7-729aacaf64e3 · outbound

This paper cites Deep residual learning for image recognition.

CENDRe: Concept Extraction with Natural Domain Representations Deep residual learning for image recognition

Reference 16

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Observation f1e2cff0-08f9-4d0d-8003-b07e3d052cbf · outbound

This paper cites Classification of time series by shapelet transformation.Data mining and knowledge discovery, 2014.

CENDRe: Concept Extraction with Natural Domain Representations Classification of time series by shapelet transformation.Data mining and knowledge discovery, 2014

Reference 17

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Observation b08e4a08-425e-4564-b561-892787130474 · outbound

This paper cites A simple sequentially rejective multiple test procedure.Scandinavian journal of statistics, pages 65–70, 1979.

CENDRe: Concept Extraction with Natural Domain Representations A simple sequentially rejective multiple test procedure.Scandinavian journal of statistics, pages 65–70, 1979

Reference 18

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Observation f0623517-7965-4c5a-bfa0-d32b3ded4a86 · outbound

This paper cites Concept extraction for time series with eclad-ts.

CENDRe: Concept Extraction with Natural Domain Representations Concept extraction for time series with eclad-ts

Reference 19

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Observation 7a99a15c-8a7b-4589-8b9f-693c542f4037 · outbound

This paper cites Densely connected convolutional networks.

CENDRe: Concept Extraction with Natural Domain Representations Densely connected convolutional networks

Reference 20

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Observation 0957c068-1856-41cc-a507-ab43907dcf42 · outbound

This paper cites Inceptiontime: Finding alexnet for time series classification.Data Mining and Knowledge Discovery, 34(6):1936–1962, 2020.

CENDRe: Concept Extraction with Natural Domain Representations Inceptiontime: Finding alexnet for time series classification.Data Mining and Knowledge Discovery, 34(6):1936–1962, 2020

Reference 21

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Observation f547bb95-af1f-4e04-a74c-763a979d683b · outbound

This paper cites PACE: Posthoc Architecture- Agnostic Concept Extractor for Explaining CNNs.

CENDRe: Concept Extraction with Natural Domain Representations PACE: Posthoc Architecture- Agnostic Concept Extractor for Explaining CNNs

Reference 22

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Observation 7a300b48-f89b-4ee1-a96f-f4c20c52ed62 · outbound

This paper cites The simple difference formula: An approach to teaching nonparametric correla- tion.Comprehensive Psychology, 3:11–IT, 2014.

CENDRe: Concept Extraction with Natural Domain Representations The simple difference formula: An approach to teaching nonparametric correla- tion.Comprehensive Psychology, 3:11–IT, 2014

Reference 23

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Observation 98750265-2433-45c0-9f44-4e981e06944e · outbound

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

CENDRe: Concept Extraction with Natural Domain Representations Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCA V)

Reference 24

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Observation f2874bbe-8ab2-42dd-8632-5c010a08df7e · outbound

This paper cites Time-frequency multi-domain 1d convolutional neural network with channel-spatial attention for noise-robust bearing fault diagnosis.Sensors, 23(23): 9311, 2023.

CENDRe: Concept Extraction with Natural Domain Representations Time-frequency multi-domain 1d convolutional neural network with channel-spatial attention for noise-robust bearing fault diagnosis.Sensors, 23(23): 9311, 2023

Reference 25

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Observation 41013489-b2fe-4bd1-b623-d1ebf8021f73 · outbound

This paper cites Atrial fibrillation and atrial flutter detection using deep learning.

CENDRe: Concept Extraction with Natural Domain Representations Atrial fibrillation and atrial flutter detection using deep learning

Reference 26

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This paper cites Pywavelets: A python package for wavelet analysis.Journal of Open Source Software, 4(36):1237, 2019.

CENDRe: Concept Extraction with Natural Domain Representations Pywavelets: A python package for wavelet analysis.Journal of Open Source Software, 4(36):1237, 2019

Reference 27

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Observation 1b34559a-ad35-499f-898c-4669b76d2f91 · outbound

This paper cites Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: A benchmark data set for data-driven classification.

