Dataset Distillation by Influence Matching | Paper Citation Record

Observation 11999c20-d137-4931-b2cc-3fb8cf06bf81 · outbound

This paper cites Automatic differentiation in py- torch.

Dataset Distillation by Influence Matching Automatic differentiation in py- torch

Reference 1

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Observation aa9972a3-b9b0-41c8-93f2-811dfe54650b · outbound

This paper cites Learning multiple layers of features from tiny images.Technical report, 2009.

Dataset Distillation by Influence Matching Learning multiple layers of features from tiny images.Technical report, 2009

Reference 2

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Observation 64077153-a916-42d5-aa27-9b9f0b7d5ce2 · outbound

This paper cites Neural Networks as Kernel Learners: The Silent Alignment Effect.

Dataset Distillation by Influence Matching Neural Networks as Kernel Learners: The Silent Alignment Effect

Reference 3

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Observation 40a2b0d1-e951-4926-96ab-7098dd85e707 · outbound

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

Dataset Distillation by Influence Matching On second- order group influence functions for black-box predictions

Reference 4

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Observation a10ec697-21b6-4998-bd36-d44143310fe9 · outbound

This paper cites Flexible dataset distillation: Learn labels instead of images.

Dataset Distillation by Influence Matching Flexible dataset distillation: Learn labels instead of images

Reference 5

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Observation 0287b1df-05e2-496b-bcda-d4eaa4aac4cf · outbound

This paper cites Smith, and Karen Si- monyan.

Dataset Distillation by Influence Matching Smith, and Karen Si- monyan

Reference 6

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Observation e20ba28f-4884-461d-8e4f-18bb710c33f5 · outbound

This paper cites Dataset distillation by matching training trajectories.

Dataset Distillation by Influence Matching Dataset distillation by matching training trajectories

Reference 7

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Observation bf81b67d-75fe-44ee-a1e9-45980400130c · outbound

This paper cites Generalizing dataset distillation via deep generative prior.

Dataset Distillation by Influence Matching Generalizing dataset distillation via deep generative prior

Reference 8

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Observation 5b0803a8-07fc-4db6-a5aa-9bb07843b04e · outbound

This paper cites Rkhs-shap: Shapley values for kernel methods.Ad- vances in neural information processing systems, 35:13050– 13063, 2022.

Dataset Distillation by Influence Matching Rkhs-shap: Shapley values for kernel methods.Ad- vances in neural information processing systems, 35:13050– 13063, 2022

Reference 9

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Observation b7bf0901-9ef4-4e75-ae12-5ae45a7e43bf · outbound

This paper cites Aligning effective tokens with video anomaly in large language models.

Dataset Distillation by Influence Matching Aligning effective tokens with video anomaly in large language models

Reference 10

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Observation 836cf301-42ec-415f-8809-31b7d9fe05af · outbound

This paper cites The loss surfaces of multi- layer networks.Journal of Machine Learning Research, 38: 192–204, 2015.

Dataset Distillation by Influence Matching The loss surfaces of multi- layer networks.Journal of Machine Learning Research, 38: 192–204, 2015

Reference 11

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Observation c3acaa23-037b-48c4-b888-4112c9fc1d62 · outbound

This paper cites Assessment of local influence.Journal of the Royal Statistical Society Series B: Statistical Methodology, 48(2):133–155, 1986.

Dataset Distillation by Influence Matching Assessment of local influence.Journal of the Royal Statistical Society Series B: Statistical Methodology, 48(2):133–155, 1986

Reference 12

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Observation 975ffc7d-f499-428a-93e8-f997ed60e9a4 · outbound

This paper cites Scaling up dataset distillation to imagenet-1k with constant memory.

