Effective Data Pruning through Score Extrapolation | Paper Citation Record

Observation c892a889-e314-4bcf-a3f0-bef7bd4878cc · outbound

This paper cites Large Language Models: A Survey.

Effective Data Pruning through Score Extrapolation Large Language Models: A Survey

Reference 1

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This paper cites Segment anything.

Effective Data Pruning through Score Extrapolation Segment anything

Reference 2

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Observation acd47061-5f0e-4456-abb0-f42119e4cbd2 · outbound

This paper cites Efficient time series processing for transformers and state-space models through token merging.

Effective Data Pruning through Score Extrapolation Efficient time series processing for transformers and state-space models through token merging

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:36.761224Z

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Observation 63e302c0-0845-4f41-ab0f-8cc6d559c1f1 · outbound

This paper cites Byte Pair Encoding for Efficient Time Series Forecasting.

Effective Data Pruning through Score Extrapolation Byte Pair Encoding for Efficient Time Series Forecasting

Reference 4

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Observation 2230a621-3549-47a4-ae5f-17b23f06baae · outbound

This paper cites Advanced active learning strategies for object detection.

Effective Data Pruning through Score Extrapolation Advanced active learning strategies for object detection

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:36.479557Z

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Observation b27b1f1f-c9c8-47d1-875e-673d529e1faf · outbound

This paper cites Generalized synchronized active learning for multi-agent-based data selection on mobile robotic systems.

Effective Data Pruning through Score Extrapolation Generalized synchronized active learning for multi-agent-based data selection on mobile robotic systems

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:36.284799Z

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Observation 26b41e7b-cf9b-4f42-bcb6-7cedf79a189b · outbound

This paper cites Large-scale dataset pruning with dynamic uncertainty.

Effective Data Pruning through Score Extrapolation Large-scale dataset pruning with dynamic uncertainty

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:36.113895Z

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Observation 20e688ec-ed4f-450e-8251-58b771210a2c · outbound

This paper cites Datamodels: Predicting predictions from training data.

Effective Data Pruning through Score Extrapolation Datamodels: Predicting predictions from training data

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:35.837094Z

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Observation fba00753-c902-48b2-a36f-af9cc8a1fc4c · outbound

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

Effective Data Pruning through Score Extrapolation Understanding black-box predictions via influence functions

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:35.668370Z

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Observation fb07b26f-a114-48bb-b8b1-3d302b7c6f44 · outbound

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

Effective Data Pruning through Score Extrapolation Learning multiple layers of features from tiny images

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:35.487103Z

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Observation 05e88179-32a5-4e54-93c2-c2c5655035db · outbound

This paper cites Places: A 10 million image database for scene recognition.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017.

Effective Data Pruning through Score Extrapolation Places: A 10 million image database for scene recognition.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017

Reference 12

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Observation 34608993-c9c1-4da4-91da-7eb90d46e465 · outbound

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

Effective Data Pruning through Score Extrapolation Imagenet: A large-scale hierarchical image database

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:35.314608Z

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Observation 9c4d95a5-875d-4f71-a078-a73ed081a0f8 · outbound

This paper cites Beyond neural scaling laws: beating power law scaling via data pruning.Advances in Neural Information Processing Systems (NeurIPS), 2022.

Effective Data Pruning through Score Extrapolation Beyond neural scaling laws: beating power law scaling via data pruning.Advances in Neural Information Processing Systems (NeurIPS), 2022

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:35.175962Z

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Observation a4cb40a8-e207-49d1-be53-1e2f2d00ff37 · outbound

This paper cites Spanning training progress: Temporal dual-depth scoring (tdds) for enhanced dataset pruning.

Effective Data Pruning through Score Extrapolation Spanning training progress: Temporal dual-depth scoring (tdds) for enhanced dataset pruning

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:36.009892Z

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Observation 3a0c5ec7-b715-41c8-9d56-791c257e501d · outbound

This paper cites Active learning for convolutional neural networks: A core-set approach.