CENDRe: Concept Extraction with Natural Domain Representations Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: A benchmark data set for data-driven classification

Reference 28

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Observation 261373ee-3bcf-4c83-b9f7-c84d68726e5f · outbound

This paper cites Decoupled Weight Decay Regularization.

CENDRe: Concept Extraction with Natural Domain Representations Decoupled Weight Decay Regularization

Reference 29

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Observation 3aa35b6f-aa75-4f09-bd30-1fbd3c00376c · outbound

This paper cites Concept-based explainability for an eeg transformer model.

CENDRe: Concept Extraction with Natural Domain Representations Concept-based explainability for an eeg transformer model

Reference 30

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Observation 42c7f0be-9459-4f52-85fa-16c2a359d9e3 · outbound

This paper cites From confusion to clarity: Protoscore-a framework for evaluating prototype-based xai.

CENDRe: Concept Extraction with Natural Domain Representations From confusion to clarity: Protoscore-a framework for evaluating prototype-based xai

Reference 31

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Observation 8f3bcb8e-45b5-47b5-bb55-348dc911f7b0 · outbound

This paper cites Factorial sampling plans for preliminary computational experiments.Quality control and applied statistics, 37(6):307–310, 1992.

CENDRe: Concept Extraction with Natural Domain Representations Factorial sampling plans for preliminary computational experiments.Quality control and applied statistics, 37(6):307–310, 1992

Reference 32

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Observation 89208605-6af7-4453-a0fc-35ee74bcb7fe · outbound

This paper cites Modern hierarchical, agglomerative clustering algorithms.

CENDRe: Concept Extraction with Natural Domain Representations Modern hierarchical, agglomerative clustering algorithms

Reference 33

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Observation 00134893-2bf3-4abe-9879-988ef3b33067 · outbound

This paper cites Generalized eta and omega squared statistics: measures of effect size for some common research designs.Psychological methods, 8(4):434, 2003.

CENDRe: Concept Extraction with Natural Domain Representations Generalized eta and omega squared statistics: measures of effect size for some common research designs.Psychological methods, 8(4):434, 2003

Reference 34

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Observation 103db7b9-244e-4189-bc79-d339bdb47759 · outbound

This paper cites Pearson Education India, 1999.

CENDRe: Concept Extraction with Natural Domain Representations Pearson Education India, 1999

Reference 35

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Observation eabc797c-b443-4fd4-9ae0-dfe35f04750b · outbound

This paper cites Concept- based explainable artificial intelligence: A survey.ACM Computing Surveys, 2023.

CENDRe: Concept Extraction with Natural Domain Representations Concept- based explainable artificial intelligence: A survey.ACM Computing Surveys, 2023

Reference 36

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Observation efd63c30-8dda-433c-916d-8118b9f1120d · outbound

This paper cites Scale-preserving automatic concept extraction (SPACE).Machine Learning, 112(11):4495– 4525, 2023.

CENDRe: Concept Extraction with Natural Domain Representations Scale-preserving automatic concept extraction (SPACE).Machine Learning, 112(11):4495– 4525, 2023

Reference 37

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-03T03:25:35.761849Z digest=sha256:82ac9f6d7db02d3cb5b3043eb6a50efb2d31badeb18f3f73eb4f8aaaccb97041

Observation 20ef21a7-8ef2-4938-9176-961ec1f52923 · outbound

This paper cites ECLAD: Extracting Concepts with Local Aggregated Descriptors.Pattern Recognition, 147:110146, 2023.

CENDRe: Concept Extraction with Natural Domain Representations ECLAD: Extracting Concepts with Local Aggregated Descriptors.Pattern Recognition, 147:110146, 2023

Reference 38

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T03:25:35.764826Z digest=sha256:9d7fe34431d0335d95ad1dc3a226dd2d63494860b997787faf3ce377e4c70532

Observation d6404abd-7228-4438-bb39-25a6b1483a80 · outbound

This paper cites Rolling element bearing diagnostics—a tutorial.Me- chanical systems and signal processing, 25(2):485–520, 2011.