Dataset Distillation by Influence Matching Scaling up dataset distillation to imagenet-1k with constant memory

Reference 13

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Observation 77c2633c-ef95-4326-830f-2a5b0fb4712e · outbound

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

Dataset Distillation by Influence Matching Iden- tifying and attacking the saddle point problem in high- dimensional non-convex optimization

Reference 14

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Observation 092cef57-214f-4fb8-b54e-2e8e7b933aa5 · outbound

This paper cites Remember the past: Distilling datasets into addressable memories for neural net- works.Advances in Neural Information Processing Systems, 35:34391–34404, 2022.

Dataset Distillation by Influence Matching Remember the past: Distilling datasets into addressable memories for neural net- works.Advances in Neural Information Processing Systems, 35:34391–34404, 2022

Reference 15

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Observation 1ca6d411-bccf-45f8-a653-cd842c82c6c9 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Dataset Distillation by Influence Matching BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 16

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Observation cf874c07-f478-4331-ba56-1056ffe942d0 · outbound

This paper cites Privacy for free: How does dataset condensation help privacy? InInternational Conference on Machine Learning, pages 5378–5396.

Dataset Distillation by Influence Matching Privacy for free: How does dataset condensation help privacy? InInternational Conference on Machine Learning, pages 5378–5396

Reference 17

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Observation 3460be37-79e1-429b-8711-6c5bf564e8f3 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.ICLR, 2021.

Dataset Distillation by Influence Matching An image is worth 16x16 words: Transformers for image recognition at scale.ICLR, 2021

Reference 18

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Observation 461538f7-ac17-4e9e-b9f2-ea9611dd22ee · outbound

This paper cites Minimizing the accumulated trajectory error to improve dataset distillation.

Dataset Distillation by Influence Matching Minimizing the accumulated trajectory error to improve dataset distillation

Reference 19

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Observation c53e610e-c5b7-4f22-a6d8-b81f48e2bae6 · outbound

This paper cites Sequential subset matching for dataset distillation.Advances in Neural Infor- mation Processing Systems, 36, 2024.

Dataset Distillation by Influence Matching Sequential subset matching for dataset distillation.Advances in Neural Infor- mation Processing Systems, 36, 2024

Reference 20

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Observation a96a097a-d984-49fd-844f-c8cd6abe1f4f · outbound

This paper cites Embarrassingly simple dataset distillation.

Dataset Distillation by Influence Matching Embarrassingly simple dataset distillation

Reference 21

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Observation 76d40b8f-0378-406f-91dc-8bc758ff267d · outbound

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

Dataset Distillation by Influence Matching Studying Large Language Model Generalization with Influence Functions

Reference 22

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Observation 6b37a238-783e-47b4-afdb-b736eb8979f9 · outbound

This paper cites Summarizing Stream Data for Memory-Constrained Online Continual Learning.

Dataset Distillation by Influence Matching Summarizing Stream Data for Memory-Constrained Online Continual Learning

Reference 23

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Observation e8689628-dbef-4007-b3ca-bba7c47edcc5 · outbound

This paper cites Towards Lossless Dataset Distillation via Difficulty-Aligned Trajectory Matching.

Dataset Distillation by Influence Matching Towards Lossless Dataset Distillation via Difficulty-Aligned Trajectory Matching

Reference 24

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Observation 47a0272b-c6df-437a-a999-0609b1f00ccc · outbound

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

Dataset Distillation by Influence Matching Training Data Influence Analysis and Estimation: A Survey

Reference 25

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Observation 3b4bbb79-146d-462a-b2e9-dc41e9c430a6 · outbound

This paper cites Data cleansing for models trained with sgd.Advances in Neural Information Processing Systems, 32, 2019.

Dataset Distillation by Influence Matching Data cleansing for models trained with sgd.Advances in Neural Information Processing Systems, 32, 2019

Reference 26

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Observation e3c608f2-73a2-4cfb-bce4-96c93dc3a0b4 · outbound

This paper cites Deep residual learning for image recognition.

Dataset Distillation by Influence Matching Deep residual learning for image recognition

Reference 27

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Observation 770a2f6f-6a7e-44a3-ba86-ca080ef2647d · outbound

This paper cites an unresolved cited work.

