Paper Citation Record · LEDGER

Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution

As of 8 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2505.21438.

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

pith.paper-citation-record.v1
2505.21438 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:34:06.174870Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

39 of 39 outbound references displayed

verified exact4
verified fuzzy9
unresolved22
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 85fa31a4-84d9-4e5e-bdd3-8d4b18b83289 · outbound

This paper cites nlX i=1 ∂ℓli ∂θl + ∂Ωl ∂θl # − N −1.

Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution nlX i=1 ∂ℓli ∂θl + ∂Ωl ∂θl # − N −1

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:08.906908Z

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Observation 90b66583-03f0-43d5-9629-9af6cfdcf2e8 · outbound

This paper cites URL https://doi.org/10.1214/23-AOS2319.

Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution URL https://doi.org/10.1214/23-AOS2319

Reference 6

Resolution: verified exact; doi, observed 2026-08-07T13:34:06.696118Z

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Observation e287bfce-2dde-4a37-9114-b22b3d97f1d3 · outbound

This paper cites Amirata Ghorbani and James Zou.

Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution Amirata Ghorbani and James Zou

Reference 9

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Observation 146ea4c5-a0c4-4d90-b2d7-f6582e82edcb · outbound

This paper cites Studying Large Language Model Generalization with Influence Functions.

Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution Studying Large Language Model Generalization with Influence Functions

Reference 10

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Observation 01168e9b-57ea-4e59-a611-8db0e556c5ea · outbound

This paper cites doi: 10.18653/v1/2021.emnlp-main.808.

Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution doi: 10.18653/v1/2021.emnlp-main.808

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T13:34:03.507057Z

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Observation 284d4c95-bd75-4dff-9e13-1bd1f3ade234 · outbound

This paper cites Rapid adaptation for deep neural networks through multi-task learning.

Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution Rapid adaptation for deep neural networks through multi-task learning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:09.889852Z

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Observation 1aa4c9ce-13c4-448e-9b52-8382123104a7 · outbound

This paper cites 2015-719.

Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution 2015-719

Reference 15

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Observation 0f54444d-168e-4810-b2b1-ab00a5650b49 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution Adam: A Method for Stochastic Optimization

Reference 16

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Observation 44d738a5-9429-413b-b5b2-f2278ae2536b · outbound

This paper cites Identifying Task Groupings for Multi-Task Learning Using Pointwise V-Usable Information.

Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution Identifying Task Groupings for Multi-Task Learning Using Pointwise V-Usable Information

Reference 17

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Observation 8664f051-ee6f-4ed4-a78d-be80f5587362 · outbound

This paper cites Xi Lin, Xiaoyuan Zhang, Zhiyuan Yang, Fei Liu, Zhenkun Wang, and Qingfu Zhang.

Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution Xi Lin, Xiaoyuan Zhang, Zhiyuan Yang, Fei Liu, Zhenkun Wang, and Qingfu Zhang

Reference 18

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Observation f77e91a1-68bc-4545-b5cd-295944677f5c · outbound

This paper cites Conflict-averse gradient descent for multi-task learning.

Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution Conflict-averse gradient descent for multi-task learning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:09.728235Z

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Observation c7f30066-b454-42dd-b154-4f933ea1100e · outbound

This paper cites doi: https://doi.org/10.1016/j.eswa.2024.123739.

Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution doi: https://doi.org/10.1016/j.eswa.2024.123739

Reference 21

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Observation 6ec95f1e-6ad9-4f25-93f4-bc42243503a4 · outbound

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Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution ISBN 9781450355520

Reference 22

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Observation fc000d66-154f-4ec0-b826-664e63a390dc · outbound

This paper cites 11 Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution A PREPRINT Sung Min Park, Kristian Georgiev, Andrew Ilyas, Guillaume Leclerc, and Aleksander Madry.

Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution 11 Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution A PREPRINT Sung Min Park, Kristian Georgiev, Andrew Ilyas, Guillaume Leclerc, and Aleksander Madry

Reference 23

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Observation 6eae72c4-b3ee-4411-8b52-d79f5462abc2 · outbound

This paper cites Garima Pruthi, Frederick Liu, Satyen Kale, and Mukund Sundararajan.

Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution Garima Pruthi, Frederick Liu, Satyen Kale, and Mukund Sundararajan

Reference 24

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Observation 58d704dc-2a2d-4f9d-9f30-37ab6b7820a8 · outbound

This paper cites An Overview of Multi-Task Learning in Deep Neural Networks.

Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution An Overview of Multi-Task Learning in Deep Neural Networks

Reference 25

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Observation 0765fa39-dd8c-4463-8c65-513e7aaab819 · outbound

This paper cites Towards Principled Task Grouping for Multi-Task Learning.

Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution Towards Principled Task Grouping for Multi-Task Learning

Reference 27

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This paper cites doi: https://doi.org/10.1016/j.patrec.2020.05.031.

Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution doi: https://doi.org/10.1016/j.patrec.2020.05.031

Reference 28

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Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution doi: 12 Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution A PREPRINT https://doi.org/10.1016/j.knosys.2020.106132

Reference 29

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Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution Unresolved cited work

Reference 30

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Observation 7a912143-bcb0-4f9e-bfa2-ec9e7770ecd0 · outbound

This paper cites A survey on negative transfer.

Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution A survey on negative transfer

Reference 31

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Observation cd9977bb-e551-427e-a3a3-30b198d1609b · outbound

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Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution 2020.3004555

Reference 33

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Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution nkX i=1 ∂2ℓki(θk, γ) ∂θk∂θ ⊤ k + ∂2Ωk(θk, γ) ∂θk∂θ ⊤ k # ∈ Rdk×dk for 1 ≤ k ≤ K, Hkl = 0 ∈ Rdk×dl for 1 ≤ k, l≤ K and k ̸= l, H ⊤ K+1,k = Hk,K+1 = σk

Reference 34

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Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution Unresolved cited work

Reference 35

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Observation 49724457-a3c7-4ded-adba-b19454d62e83 · outbound

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Reference 37

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Observation 550b977e-ebeb-419f-8e54-9527b2cefe57 · outbound

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Reference 39

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Observation c70ebbb2-8479-474a-b09e-f59752fe50fa · outbound

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Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution Unresolved cited work

Reference 409

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Observation b0c0c19a-bdf7-44c9-b156-106bf7064526 · outbound

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Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution URL https://doi.org/10.1023/A:1007379606734

Reference 1997

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Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution ISBN 1581138881

Reference 2004

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Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution Feature Decomposition for Reducing Negative Transfer: A Novel Multi-task Learning Method for Recommender System

Reference 2010

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Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution doi: 10.3115/v1/P15-2139

Reference 2015

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Observation 2196ed88-286e-470f-bb66-53c9c99db13e · outbound

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Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution doi: 10.18653/v1/D17-1206

Reference 2017

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Source-reported events for the cited work

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Observation 55bc0b35-aa55-4285-a8cf-3c4764e60a2a · outbound

This paper cites Zhao Chen, Jiquan Ngiam, Yanping Huang, Thang Luong, Henrik Kretzschmar, Yuning Chai, and Dragomir Anguelov.

Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution Zhao Chen, Jiquan Ngiam, Yanping Huang, Thang Luong, Henrik Kretzschmar, Yuning Chai, and Dragomir Anguelov

Reference 2018

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Source-reported events for the cited work

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Observation b4284703-7798-43fa-9bfd-244c6b6fdb17 · outbound

This paper cites Deep learning in sheet metal bending with a novel theory-guided deep neural network.

Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution Deep learning in sheet metal bending with a novel theory-guided deep neural network

Reference 2019

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Observation 2ed2534a-56d6-4867-92a7-73d771d0590b · outbound

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Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution Adaptive and robust multi-task learning

Reference 2020

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Source-reported events for the cited work

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Observation 47cdbcbe-b51b-4d31-95d9-6e8d0dfbfeb4 · outbound

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Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution doi: 10.1093/imaiai/iaaa033

Reference 2021

Resolution: verified exact; doi, observed 2026-08-07T13:34:06.919409Z

Source-reported events for the cited work

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Observation a102c5fa-9e38-4c75-8048-d4ed1dc76cad · outbound

This paper cites Trevor Standley, Amir Zamir, Dawn Chen, Leonidas Guibas, Jitendra Malik, and Silvio Savarese.

Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution Trevor Standley, Amir Zamir, Dawn Chen, Leonidas Guibas, Jitendra Malik, and Silvio Savarese

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:09.520922Z

Source-reported events for the cited work

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Observation 7adf48e6-12f5-49fa-bc04-140a994320ea · outbound

This paper cites "It's a Match!" -- A Benchmark of Task Affinity Scores for Joint Learning.

Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution "It's a Match!" -- A Benchmark of Task Affinity Scores for Joint Learning

Reference 2023

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Source-reported events for the cited work

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Observation 2538f6a0-f0bf-4932-92d6-e7378fa04da7 · outbound

This paper cites A joint many-task model: Growing a neural network for multiple NLP tasks.

Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution A joint many-task model: Growing a neural network for multiple NLP tasks

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:10.037038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

No inbound Pith citation observations are available.