CENDRe: Concept Extraction with Natural Domain Representations Rolling element bearing diagnostics—a tutorial.Me- chanical systems and signal processing, 25(2):485–520, 2011

Reference 39

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no resolver link, observed 2026-08-03T03:25:35.767549Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-03T03:25:35.767549Z digest=sha256:c0235aea892f8a4a609d34cb348a1e9da8b095afb34363ae847dcfdf5aa2cabd

Observation 096c7f0c-1dee-46eb-893c-52e2da17898f · outbound

This paper cites A hybrid 1d cnn-bilstm model for epileptic seizure detection using multichannel eeg feature fusion.Biomedical physics & engineering express, 10 (3):035040, 2024.

CENDRe: Concept Extraction with Natural Domain Representations A hybrid 1d cnn-bilstm model for epileptic seizure detection using multichannel eeg feature fusion.Biomedical physics & engineering express, 10 (3):035040, 2024

Reference 40

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no resolver link, observed 2026-08-03T03:25:35.770438Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T03:25:35.770438Z digest=sha256:b4d94ed7adc30a734009169de508e706e6b98c6d37d8973cdeb7a5125b0ab923

Observation 99d20c14-77d3-432d-adbb-93fc30d402f3 · outbound

This paper cites Silhouettes: a graphical aid to the interpretation and validation of cluster analysis.Journal of computational and applied mathematics, 20:53–65, 1987.

CENDRe: Concept Extraction with Natural Domain Representations Silhouettes: a graphical aid to the interpretation and validation of cluster analysis.Journal of computational and applied mathematics, 20:53–65, 1987

Reference 41

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no resolver link, observed 2026-08-03T03:25:35.773275Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T03:25:35.773275Z digest=sha256:aa985d813276af17dfb27b2fb7159663e1848b40e4bc8f6d10d10b0fa4a54ec7

Observation a3d7a9b8-b22e-44e4-bbe4-da97e5faa552 · outbound

This paper cites John Wiley & Sons, 2008.

CENDRe: Concept Extraction with Natural Domain Representations John Wiley & Sons, 2008

Reference 42

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no resolver link, observed 2026-08-03T03:25:35.776100Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T03:25:35.776100Z digest=sha256:e6db4c6f64a5d245fa0d47e02d4e253cb739297cc9276522ee084304c5156fec

Observation 3c4cdb6b-5bc3-48ca-99c4-1ec66261f0f4 · outbound

This paper cites Web-scale k-means clustering.

CENDRe: Concept Extraction with Natural Domain Representations Web-scale k-means clustering

Reference 43

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no resolver link, observed 2026-08-03T03:25:35.779084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T03:25:35.779084Z digest=sha256:4daa1bf34fdb0aaf7f22109c5cb3c49c95fd142aeaaa9e6d932c00410974018c

Observation f5277d79-0f3a-452e-a770-a026e72d130f · outbound

This paper cites SmoothGrad: removing noise by adding noise.

CENDRe: Concept Extraction with Natural Domain Representations SmoothGrad: removing noise by adding noise

Reference 44

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no resolver link, observed 2026-08-03T03:25:35.781721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T03:25:35.781721Z digest=sha256:ee5a2f1f16c383d5ede9071f36616c409b660f779cc153938b8197c1002a19f8

Observation 7b83cb7c-56f8-4d6b-bc3a-94694ba115b1 · outbound

This paper cites Smith and Robert B.

CENDRe: Concept Extraction with Natural Domain Representations Smith and Robert B

Reference 45

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no resolver link, observed 2026-08-03T03:25:35.784929Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T03:25:35.784929Z digest=sha256:cf5b121c7d26a35625e149406b2d4a7c61cafcbb6a30c6d8355519d22a934527

Observation 95f9d8cd-8685-48b5-844d-9cc39bc454ef · outbound

This paper cites Global sensitivity indices for nonlinear mathematical models and their monte carlo estimates.Mathematics and computers in simulation, 55(1-3):271–280, 2001.

CENDRe: Concept Extraction with Natural Domain Representations Global sensitivity indices for nonlinear mathematical models and their monte carlo estimates.Mathematics and computers in simulation, 55(1-3):271–280, 2001

Reference 46

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no resolver link, observed 2026-08-03T03:25:35.787873Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T03:25:35.787873Z digest=sha256:be871d8f7b4359585d0fd05aa418a23f9119f4fb84e98fcbeabd97640a8a66f7

Observation 4b5b2b6d-dbba-4ea9-a111-df259fb808bf · outbound

This paper cites Convolutional neural networks for raw signal classification in cnc turning process monitoring.

CENDRe: Concept Extraction with Natural Domain Representations Convolutional neural networks for raw signal classification in cnc turning process monitoring

Reference 47

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no resolver link, observed 2026-08-03T03:25:35.790805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T03:25:35.790805Z digest=sha256:d1e95bb32daa91457acb35d98ddcef133cfb78c71d7a56cf8bc4373aa024c337

Observation 0d4a6f08-00af-4b16-a021-870409535c1c · outbound

This paper cites Ex- plainable ai for time series via virtual inspection layers.Pattern Recognition, 150:110309, 2024.

CENDRe: Concept Extraction with Natural Domain Representations Ex- plainable ai for time series via virtual inspection layers.Pattern Recognition, 150:110309, 2024

Reference 48

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no resolver link, observed 2026-08-03T03:25:35.793832Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T03:25:35.793832Z digest=sha256:85b60d258e5654fd5aef53cc970cd9a79c7d0e62ed67f8a25c763866db530b66

Observation aa88b080-5353-458b-8dd6-494731bb0ccd · outbound

This paper cites Individual comparisons by ranking methods.

CENDRe: Concept Extraction with Natural Domain Representations Individual comparisons by ranking methods

Reference 49

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no resolver link, observed 2026-08-03T03:25:35.796447Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T03:25:35.796447Z digest=sha256:d659365773d171ea35d2d445f182995de2f7c71f2f8fc84849c137f01042884d

Observation df1676da-59e1-4478-a79e-cc88bdc02c7c · outbound

This paper cites Ptwt-the pytorch wavelet toolbox.Journal of Machine Learning Research, 25(80):1–7, 2024.

CENDRe: Concept Extraction with Natural Domain Representations Ptwt-the pytorch wavelet toolbox.Journal of Machine Learning Research, 25(80):1–7, 2024

Reference 50

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no resolver link, observed 2026-08-03T03:25:35.799321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T03:25:35.799321Z digest=sha256:b59a32ed61cae824f0dac68a9ec7801f287704e3a6a8495e74a539dc01ebefee

Observation f889b7e8-8c56-49ed-8ebd-e759680c3dee · outbound

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

CENDRe: Concept Extraction with Natural Domain Representations On completeness-aware concept-based explanations in deep neural networks

Reference 51

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T03:25:35.802302Z digest=sha256:c82e6f3791b818832909d5488f87af24784837b2f22461b0edf75ce964203625

Observation a1a258ce-1cdb-42b0-85a6-285641bdc719 · outbound

This paper cites absence” masks described below. Shared generation parameters are summarized in Table 2. Negatives as primitives.A model can learn “absence of square.

CENDRe: Concept Extraction with Natural Domain Representations absence” masks described below. Shared generation parameters are summarized in Table 2. Negatives as primitives.A model can learn “absence of square

Reference 52

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malformed identifier
no resolver link, observed 2026-08-03T03:25:35.805163Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T03:25:35.805163Z digest=sha256:446df3954765bdc124a21709c0b7022aa50c4230f935e25d8f91af73e0a9333c

Observation bdb10ed1-3153-431b-bc46-3c973a841497 · outbound

This paper cites an unresolved cited work.

CENDRe: Concept Extraction with Natural Domain Representations Unresolved cited work

Reference 2024

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no resolver link, observed 2026-08-03T03:25:35.652861Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T03:25:35.652861Z digest=sha256:4fa17748818631b61584a43b5dc2498bd7f04c7bbb1e71e94bfda3e3af443288

Pith citing papers

No inbound Pith citation observations are available.