Effective Data Pruning through Score Extrapolation Active learning for convolutional neural networks: A core-set approach

Reference 16

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Observation 666da399-f9df-4df4-9066-ece45efed210 · outbound

This paper cites What neural networks memorize and why: Discovering the long tail via influence estimation.Advances in Neural Information Processing Systems (NeurIPS), 33:2881–2891, 2020.

Effective Data Pruning through Score Extrapolation What neural networks memorize and why: Discovering the long tail via influence estimation.Advances in Neural Information Processing Systems (NeurIPS), 33:2881–2891, 2020

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:34.934354Z

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Observation 0de80de3-abd7-4bcf-a45c-2bf89dcacd52 · outbound

This paper cites Deepcore: A comprehensive library for coreset selection in deep learning.Database and Expert Systems Applications (DEXA), 4 2022.

Effective Data Pruning through Score Extrapolation Deepcore: A comprehensive library for coreset selection in deep learning.Database and Expert Systems Applications (DEXA), 4 2022

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:34.825455Z

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Observation c1edf418-204c-42d5-9b09-d08440757c69 · outbound

This paper cites Generalizing neural wave functions.

Effective Data Pruning through Score Extrapolation Generalizing neural wave functions

Reference 19

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Observation a6c206b1-5b95-43f1-853c-1969d49a830a · outbound

This paper cites Neural pfaffians: Solving many many-electron schrödinger equations.

Effective Data Pruning through Score Extrapolation Neural pfaffians: Solving many many-electron schrödinger equations

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:34.691543Z

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Observation b374d3eb-6143-4f6d-a69d-3aef393c7c5c · outbound

This paper cites Large-scale dataset pruning in adversarial training through data importance extrapolation.

Effective Data Pruning through Score Extrapolation Large-scale dataset pruning in adversarial training through data importance extrapolation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:34.580694Z

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Observation d8ee9dae-b87e-4cec-acdb-2493fd017a3e · outbound

This paper cites Data pruning via moving-one-sample-out.Advances in neural information processing systems (NeurIPS), 2023.

Effective Data Pruning through Score Extrapolation Data pruning via moving-one-sample-out.Advances in neural information processing systems (NeurIPS), 2023

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:34.475058Z

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Observation dcef3b07-d1b4-4bd6-b66f-962e4096df87 · outbound

This paper cites An empirical study of example forgetting during deep neural network learning.

Effective Data Pruning through Score Extrapolation An empirical study of example forgetting during deep neural network learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:34.368718Z

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Observation 563f3306-b0e1-4bdd-b5db-891ad015a9f7 · outbound

This paper cites Deep learning on a data diet: Finding important examples early in training.Advances in Neural Information Processing Systems (NeurIPS), 34, 2021.

Effective Data Pruning through Score Extrapolation Deep learning on a data diet: Finding important examples early in training.Advances in Neural Information Processing Systems (NeurIPS), 34, 2021

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:34.264832Z

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Observation 02a1dce2-08e4-4867-802e-86393d4686f6 · outbound

This paper cites Selection via proxy: Efficient data selection for deep learning.

Effective Data Pruning through Score Extrapolation Selection via proxy: Efficient data selection for deep learning

Reference 25

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Observation 11996d32-df6b-4b67-af6a-cc1da26e0b16 · outbound

This paper cites Identifying mislabeled data using the area under the margin ranking.Advances in Neural Information Processing Systems, 33:17044–17056, 2020.

Effective Data Pruning through Score Extrapolation Identifying mislabeled data using the area under the margin ranking.Advances in Neural Information Processing Systems, 33:17044–17056, 2020

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:34.114092Z

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Observation f9d0c74a-6c50-4e1b-980a-8b5c0125637e · outbound

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

Effective Data Pruning through Score Extrapolation Dataset pruning: Reducing training data by examining generalization influence

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:33.956223Z

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Observation 1e94c558-6621-45d2-a4e5-6310d63d648f · outbound

This paper cites Herding dynamical weights to learn.

