Understanding Data Influence with Differential Approximation | Paper Citation Record

Observation ff870141-5646-43b0-93e5-bb79649a4d3f · outbound

This paper cites Language models are few-shot learners,.

Understanding Data Influence with Differential Approximation Language models are few-shot learners,

Reference 1

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Observation 8187c9d8-e992-43f9-8e88-a2a0a6933267 · outbound

This paper cites Segment Anything.

Understanding Data Influence with Differential Approximation Segment Anything

Reference 2

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Observation 2cfbaa79-bd68-4b9f-a64c-350d4bcbbfe1 · outbound

This paper cites DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models.

Understanding Data Influence with Differential Approximation DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models

Reference 3

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Observation 0e273752-99ae-4c91-b52e-1f11e773e496 · outbound

This paper cites Dataset pruning: Reducing training data by examining generalization influence,.

Understanding Data Influence with Differential Approximation Dataset pruning: Reducing training data by examining generalization influence,

Reference 4

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Observation b10d7fa2-8da9-4dd2-bb51-0fc5e4b821df · outbound

This paper cites LESS: Selecting Influential Data for Targeted Instruction Tuning.

Understanding Data Influence with Differential Approximation LESS: Selecting Influential Data for Targeted Instruction Tuning

Reference 5

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Observation 6ca25a10-b720-4066-ace5-ea154ca1f69c · outbound

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

Understanding Data Influence with Differential Approximation Studying Large Language Model Generalization with Influence Functions

Reference 6

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Observation 487298c5-1382-4a9b-ab84-42f9a05350b8 · outbound

This paper cites Training Data Attribution for Diffusion Models.

Understanding Data Influence with Differential Approximation Training Data Attribution for Diffusion Models

Reference 7

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This paper cites an unresolved cited work.

Understanding Data Influence with Differential Approximation Unresolved cited work

Reference 8

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Observation 85a9d783-13fc-414f-8525-c35db70b8bdf · outbound

This paper cites Assessment of local influence,.

Understanding Data Influence with Differential Approximation Assessment of local influence,

Reference 9

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Observation a696a79a-acee-4f38-bed0-a323e1ef2165 · outbound

This paper cites Understanding black-box predictions via influence functions,.

Understanding Data Influence with Differential Approximation Understanding black-box predictions via influence functions,

Reference 10

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Observation e6f3b2d9-8b6a-4b65-bcaa-afb9c1bf5f46 · outbound

This paper cites On second-order group influence functions for black-box predictions,.

Understanding Data Influence with Differential Approximation On second-order group influence functions for black-box predictions,

Reference 11

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Observation d65c6bc0-da2f-4eb9-be3d-42b52eecb41e · outbound

This paper cites On the accuracy of influence functions for measuring group effects,.

Understanding Data Influence with Differential Approximation On the accuracy of influence functions for measuring group effects,

Reference 12

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Observation 19edba3f-c702-4077-8b20-e67ee76dc9e7 · outbound

This paper cites Influence functions in deep learning are fragile,.

Understanding Data Influence with Differential Approximation Influence functions in deep learning are fragile,

Reference 13

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Observation 3a516232-549e-472c-8c05-7140269b8603 · outbound

This paper cites The mirrored influ- ence hypothesis: Efficient data influence estimation by harnessing forward passes,.

Understanding Data Influence with Differential Approximation The mirrored influ- ence hypothesis: Efficient data influence estimation by harnessing forward passes,

Reference 14

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Observation da82ed8f-6d10-4dec-8218-dad46b4c3501 · outbound

This paper cites Estimating Training Data Influence by Tracing Gradient Descent.

Understanding Data Influence with Differential Approximation Estimating Training Data Influence by Tracing Gradient Descent

Reference 15

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Observation 5a0161ec-1422-419f-88b2-683aa5c59a8c · outbound

This paper cites Capturing the temporal dependence of training data influence,.

Understanding Data Influence with Differential Approximation Capturing the temporal dependence of training data influence,

Reference 16

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Observation e4de5cbb-0b90-448b-8e29-ac068077605d · outbound

This paper cites Data pruning via moving-one-sample-out,.

Understanding Data Influence with Differential Approximation Data pruning via moving-one-sample-out,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:29:09.903201Z

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Observation 76df9b03-0eae-4f0a-a7c4-2ccfefbec250 · outbound

This paper cites Data cleansing for models trained with sgd,.

Understanding Data Influence with Differential Approximation Data cleansing for models trained with sgd,

Reference 18

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Observation c4b4ed82-b186-450f-a950-22cb438f451b · outbound

This paper cites Fast exact multiplication by the hessian,.