Dataset Distillation by Influence Matching Unresolved cited work

Reference 28

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Observation 0e49d098-1b06-4426-bb7d-bb781f85ec09 · outbound

This paper cites Understanding black-box pre- dictions via influence functions.

Dataset Distillation by Influence Matching Understanding black-box pre- dictions via influence functions

Reference 29

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Observation 2668f4fd-a928-4031-82ca-420cf248da28 · outbound

This paper cites an unresolved cited work.

Dataset Distillation by Influence Matching Unresolved cited work

Reference 30

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Observation 306768cd-e2ae-447f-9207-16cc177f9a01 · outbound

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

Dataset Distillation by Influence Matching DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models

Reference 31

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Observation 6c2c4b64-65fa-4c88-9e89-381498abc2f2 · outbound

This paper cites Lecun, L.

Dataset Distillation by Influence Matching Lecun, L

Reference 32

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Observation c69c1ac0-db15-4d8d-9930-19a2bbdbfcea · outbound

This paper cites Awesome dataset distillation.

Dataset Distillation by Influence Matching Awesome dataset distillation

Reference 33

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Observation e8ca8f30-7f87-4fc7-9c6d-dc8409080e01 · outbound

This paper cites Mars3d: A plug-and-play motion- aware model for semantic segmentation on multi-scan 3d point clouds.

Dataset Distillation by Influence Matching Mars3d: A plug-and-play motion- aware model for semantic segmentation on multi-scan 3d point clouds

Reference 34

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Observation c78983c8-41ee-4bc4-ae91-c7d0b7fc473f · outbound

This paper cites Very deep convolutional neural network based image classification using small training sample size.

Dataset Distillation by Influence Matching Very deep convolutional neural network based image classification using small training sample size

Reference 35

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Observation 018d64d9-12ea-4e53-947d-145726717bd0 · outbound

This paper cites Efficient dataset distillation using random feature approxima- tion.Advances in Neural Information Processing Systems, 35:13877–13891, 2022.

Dataset Distillation by Influence Matching Efficient dataset distillation using random feature approxima- tion.Advances in Neural Information Processing Systems, 35:13877–13891, 2022

Reference 36

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Observation ddd41453-4402-4ac7-a7dc-8206e87a6892 · outbound

This paper cites Herding dynamical weights to learn.

Dataset Distillation by Influence Matching Herding dynamical weights to learn

Reference 37

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Observation 53d75f08-b5f9-4601-91f4-73f6d78c0569 · outbound

This paper cites Dataset meta-learning from kernel ridge-regression.

Dataset Distillation by Influence Matching Dataset meta-learning from kernel ridge-regression

Reference 38

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Observation b6c76450-281c-4ef6-b4cf-f97cb34147c6 · outbound

This paper cites Pearlmutter.

Dataset Distillation by Influence Matching Pearlmutter

Reference 39

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Observation fa8e7fe4-6ec6-4a50-97c3-fb230245038e · outbound

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

Dataset Distillation by Influence Matching Flickr30k entities: Collecting region-to-phrase correspon- dences for richer image-to-sentence models

Reference 40

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Observation 80ed8efe-3b24-45f2-807e-4eba225cedd1 · outbound

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

Dataset Distillation by Influence Matching Estimating Training Data Influence by Tracing Gradient Descent

Reference 41

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Observation cce01f84-efa9-4724-977e-06e2b3c1b923 · outbound

This paper cites Datadam: Efficient dataset distillation with attention matching.

Dataset Distillation by Influence Matching Datadam: Efficient dataset distillation with attention matching

Reference 42

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Observation f3a888e5-d854-432e-a09d-1e717de8e62f · outbound

This paper cites Scaling up influence functions.

Dataset Distillation by Influence Matching Scaling up influence functions

Reference 43

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Observation 2f896dfb-3be5-48b2-a71f-77c856631cb3 · outbound

This paper cites Theoretical and practical perspectives on what influence functions do.Advances in Neural Information Pro- cessing Systems, 36, 2024.

Dataset Distillation by Influence Matching Theoretical and practical perspectives on what influence functions do.Advances in Neural Information Pro- cessing Systems, 36, 2024

Reference 44

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Observation cc96d57c-4b85-4872-8c9c-dd3938ea91a7 · outbound

This paper cites A value for n-person games.

Dataset Distillation by Influence Matching A value for n-person games

Reference 45

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Observation d74119ce-4eca-4ee4-b067-18df8475c5db · outbound

This paper cites Generalized Large-Scale Data Condensation via Various Backbone and Statistical Matching.

Dataset Distillation by Influence Matching Generalized Large-Scale Data Condensation via Various Backbone and Statistical Matching

Reference 46

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Observation 1ce0fdd0-e51f-4b33-a832-bafd3d9897f2 · outbound

This paper cites Soft-label dataset distillation and text dataset distillation.

Dataset Distillation by Influence Matching Soft-label dataset distillation and text dataset distillation

Reference 47

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Observation 441a0324-9876-49d6-ada0-cddeeddcd0bc · outbound

This paper cites On the Diversity and Realism of Distilled Dataset: An Efficient Dataset Distillation Paradigm.

Dataset Distillation by Influence Matching On the Diversity and Realism of Distilled Dataset: An Efficient Dataset Distillation Paradigm

Reference 48

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Observation 2ac0803b-5029-4f00-904e-b6a559c67d38 · outbound

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

Dataset Distillation by Influence Matching Data pruning via moving-one- sample-out

Reference 49

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Observation 8801f3d7-5b01-4e58-9b7d-3aa941d9e708 · outbound

This paper cites Understanding data influence with differential approximation, 2025.

Dataset Distillation by Influence Matching Understanding data influence with differential approximation, 2025

Reference 50

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Observation 8cef67ca-9a50-43d2-98a9-779807e9b1c7 · outbound

This paper cites Understanding data influence in reinforcement finetuning.

Dataset Distillation by Influence Matching Understanding data influence in reinforcement finetuning

Reference 51

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Observation 12db354b-e0e0-48c0-89a6-b0a365863da0 · outbound

This paper cites Cafe: Learning to condense dataset by align- ing features.

Dataset Distillation by Influence Matching Cafe: Learning to condense dataset by align- ing features

Reference 52

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Observation 2eb0d057-8e57-4e71-a991-97ee812ae779 · outbound

This paper cites DiM: Distilling Dataset into Generative Model.

Dataset Distillation by Influence Matching DiM: Distilling Dataset into Generative Model

Reference 53

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Observation 28430e8f-5e46-4cea-9667-d3f91388ef57 · outbound

This paper cites Dataset distil- lation with neural characteristic function: A minmax perspec- tive, 2025.

Dataset Distillation by Influence Matching Dataset distil- lation with neural characteristic function: A minmax perspec- tive, 2025

Reference 54

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Observation d24a4a77-3688-4054-a729-2c4b51a5474a · outbound

This paper cites Dataset Distillation.

Dataset Distillation by Influence Matching Dataset Distillation

Reference 55

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Observation 1317d831-5eb3-446c-a4b3-6ffd8a455810 · outbound

This paper cites Saco loss: Sample-wise affinity consistency for vision-language pre-training.

Dataset Distillation by Influence Matching Saco loss: Sample-wise affinity consistency for vision-language pre-training

Reference 56

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Observation 1b8908d7-dacc-47d2-8115-0e734121e761 · outbound

This paper cites Mixture- of-scores: Robust image-text data valuation via three lines of code.

Dataset Distillation by Influence Matching Mixture- of-scores: Robust image-text data valuation via three lines of code

Reference 57

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Observation 8aadeb01-ae29-4495-a6f8-35df6eb472ee · outbound

This paper cites Vision-Language Dataset Distillation.