Effective Data Pruning through Score Extrapolation Herding dynamical weights to learn

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:33.769222Z

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Observation dbd608ca-d8bf-4b0b-942e-481993dda6d2 · outbound

This paper cites Super-samples from kernel herding.

Effective Data Pruning through Score Extrapolation Super-samples from kernel herding

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:33.582394Z

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Observation b585ab51-a3ac-4d5e-adb9-096cefe78417 · outbound

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

Effective Data Pruning through Score Extrapolation Moderate coreset: A uni- versal method of data selection for real-world data-efficient deep learning

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:33.404524Z

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Observation 34db355a-e22a-442a-b19c-dc63044a65c1 · outbound

This paper cites Efficient and robust quantization-aware training via adaptive coreset selection.Transaction on Machine Learning (TMLR), 8 2024.

Effective Data Pruning through Score Extrapolation Efficient and robust quantization-aware training via adaptive coreset selection.Transaction on Machine Learning (TMLR), 8 2024

Reference 31

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Observation b94ea45a-b671-4d15-8219-dce0c39db63c · outbound

This paper cites Coresets for data-efficient training of machine learning models.

Effective Data Pruning through Score Extrapolation Coresets for data-efficient training of machine learning models

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:33.039963Z

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Observation b8cd4a89-a984-4b68-9314-2b910b9c3c8e · outbound

This paper cites Maximum margin coresets for active and noise tolerant learning.

Effective Data Pruning through Score Extrapolation Maximum margin coresets for active and noise tolerant learning

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:32.800735Z

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Observation 750a29ee-ebd5-4254-9620-c10219690c5a · outbound

This paper cites Coverage-centric coreset selection for high pruning rates.

Effective Data Pruning through Score Extrapolation Coverage-centric coreset selection for high pruning rates

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:32.547464Z

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Observation bf579ad8-4644-4b1c-afaf-c5086fc408ee · outbound

This paper cites Zero-shot coreset selection: Efficient pruning for unlabeled data.Arxiv, 2411.15349, 2024.

Effective Data Pruning through Score Extrapolation Zero-shot coreset selection: Efficient pruning for unlabeled data.Arxiv, 2411.15349, 2024

Reference 35

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Observation fd8c04dc-0ffb-4722-8530-eb1173f3cfc7 · outbound

This paper cites Coresets via bilevel optimization for continual learning and streaming.

Effective Data Pruning through Score Extrapolation Coresets via bilevel optimization for continual learning and streaming

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:32.212997Z

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Observation a56fd733-3799-47a7-8455-9441e34219b4 · outbound

This paper cites Glis- ter: Generalization based data subset selection for efficient and robust learning.

Effective Data Pruning through Score Extrapolation Glis- ter: Generalization based data subset selection for efficient and robust learning

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:31.875565Z

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Observation 33b71d73-8d76-4861-9fab-14b1dcf800d1 · outbound

This paper cites Grad-match: Gradient matching based data subset selection for efficient deep model training.

Effective Data Pruning through Score Extrapolation Grad-match: Gradient matching based data subset selection for efficient deep model training

Reference 38

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Observation dea620c0-a2e2-474b-a719-721971dd29d4 · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

Effective Data Pruning through Score Extrapolation Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 39

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Observation a48e35e5-7be2-48e9-b905-2084595c2f48 · outbound

This paper cites Clip: Cheap lipschitz training of neural networks.

Effective Data Pruning through Score Extrapolation Clip: Cheap lipschitz training of neural networks

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:31.577925Z

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Observation e89dbd64-ea3a-49e4-b3e3-e6fd61768fb6 · outbound

This paper cites Better diffusion models further improve adversarial training.

Effective Data Pruning through Score Extrapolation Better diffusion models further improve adversarial training

Reference 41

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Observation 7e3aaa07-08be-4c24-8d39-98bcca78195a · outbound

This paper cites On the scalability of certified adversarial robustness with generated data.

Effective Data Pruning through Score Extrapolation On the scalability of certified adversarial robustness with generated data

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:31.273843Z

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Observation 59b99ff2-29b1-4f2e-b93a-3145449ca2ba · outbound

This paper cites Efficient adversarial training in llms with continuous attacks.