Understanding Data Influence with Differential Approximation Fast exact multiplication by the hessian,

Reference 19

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Observation 0dc20645-cda7-4326-a0a8-37389cd1af50 · outbound

This paper cites Gex: A flexible method for approximating influence via geometric ensemble,.

Understanding Data Influence with Differential Approximation Gex: A flexible method for approximating influence via geometric ensemble,

Reference 20

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Observation cb7d8029-34f3-4084-84ce-4c2a602c19f0 · outbound

This paper cites Scaling up influence functions,.

Understanding Data Influence with Differential Approximation Scaling up influence functions,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:29:09.218332Z

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Observation b8f7c7c8-9c50-4984-a392-ebbc5e3779b2 · outbound

This paper cites Beyond neural scaling laws: beating power law scaling via data pruning,.

Understanding Data Influence with Differential Approximation Beyond neural scaling laws: beating power law scaling via data pruning,

Reference 22

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Observation 655b0cd7-e517-46f5-8a13-42b4e2bd44c6 · outbound

This paper cites Knowledge removal in sampling- based bayesian inference,.

Understanding Data Influence with Differential Approximation Knowledge removal in sampling- based bayesian inference,

Reference 23

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Observation b5e57fb1-a14f-45aa-931d-79da63853fef · outbound

This paper cites The llama 3 herd of models,.

Understanding Data Influence with Differential Approximation The llama 3 herd of models,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:29:08.647414Z

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Observation 84ee287d-eeb4-439d-abf2-ee0bd0e1b350 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Understanding Data Influence with Differential Approximation Training Verifiers to Solve Math Word Problems

Reference 25

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Observation 55d186cf-e622-454f-843a-c48042fa3830 · outbound

This paper cites Moderate coreset: A universal method of data selection for real- world data-efficient deep learning,.

Understanding Data Influence with Differential Approximation Moderate coreset: A universal method of data selection for real- world data-efficient deep learning,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:29:08.446898Z

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Observation 37f1d618-82aa-4b34-bb40-d5390fc382b7 · outbound

This paper cites Training Data Influence Analysis and Estimation: A Survey.

Understanding Data Influence with Differential Approximation Training Data Influence Analysis and Estimation: A Survey

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-05T18:28:57.606556Z

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Observation 37761238-7203-48e9-866f-32d8a1f13cc1 · outbound

This paper cites A value for n-person games,.

Understanding Data Influence with Differential Approximation A value for n-person games,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:29:08.263203Z

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Observation 715d3106-9258-490d-b792-ba717c9035cd · outbound

This paper cites Rkhs-shap: Shapley values for kernel methods,.

Understanding Data Influence with Differential Approximation Rkhs-shap: Shapley values for kernel methods,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:29:08.092937Z

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Observation 9fc65f2b-074b-4b41-8f07-69824d4df725 · outbound

This paper cites Imagenet large scale visual recognition challenge,.

Understanding Data Influence with Differential Approximation Imagenet large scale visual recognition challenge,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:29:07.950684Z

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Observation 4da557e9-8278-4e5f-a9f3-3de7921ae714 · outbound

This paper cites LAION-5b: An open large-scale dataset for training next generation image-text models,.

Understanding Data Influence with Differential Approximation LAION-5b: An open large-scale dataset for training next generation image-text models,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:29:07.767497Z

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Observation 59f348fb-6648-4ba2-9e6f-246a1738cb21 · outbound

This paper cites Conceptual 12M: Pushing web-scale image-text pre-training to recognize long-tail visual concepts,.

Understanding Data Influence with Differential Approximation Conceptual 12M: Pushing web-scale image-text pre-training to recognize long-tail visual concepts,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:29:07.386303Z

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Observation 56b738b5-9e03-474a-99d8-a064d86d573a · outbound

This paper cites What neural networks memorize and why: Discovering the long tail via influence estimation,.

Understanding Data Influence with Differential Approximation What neural networks memorize and why: Discovering the long tail via influence estimation,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:29:06.972719Z

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Observation 05810230-0a70-473b-afa7-d914e1cc7ea4 · outbound

This paper cites The loss surfaces of multilayer networks,.

Understanding Data Influence with Differential Approximation The loss surfaces of multilayer networks,

Reference 34

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Observation 1a30d011-41aa-440b-a64e-1950a10ea1ea · outbound

This paper cites Identifying and attacking the saddle point problem in high-dimensional non-convex optimization,.

Understanding Data Influence with Differential Approximation Identifying and attacking the saddle point problem in high-dimensional non-convex optimization,

Reference 35

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Observation 5aa53c83-43ea-4f52-8330-e8d6a8525040 · outbound

This paper cites Revisiting inverse hessian vector products for calculating influence functions,.

