Paper Citation Record · LEDGER

Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights

As of 20 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2411.18343.

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

pith.paper-citation-record.v1
2411.18343 v3

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T11:22:38.258951Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

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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

44 of 44 outbound references displayed

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External citation measurements

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Outbound references

Observation 53adb352-c985-4a27-84ca-0cce406aa3ac · outbound

This paper cites LESS- VFL: communication-efficient feature selection for vertical federated learning.

Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights LESS- VFL: communication-efficient feature selection for vertical federated learning

Reference 3

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Observation ba9656c2-3ebc-4ddb-9d41-41e92711d2c6 · outbound

This paper cites Fairness in federated learning via core-stability.

Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights Fairness in federated learning via core-stability

Reference 4

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Observation 43b42d48-dbec-47bb-a97a-8b9bf9385da4 · outbound

This paper cites Liang Gao, Huazhu Fu, Li Li, Yingwen Chen, Ming Xu, and Cheng-Zhong Xu.

Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights Liang Gao, Huazhu Fu, Li Li, Yingwen Chen, Ming Xu, and Cheng-Zhong Xu

Reference 8

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This paper cites Amirata Ghorbani, James Wexler, James Y.

Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights Amirata Ghorbani, James Wexler, James Y

Reference 9

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This paper cites Been Kim, Martin Wattenberg, Justin Gilmer, Carrie J.

Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights Been Kim, Martin Wattenberg, Justin Gilmer, Carrie J

Reference 13

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Observation 00b272c6-7eef-4040-a2e1-d1e40b8d8496 · outbound

This paper cites Kingma and Max Welling.

Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights Kingma and Max Welling

Reference 14

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Observation 6d6d29c8-8976-4ef8-b73f-508778b95a01 · outbound

This paper cites Dan Ley, Saumitra Mishra, and Daniele Magazzeni.

Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights Dan Ley, Saumitra Mishra, and Daniele Magazzeni

Reference 16

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This paper cites Peichun Li, Guoliang Cheng, Xumin Huang, Jiawen Kang, Rong Yu, Yuan Wu, and Miao Pan.

Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights Peichun Li, Guoliang Cheng, Xumin Huang, Jiawen Kang, Rong Yu, Yuan Wu, and Miao Pan

Reference 17

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This paper cites Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith.

Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith

Reference 18

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This paper cites Xiaoxiao Li, Meirui Jiang, Xiaofei Zhang, Michael Kamp, and Qi Dou.

Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights Xiaoxiao Li, Meirui Jiang, Xiaofei Zhang, Michael Kamp, and Qi Dou

Reference 19

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Observation 9eb1be41-bea0-4a20-8006-7f7e03e51b0d · outbound

This paper cites Tzuong-Tsieng Moh.

Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights Tzuong-Tsieng Moh

Reference 22

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This paper cites doi: 10.1142/10861.

Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights doi: 10.1142/10861

Reference 23

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This paper cites Burrows NR, Hora I, Geiss LS, Gregg EW, and Albright A.

Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights Burrows NR, Hora I, Geiss LS, Gregg EW, and Albright A

Reference 24

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Observation a0f093a2-006c-433d-a21e-69eee106a428 · outbound

This paper cites URL ”http://dx.doi.org/10.15585/mmwr.mm6643a2”.

Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights URL ”http://dx.doi.org/10.15585/mmwr.mm6643a2”

Reference 25

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This paper cites 14 Yulei Qin, Xingyu Chen, Chao Chen, Yunhang Shen, Bo Ren, Yun Gu, Jie Yang, and Chunhua Shen.

Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights 14 Yulei Qin, Xingyu Chen, Chao Chen, Yunhang Shen, Bo Ren, Yun Gu, Jie Yang, and Chunhua Shen

Reference 26

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Observation 0516ab6d-5655-43a2-bb3f-dc48c76b3f59 · outbound

This paper cites Mohammad Rami and McCluskey Lee.

Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights Mohammad Rami and McCluskey Lee

Reference 27

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This paper cites URL https://doi.org/10.1007/s11263-015-0816-y.

Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights URL https://doi.org/10.1007/s11263-015-0816-y

Reference 29

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Observation fbd8d5e7-be29-4d0a-885c-7b44e2b3b8fe · outbound

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Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights Jamie Shotton, John M

Reference 31

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Observation 97652bee-ba10-4fad-9971-081dae2a7809 · outbound

This paper cites Deep inside convolutional networks: Visualising image classification models and saliency maps.

Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights Deep inside convolutional networks: Visualising image classification models and saliency maps

Reference 33

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Observation 74061cea-69d6-4e8e-8706-3ce706fb9adc · outbound

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Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights SmoothGrad: removing noise by adding noise

Reference 35

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Observation ef030218-7caa-4d19-a39f-660d8a3e58ad · outbound

This paper cites Explain any concept: Segment anything meets concept-based explanation.

Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights Explain any concept: Segment anything meets concept-based explanation

Reference 37

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Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights Axiomatic attribution for deep networks

Reference 38

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Observation c1b9e4eb-2a1f-4d02-b891-a91c8eb80ab8 · outbound

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Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights Efficient participant contribution eval- uation for horizontal and vertical federated learning

Reference 39

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Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights Unresolved cited work

Reference 40

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Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights Chenhao Xu, Youyang Qu, Yong Xiang, and Longxiang Gao

Reference 41

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Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights Peiyu Yang, Naveed Akhtar, Zeyi Wen, and Ajmal Mian

Reference 42

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Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights V37I9.26330

Reference 43

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Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights Unresolved cited work

Reference 44

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Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights Towards fair federated learning

Reference 45

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Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights Full-gradient representation for neural network visualization

Reference 1997

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This paper cites Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Ag¨uera y Arcas.

Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Ag¨uera y Arcas

Reference 1999

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This paper cites Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje.

Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje

Reference 2009

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Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights Ramprasaath R

Reference 2012

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Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights Auto-Encoding Variational Bayes

Reference 2014

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Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael S

Reference 2015

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Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights net/forum?id=HyxQzBceg

Reference 2017

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Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights Unresolved cited work

Reference 2018

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Observation 5e69a533-1bd6-445c-8d96-b0979a7bb936 · outbound

This paper cites Lower bounds and optimal algo- rithms for personalized federated learning.

Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights Lower bounds and optimal algo- rithms for personalized federated learning

Reference 2019

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Observation da598218-6f69-4982-ae0e-74b39ec8b798 · outbound

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Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights Jun Deng Jianxun Zhao

Reference 2020

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Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights Thomas Fel, Agustin Martin Picard, Louis B ´ethune, Thibaut Boissin, David Vigouroux, Julien Colin, R ´emi Cad`ene, and Thomas Serre

Reference 2021

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Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights Huancheng Chen, Chianing Wang, and Haris Vikalo

Reference 2022

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Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights Alexander A

Reference 2023

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Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights Gabriel G

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Pith citing papers

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