Effective Data Pruning through Score Extrapolation Efficient adversarial training in llms with continuous attacks

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:30.968748Z

Source-reported events for the cited work

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Observation 7dbba4c1-7f94-421d-bca5-cd6863ec39c2 · outbound

This paper cites Identifying untrustworthy predictions in neural networks by geometric gradient analysis.

Effective Data Pruning through Score Extrapolation Identifying untrustworthy predictions in neural networks by geometric gradient analysis

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:30.628739Z

Source-reported events for the cited work

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Observation f9a04814-63bf-498a-819a-c6875ed7ce41 · outbound

This paper cites Improving robustness against real-world and worst-case distribution shifts through decision region quantification.

Effective Data Pruning through Score Extrapolation Improving robustness against real-world and worst-case distribution shifts through decision region quantification

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:30.313164Z

Source-reported events for the cited work

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Observation 186b9dac-2464-4ce4-9043-472a915b9b3a · outbound

This paper cites Collec- tive robustness certificates: Exploiting interdependence in graph neural networks.

Effective Data Pruning through Score Extrapolation Collec- tive robustness certificates: Exploiting interdependence in graph neural networks

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:30.098123Z

Source-reported events for the cited work

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Observation f9359adc-bf48-4123-bbd9-87508060e3e7 · outbound

This paper cites Invariance-aware randomized smoothing certificates.

Effective Data Pruning through Score Extrapolation Invariance-aware randomized smoothing certificates

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:29.890894Z

Source-reported events for the cited work

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Observation fef2fdf7-55d1-44d0-9b18-a2d581f7471f · outbound

This paper cites Dynamically sampled nonlocal gradients for stronger adversarial attacks.

Effective Data Pruning through Score Extrapolation Dynamically sampled nonlocal gradients for stronger adversarial attacks

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:29.703174Z

Source-reported events for the cited work

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Observation 4a370644-5ef4-425c-92aa-ded0b4012120 · outbound

This paper cites Exploring mis- classifications of robust neural networks to enhance adversarial attacks.Applied Intelligence, 2023.

Effective Data Pruning through Score Extrapolation Exploring mis- classifications of robust neural networks to enhance adversarial attacks.Applied Intelligence, 2023

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:29.622096Z

Source-reported events for the cited work

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Observation 5ad1444d-bcce-4bf4-84bd-baf968b9c46a · outbound

This paper cites Assessing robustness via score-based adversarial image generation.Transactions on Machine Learning Research (TMLR), 2023.

Effective Data Pruning through Score Extrapolation Assessing robustness via score-based adversarial image generation.Transactions on Machine Learning Research (TMLR), 2023

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:29.586798Z

Source-reported events for the cited work

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Observation 1aacce51-526b-4065-83c1-623c9d0671c9 · outbound

This paper cites Localized randomized smoothing for collective robustness certification.

Effective Data Pruning through Score Extrapolation Localized randomized smoothing for collective robustness certification

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:29.554582Z

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Observation ce540da2-28e3-4e12-ae93-8fc8136979c0 · outbound

This paper cites Edward Suh.

Effective Data Pruning through Score Extrapolation Edward Suh

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:29.508200Z

Source-reported events for the cited work

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Observation ab300f6e-37ff-4c3a-b48f-9fb542dafbee · outbound

This paper cites Data filtering for efficient adversarial training.Pattern Recognition, 151, 2024.

Effective Data Pruning through Score Extrapolation Data filtering for efficient adversarial training.Pattern Recognition, 151, 2024

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:29.470895Z

Source-reported events for the cited work

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Observation 275d6b1d-a158-4da7-924f-adfab6b77e22 · outbound

This paper cites GRAD-MATCH: Gradient matching based data subset selection for efficient deep model training.PMLR, 2021.