Understanding Data Influence with Differential Approximation Revisiting inverse hessian vector products for calculating influence functions,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:29:05.689137Z

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Observation cac8e4c5-d5fd-4ca0-b184-ecf205fd1344 · outbound

This paper cites Revisit, extend, and enhance hessian-free influence functions,.

Understanding Data Influence with Differential Approximation Revisit, extend, and enhance hessian-free influence functions,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:29:05.205299Z

Source-reported events for the cited work

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Observation c79bf00d-6fe5-4ba0-93ea-c4337c25f16a · outbound

This paper cites If influence functions are the answer, then what is the question?.

Understanding Data Influence with Differential Approximation If influence functions are the answer, then what is the question?

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:29:04.741139Z

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Observation 3848e550-ef3c-4f6d-9474-21ccac983588 · outbound

This paper cites ”what data benefits my classifier?.

Understanding Data Influence with Differential Approximation ”what data benefits my classifier?

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:29:04.383926Z

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Observation 3ad8bf3d-3e63-44c8-a2ba-81c80262ba56 · outbound

This paper cites Adam: A method for stochastic opti- mization,.

Understanding Data Influence with Differential Approximation Adam: A method for stochastic opti- mization,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:29:03.999205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d2c4f213-b62a-4bba-b52a-9db5c4e81dc0 · outbound

This paper cites Data shapley: Equitable valuation of data for machine learning,.

Understanding Data Influence with Differential Approximation Data shapley: Equitable valuation of data for machine learning,

Reference 41

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Observation d9af9b18-a9b0-4455-84f5-d9de96378dd2 · outbound

This paper cites Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms.

Understanding Data Influence with Differential Approximation Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms

Reference 42

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Observation 63ffcd8a-c106-4096-84db-a534c688f3b4 · outbound

This paper cites Scalability vs. utility: Do we have to sacrifice one for the other in data importance quan- tification?.

Understanding Data Influence with Differential Approximation Scalability vs. utility: Do we have to sacrifice one for the other in data importance quan- tification?

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:29:03.677588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2715468a-44c5-4202-bb8f-d75227ad2398 · outbound

This paper cites What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions.

Understanding Data Influence with Differential Approximation What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-05T18:28:54.016047Z

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Observation 97759565-341f-4816-9893-727031f5ed25 · outbound

This paper cites Data valuation for medical imaging using shapley value and application to a large-scale chest x-ray dataset,.

Understanding Data Influence with Differential Approximation Data valuation for medical imaging using shapley value and application to a large-scale chest x-ray dataset,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:29:03.257482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 52f462be-80b7-42b5-92a0-ee8a1e62287c · outbound

This paper cites Outlier gra- dient analysis: Efficiently identifying detrimental training samples for deep learning models,.

Understanding Data Influence with Differential Approximation Outlier gra- dient analysis: Efficiently identifying detrimental training samples for deep learning models,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:29:02.894107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 198f6c28-d9b3-4bf6-9e13-1820497f7539 · outbound

This paper cites Resolving training biases via influence-based data relabeling,.

Understanding Data Influence with Differential Approximation Resolving training biases via influence-based data relabeling,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:29:02.420528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation add88444-81db-482f-a716-6f0aa71f1faa · outbound

This paper cites Influence function based data poisoning attacks to top-n recommender systems,.

Understanding Data Influence with Differential Approximation Influence function based data poisoning attacks to top-n recommender systems,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:29:02.121652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9ff74154-4ca7-446a-9764-71ed60062b6b · outbound

This paper cites Exploring example influence in continual learning,.

Understanding Data Influence with Differential Approximation Exploring example influence in continual learning,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:29:01.898571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2650e56d-40aa-4f72-b04d-000f4b38c4e6 · outbound

This paper cites Explaining a series of models by propagating shapley values,.

Understanding Data Influence with Differential Approximation Explaining a series of models by propagating shapley values,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:29:01.606746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7f553edb-8b9b-45e3-ae1d-7421aef739e5 · outbound

This paper cites TRAK: Attributing Model Behavior at Scale.

Understanding Data Influence with Differential Approximation TRAK: Attributing Model Behavior at Scale

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-05T18:28:54.826260Z

Source-reported events for the cited work

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Observation 30e20088-5ed5-4df3-bb22-6921e0c7ffd8 · outbound

This paper cites Datamodels: Predicting Predictions from Training Data.

Understanding Data Influence with Differential Approximation Datamodels: Predicting Predictions from Training Data

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-05T18:28:54.951998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10d81630-3f0e-4a6b-8e96-b014ca3894c2 · outbound

This paper cites Achieving fairness at no utility cost via data reweighing with influence,.

Understanding Data Influence with Differential Approximation Achieving fairness at no utility cost via data reweighing with influence,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:29:01.351497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 26c65be0-fb20-427a-b822-93e3c9290c48 · outbound

This paper cites Hydra: Hypergradient data relevance analysis for interpreting deep neural networks,.

Understanding Data Influence with Differential Approximation Hydra: Hypergradient data relevance analysis for interpreting deep neural networks,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:29:01.084213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c4b06e36-73df-4b86-bcd1-ab2b4bce4b23 · outbound

This paper cites Influence selection for active learning,.

Understanding Data Influence with Differential Approximation Influence selection for active learning,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:29:00.763839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 40faa722-74a0-40c8-8919-d6d385ae79e8 · outbound

This paper cites Automatic differentiation in pytorch,.

Understanding Data Influence with Differential Approximation Automatic differentiation in pytorch,

Reference 56

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Observation 1d0c32a0-b594-40db-8416-147c481ecb6f · outbound

This paper cites Deep residual learning for image recognition,.

Understanding Data Influence with Differential Approximation Deep residual learning for image recognition,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:29:00.276893Z

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Observation c30ad7f9-8747-46fc-be68-5d67ecedcbbf · outbound

This paper cites How does batch normalization help optimization?.

Understanding Data Influence with Differential Approximation How does batch normalization help optimization?

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:29:00.104691Z

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Observation 3a7829e2-9ae1-4c43-9201-858cb4ab84c1 · outbound

This paper cites Visualizing the loss landscape of neural nets,.

Understanding Data Influence with Differential Approximation Visualizing the loss landscape of neural nets,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:28:59.837174Z

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Observation c1593dae-2396-43a3-a751-4b1fd3e9e68c · outbound

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

Understanding Data Influence with Differential Approximation Learning multiple layers of features from tiny images,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:28:59.597370Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fb929685-2bc5-43dd-99bc-ff1358b70805 · outbound

This paper cites Tiny imagenet visual recognition challenge,.

Understanding Data Influence with Differential Approximation Tiny imagenet visual recognition challenge,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:28:59.366251Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b1d4be66-a195-4ee2-982d-2389e5f0ab5d · outbound

This paper cites Learning Transferable Visual Models From Natural Language Supervision.

Understanding Data Influence with Differential Approximation Learning Transferable Visual Models From Natural Language Supervision

Reference 62

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This paper cites Automated Cleanup of the ImageNet Dataset by Model Consensus, Explainability and Confident Learning.

Understanding Data Influence with Differential Approximation Automated Cleanup of the ImageNet Dataset by Model Consensus, Explainability and Confident Learning

Reference 63

Resolution: verified exact; local_arxiv, observed 2026-08-05T18:28:57.202612Z

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Observation 221b9246-a679-4f0d-acf4-83d4e46e360a · outbound

This paper cites Ssse: Efficiently erasing samples from trained machine learning models,.

Understanding Data Influence with Differential Approximation Ssse: Efficiently erasing samples from trained machine learning models,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:28:59.131363Z

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Observation 0b653c27-336b-4a2d-adcf-a2ba60f7936c · outbound

This paper cites Active learning for convolutional neural networks: A coreset approach,.

Understanding Data Influence with Differential Approximation Active learning for convolutional neural networks: A coreset approach,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:28:59.007068Z

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Observation 97d1cbfa-8e20-480d-b697-d8458327beb1 · outbound

This paper cites Blip: Bootstrapping language- image pre-training for unified vision-language understanding and generation,.

Understanding Data Influence with Differential Approximation Blip: Bootstrapping language- image pre-training for unified vision-language understanding and generation,

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-05T18:28:56.505355Z

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This paper cites BLIP-2: bootstrapping language- image pre-training with frozen image encoders and large language models,.

Understanding Data Influence with Differential Approximation BLIP-2: bootstrapping language- image pre-training with frozen image encoders and large language models,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:28:58.832493Z

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Observation a5603f70-db77-41f8-988c-6200ab933913 · outbound

This paper cites Flickr30k entities: Collecting region- to-phrase correspondences for richer image-to-sentence models,.

Understanding Data Influence with Differential Approximation Flickr30k entities: Collecting region- to-phrase correspondences for richer image-to-sentence models,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:28:58.535983Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9948dd8e-03a6-4bca-b754-1f8b0348bfea · outbound

This paper cites Squeeze-and-excitation networks,.

Understanding Data Influence with Differential Approximation Squeeze-and-excitation networks,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:28:58.261413Z

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Observation fb45aa77-7461-447c-84f1-d6da395f6004 · outbound

This paper cites Efficientnet: Rethinking model scaling for convolutional neural networks,.

Understanding Data Influence with Differential Approximation Efficientnet: Rethinking model scaling for convolutional neural networks,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:28:58.008161Z

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