Dataset Distillation by Influence Matching Vision-Language Dataset Distillation

Reference 58

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Observation ebb5280d-b2df-4cb0-90cc-4b4439e44744 · outbound

This paper cites Dreamomni2: Multimodal instruction-based editing and generation, 2025.

Dataset Distillation by Influence Matching Dreamomni2: Multimodal instruction-based editing and generation, 2025

Reference 59

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Observation 50bc4e48-7ad6-4eb9-9e27-29334ba4b777 · outbound

This paper cites an unresolved cited work.

Dataset Distillation by Influence Matching Unresolved cited work

Reference 60

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Observation 99af39e1-4063-469a-b97a-28919c50b77d · outbound

This paper cites An efficient dataset condensation plugin and its application to continual learning.

Dataset Distillation by Influence Matching An efficient dataset condensation plugin and its application to continual learning

Reference 61

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Observation ded58d32-475c-4b4a-b155-404fb8c6aac7 · outbound

This paper cites Dataset pruning: Reducing training data by ex- amining generalization influence.

Dataset Distillation by Influence Matching Dataset pruning: Reducing training data by ex- amining generalization influence

Reference 62

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Observation e3d13878-e8cb-495e-8722-f396671c2578 · outbound

This paper cites Squeeze, recover and relabel: Dataset condensation at imagenet scale from a new perspective.Advances in Neural Information Processing Systems, 36, 2024.

Dataset Distillation by Influence Matching Squeeze, recover and relabel: Dataset condensation at imagenet scale from a new perspective.Advances in Neural Information Processing Systems, 36, 2024

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-01T19:49:27.987345Z

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Observation 67829859-806e-4e9b-b34d-4a255ff13aca · outbound

This paper cites Dataset Condensation via Generative Model.

Dataset Distillation by Influence Matching Dataset Condensation via Generative Model

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-01T19:49:28.099828Z

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Observation 36f572a1-3b09-4438-9719-8efd4cd1e775 · outbound

This paper cites M3d: Dataset condensation by minimizing maximum mean discrepancy.

Dataset Distillation by Influence Matching M3d: Dataset condensation by minimizing maximum mean discrepancy

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-01T19:49:28.277353Z

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Observation dfb48346-1531-486c-86d8-0bd02f11cc7b · outbound

This paper cites Dataset Condensation with Distribution Matching.

Dataset Distillation by Influence Matching Dataset Condensation with Distribution Matching

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-01T19:49:28.422760Z

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Observation e4e355b4-57fc-4b06-a86f-93705fee46a7 · outbound

This paper cites Dataset condensation with differ- entiable siamese augmentation.

Dataset Distillation by Influence Matching Dataset condensation with differ- entiable siamese augmentation

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-01T19:49:28.561108Z

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Observation f93bec7d-f885-436b-9668-302e605fe255 · outbound

This paper cites Dataset condensation with gradient matching.

Dataset Distillation by Influence Matching Dataset condensation with gradient matching

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-01T19:49:28.735929Z

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Observation f6af7f6c-152e-4828-a446-c8b2a46843e8 · outbound

This paper cites Im- proved distribution matching for dataset condensation.

Dataset Distillation by Influence Matching Im- proved distribution matching for dataset condensation

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-01T19:49:28.905657Z

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Observation 016c0f35-4930-4bfd-bb66-fc702aa533a2 · outbound

This paper cites Equipping vision foundation model with mixture of experts for out-of-distribution detection.

Dataset Distillation by Influence Matching Equipping vision foundation model with mixture of experts for out-of-distribution detection

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-01T19:49:29.011485Z

Source-reported events for the cited work

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Observation 2b4fc1a1-796e-4aa2-bda4-96cce3630599 · outbound

This paper cites Dataset distillation using neural feature regression.Advances in Neu- ral Information Processing Systems, 35:9813–9827, 2022.

Dataset Distillation by Influence Matching Dataset distillation using neural feature regression.Advances in Neu- ral Information Processing Systems, 35:9813–9827, 2022

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-01T19:49:29.111971Z

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