Effective Data Pruning through Score Extrapolation GRAD-MATCH: Gradient matching based data subset selection for efficient deep model training.PMLR, 2021

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:29.433065Z

Source-reported events for the cited work

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Observation a121d7b4-94ee-41c3-a839-c9b939e88cb4 · outbound

This paper cites Dolatabadi, Sarah Erfani, and Christopher Leckie.

Effective Data Pruning through Score Extrapolation Dolatabadi, Sarah Erfani, and Christopher Leckie

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:29.397009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 334fd1ce-11ee-466f-9ae7-7211e6a6bc01 · outbound

This paper cites Efficient Adversarial Training With Data Pruning.

Effective Data Pruning through Score Extrapolation Efficient Adversarial Training With Data Pruning

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:02:28.790168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0db6a0b9-1c29-4a6e-afaf-73086aecadff · outbound

This paper cites Less is More: Data Pruning for Faster Adversarial Training.

Effective Data Pruning through Score Extrapolation Less is More: Data Pruning for Faster Adversarial Training

Reference 57

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:02:28.761031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4cc7e320-cca1-4b00-8c8a-ef803fbe2038 · outbound

This paper cites A comprehensive survey of continual learning: Theory, method and application.IEEE Transactions on Pattern Analysis and Machine Intelligence, 46, 8 2024.

Effective Data Pruning through Score Extrapolation A comprehensive survey of continual learning: Theory, method and application.IEEE Transactions on Pattern Analysis and Machine Intelligence, 46, 8 2024

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:29.371057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b7daee01-61c0-43f6-baf8-8447d7d919a1 · outbound

This paper cites Dataset Distillation.

Effective Data Pruning through Score Extrapolation Dataset Distillation

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T05:02:28.273664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e902e8c4-67f8-4423-b271-28ba273f05bc · outbound

This paper cites Holder and Muhammad Shafique.

Effective Data Pruning through Score Extrapolation Holder and Muhammad Shafique

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:29.348379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d2d01876-2f8b-4226-9736-e07381630bc1 · outbound

This paper cites Unifying approaches in active learning and active sampling via fisher information and information-theoretic quantities.Transactions on Machine Learning Research (TMLR), 2022.

Effective Data Pruning through Score Extrapolation Unifying approaches in active learning and active sampling via fisher information and information-theoretic quantities.Transactions on Machine Learning Research (TMLR), 2022

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:29.329804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 18344ef3-ca6a-49b0-bce7-924569dc2f87 · outbound

This paper cites A uni- fied approach towards active learning and out-of-distribution detection.arXiv preprint arXiv:2405.11337, 2024.

Effective Data Pruning through Score Extrapolation A uni- fied approach towards active learning and out-of-distribution detection.arXiv preprint arXiv:2405.11337, 2024

Reference 62

Resolution: verified exact; raw_fallback, observed 2026-08-07T05:02:28.696761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2d48940a-b9b0-493e-bfc8-ed9ca5ad5639 · outbound

This paper cites Joint out-of- distribution filtering and data discovery active learning.

Effective Data Pruning through Score Extrapolation Joint out-of- distribution filtering and data discovery active learning

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:29.307268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 10c53136-477e-4159-8b57-e280c3ac3a83 · outbound

This paper cites Iale: Imitating active learner ensembles.Journal of Machine Learning Research, 23(107):1–29, 2022.

Effective Data Pruning through Score Extrapolation Iale: Imitating active learner ensembles.Journal of Machine Learning Research, 23(107):1–29, 2022

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:29.289935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 02dc297f-9440-4e7f-83b5-65ac29cdced9 · outbound

This paper cites Active learning of ordinal embeddings: A user study on football data.Transactions on Machine Learning Research, 2023.

Effective Data Pruning through Score Extrapolation Active learning of ordinal embeddings: A user study on football data.Transactions on Machine Learning Research, 2023

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:29.269114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1741d89e-4fc7-4789-8592-cd36a5f96012 · outbound

This paper cites Nikolakakis, Amin Karbasi, Dionysis Kalogerias, Nezihe Merve Gürel, and Theodoros Rekatsinas.

Effective Data Pruning through Score Extrapolation Nikolakakis, Amin Karbasi, Dionysis Kalogerias, Nezihe Merve Gürel, and Theodoros Rekatsinas

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:29.246114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c4ecbf6e-3a13-4db2-8b05-9e630e2f2537 · outbound

This paper cites Exploring Data Redundancy in Real-world Image Classification through Data Selection.

Effective Data Pruning through Score Extrapolation Exploring Data Redundancy in Real-world Image Classification through Data Selection

Reference 67

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:02:28.572992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation aa3e339b-be4c-42c8-82f4-20a543dca637 · outbound

This paper cites Semi-supervised classification with graph convolutional networks.

Effective Data Pruning through Score Extrapolation Semi-supervised classification with graph convolutional networks

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:29.224110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 84555bbb-a794-4b3a-8eee-bca79f5f9a25 · outbound

This paper cites Inductive representation learning on large graphs.Advances in Neural Information Processing Systems (NeurIPS), 30, 2017.

Effective Data Pruning through Score Extrapolation Inductive representation learning on large graphs.Advances in Neural Information Processing Systems (NeurIPS), 30, 2017

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:29.202524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d69638e9-5ca3-491a-ae42-568efa410d1c · outbound

This paper cites Pearson correlation coefficient.

Effective Data Pruning through Score Extrapolation Pearson correlation coefficient

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:29.184200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f62c5aab-b6c6-4d1b-8a57-b7ee13c57835 · outbound

This paper cites Spearman rank correlation.Encyclopedia of Biostatistics, 7, 2005.

Effective Data Pruning through Score Extrapolation Spearman rank correlation.Encyclopedia of Biostatistics, 7, 2005

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:29.164403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 61140051-a7e5-487a-bcf6-cf237d049f6c · outbound

This paper cites Let go of your labels with unsupervised transfer.

Effective Data Pruning through Score Extrapolation Let go of your labels with unsupervised transfer

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:29.141651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4dcde2cf-89e2-4b58-ad2f-342b1fe9752b · outbound

This paper cites Denoising diffusion probabilistic models.Advances in Neural Information Processing Systems (NeurIPS), 33:6840–6851, 2020.

Effective Data Pruning through Score Extrapolation Denoising diffusion probabilistic models.Advances in Neural Information Processing Systems (NeurIPS), 33:6840–6851, 2020

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-07T05:02:28.372646Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc515e11-9f57-467e-88ba-be711f3bfd97 · outbound

This paper cites Deep residual learning for image recognition.

Effective Data Pruning through Score Extrapolation Deep residual learning for image recognition

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-07T05:02:28.381488Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f32c127-c5e3-4051-a81e-12d10b083931 · outbound

This paper cites Wide residual networks.

Effective Data Pruning through Score Extrapolation Wide residual networks

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:29.081201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bd1e2420-09ce-4853-b2bb-0c5c5e2360f1 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Effective Data Pruning through Score Extrapolation DINOv2: Learning Robust Visual Features without Supervision

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-07T05:02:28.399591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 605279b3-0720-4e53-af28-93383b97cc54 · outbound

This paper cites Adam: A method for stochastic optimization.

Effective Data Pruning through Score Extrapolation Adam: A method for stochastic optimization

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-07T05:02:28.411351Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7712647f-9b54-461d-8fe9-ee98f97aa551 · outbound

This paper cites On the importance of initialization and momentum in deep learning.

Effective Data Pruning through Score Extrapolation On the importance of initialization and momentum in deep learning

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:29.037307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 69b4a38d-ced7-4b87-b346-ac609d03fc71 · outbound

This paper cites Sgdr: Stochastic gradient descent with warm restarts.

Effective Data Pruning through Score Extrapolation Sgdr: Stochastic gradient descent with warm restarts

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-07T05:02:28.422583Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7cbeec1a-c732-4330-9cae-66202591b3e3 · outbound

This paper cites Super-convergence: Very fast training of neural networks using large learning rates.

Effective Data Pruning through Score Extrapolation Super-convergence: Very fast training of neural networks using large learning rates

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:02:29.001794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers