Instance-dependent Early Stopping | Paper Citation Record

Observation 8e048ba3-446e-4663-9855-c982b44b668e · outbound

This paper cites Variance Reduction in SGD by Distributed Importance Sampling.

Instance-dependent Early Stopping Variance Reduction in SGD by Distributed Importance Sampling

Reference 1

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Observation cde0bafc-eba4-42da-83c4-6a92d1f51ff8 · outbound

This paper cites Towards understanding sharpness-aware minimization.

Instance-dependent Early Stopping Towards understanding sharpness-aware minimization

Reference 2

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Observation 2b60b726-f017-445c-8527-fff677ed806c · outbound

This paper cites A closer look at memorization in deep networks.

Instance-dependent Early Stopping A closer look at memorization in deep networks

Reference 3

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Observation 3b1fcb80-b219-4b80-a498-2b28e60215cf · outbound

This paper cites Reconciling modern machine-learning practice and the classical bias--variance trade-off.

Instance-dependent Early Stopping Reconciling modern machine-learning practice and the classical bias--variance trade-off

Reference 4

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Observation 06b6e04c-0ed8-43b4-abeb-1affa05f4608 · outbound

This paper cites Curriculum learning.

Instance-dependent Early Stopping Curriculum learning

Reference 5

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Observation a944e403-4b7f-42f9-930d-8a4aa68fc302 · outbound

This paper cites The power of uniform sampling for coresets.

Instance-dependent Early Stopping The power of uniform sampling for coresets

Reference 6

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Observation c3f14ccd-f51c-415c-83c4-463d17041cdc · outbound

This paper cites Language models are few-shot learners.

Instance-dependent Early Stopping Language models are few-shot learners

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.141956Z

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Observation a84fe3ea-3b43-49e9-9b2b-f8532916dd66 · outbound

This paper cites Learning imbalanced datasets with label-distribution-aware margin loss.

Instance-dependent Early Stopping Learning imbalanced datasets with label-distribution-aware margin loss

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.146016Z

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Observation d0728b77-881e-4c78-a1a3-94c84128f7cb · outbound

This paper cites Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping.

Instance-dependent Early Stopping Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.149921Z

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Observation 2ccac311-407a-43d2-aca1-dcc3086aa55f · outbound

This paper cites Active bias: Training more accurate neural networks by emphasizing high variance samples.

Instance-dependent Early Stopping Active bias: Training more accurate neural networks by emphasizing high variance samples

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.154019Z

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Observation 29dd764c-9916-44cf-a9df-c46de759833d · outbound

This paper cites Rethinking Atrous Convolution for Semantic Image Segmentation.

Instance-dependent Early Stopping Rethinking Atrous Convolution for Semantic Image Segmentation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.157767Z

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Observation 22e7138f-efa0-44ee-a206-d1f038404535 · outbound

This paper cites Importance sampling for minibatches.

Instance-dependent Early Stopping Importance sampling for minibatches

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.161966Z

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Observation 1ec14932-ed25-4d2c-a0f0-fd30a05023c7 · outbound

This paper cites Class-balanced loss based on effective number of samples.

Instance-dependent Early Stopping Class-balanced loss based on effective number of samples

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.165655Z

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Observation 439c1cf4-096b-4026-81ba-5ed178b5d781 · outbound

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

Instance-dependent Early Stopping Identifying and attacking the saddle point problem in high-dimensional non-convex optimization

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.169179Z

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Observation 1d323382-db6a-41df-b743-03d08bb79c64 · outbound

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

Instance-dependent Early Stopping Imagenet: A large-scale hierarchical image database

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.172913Z

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Observation 97f7be2e-81c9-43a9-934b-e07f6e5b430e · outbound

This paper cites Sharp minima can generalize for deep nets.

Instance-dependent Early Stopping Sharp minima can generalize for deep nets

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.176762Z

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Observation 65daa6d7-b390-479a-9ef6-4bac19503f90 · outbound

This paper cites Everingham, L.

Instance-dependent Early Stopping Everingham, L

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.180786Z

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Observation 9c3dbe42-7e08-4dcb-81cb-192764977b03 · outbound

This paper cites Everingham, L.

Instance-dependent Early Stopping Everingham, L

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.184497Z

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Observation 7e2650fa-ead3-4380-9de9-d6ecac5cd0b8 · outbound

This paper cites Sharpness-Aware Minimization for Efficiently Improving Generalization.

Instance-dependent Early Stopping Sharpness-Aware Minimization for Efficiently Improving Generalization

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.188439Z

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Observation 989a6aed-3d9b-4a2a-ab24-02131ad9b4ca · outbound

This paper cites CasCast: Skillful High-resolution Precipitation Nowcasting via Cascaded Modelling.

Instance-dependent Early Stopping CasCast: Skillful High-resolution Precipitation Nowcasting via Cascaded Modelling

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.192465Z

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Observation 7189ee7f-847f-4c9a-837a-48706d6324a4 · outbound

This paper cites Deep learning.

Instance-dependent Early Stopping Deep learning

Reference 21

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Observation 1fd25e03-d344-4445-91d0-c16cb2237716 · outbound

This paper cites On calibration of modern neural networks.

Instance-dependent Early Stopping On calibration of modern neural networks

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.200548Z

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Observation 5bc61b0f-ebc9-4b92-a9dc-5d8964ab4368 · outbound

This paper cites On the power of curriculum learning in training deep networks.

Instance-dependent Early Stopping On the power of curriculum learning in training deep networks

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.204274Z

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Observation d154fe14-b3f4-4b1d-a0b1-1c53c1db5754 · outbound

This paper cites Deep residual learning for image recognition.

Instance-dependent Early Stopping Deep residual learning for image recognition

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.208074Z

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Observation 8931630b-f3cf-4c2c-8a9e-7eaf811cc285 · outbound

This paper cites Large-scale Dataset Pruning with Dynamic Uncertainty.

Instance-dependent Early Stopping Large-scale Dataset Pruning with Dynamic Uncertainty

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.211936Z

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Observation 122290dd-2c55-4206-adca-607bfe3498f4 · outbound

This paper cites Deep Learning Scaling is Predictable, Empirically.

Instance-dependent Early Stopping Deep Learning Scaling is Predictable, Empirically

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.216274Z

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Observation 186cc7dc-b3a7-4ca1-ad90-f9aeaf005a6c · outbound

This paper cites Flat minima.

Instance-dependent Early Stopping Flat minima

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.220293Z

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Observation a1b4b04b-df50-42e8-80e0-90881f7afb88 · outbound

This paper cites Ridge regression: Biased estimation for nonorthogonal problems.

Instance-dependent Early Stopping Ridge regression: Biased estimation for nonorthogonal problems

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.224011Z

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Observation eb4e7ecf-fb42-4650-97d3-3089b52c5353 · outbound

This paper cites Improving non-transferable representation learning by harnessing content and style.

Instance-dependent Early Stopping Improving non-transferable representation learning by harnessing content and style

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.227891Z

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Observation 93f9f451-49fb-4039-9b38-7c69de252e6a · outbound

This paper cites Densely connected convolutional networks.

Instance-dependent Early Stopping Densely connected convolutional networks

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.231597Z

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Observation f65376b4-e3ac-4ca4-ab40-d51afeddde97 · outbound

This paper cites Epsilon-coresets for clustering (with outliers) in doubling metrics.

Instance-dependent Early Stopping Epsilon-coresets for clustering (with outliers) in doubling metrics

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.235199Z

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Observation 2b3657bb-155e-4415-b1b6-b95fdd5faf22 · outbound

This paper cites Harnessing Out-Of-Distribution Examples via Augmenting Content and Style.

Instance-dependent Early Stopping Harnessing Out-Of-Distribution Examples via Augmenting Content and Style

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-08-08T12:26:16.811411Z

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Observation 604d2d0d-149b-471f-aa74-0731026fad21 · outbound

This paper cites Robust generalization against photon-limited corruptions via worst-case sharpness minimization.

Instance-dependent Early Stopping Robust generalization against photon-limited corruptions via worst-case sharpness minimization

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.242828Z

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Observation 629c76ef-2194-4fb9-923c-cd5c7ab5b583 · outbound

This paper cites Winning prize comes from losing tickets: Improve invariant learning by exploring variant parameters for out-of-distribution generalization.

Instance-dependent Early Stopping Winning prize comes from losing tickets: Improve invariant learning by exploring variant parameters for out-of-distribution generalization

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.246380Z

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Observation a5bb0037-7baa-48a0-8df9-4b88c836e179 · outbound

This paper cites Coresets for scalable bayesian logistic regression.

Instance-dependent Early Stopping Coresets for scalable bayesian logistic regression

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.249894Z

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Observation bcfaa66e-8d15-4b30-98cf-3f5fd0df6508 · outbound

This paper cites Do We Need Zero Training Loss After Achieving Zero Training Error?.

Instance-dependent Early Stopping Do We Need Zero Training Loss After Achieving Zero Training Error?

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-08-08T12:26:16.795050Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 06c97e87-7ea7-41db-8e80-f661fde2a238 · outbound

This paper cites Accelerating Deep Learning by Focusing on the Biggest Losers.

Instance-dependent Early Stopping Accelerating Deep Learning by Focusing on the Biggest Losers

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.257485Z

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Observation bcb41cb4-b4ed-4750-a78e-972eb7019027 · outbound

This paper cites Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels.

Instance-dependent Early Stopping Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:17.464813Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 310f8f4c-dba1-4227-acfa-568c04bb8f7b · outbound

This paper cites Scaling Laws for Neural Language Models.

Instance-dependent Early Stopping Scaling Laws for Neural Language Models

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.265025Z

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Observation 0ed83091-7e1d-40ab-ad81-994bea5cec38 · outbound

This paper cites Biased Importance Sampling for Deep Neural Network Training.

Instance-dependent Early Stopping Biased Importance Sampling for Deep Neural Network Training

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.268782Z

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Observation 49ad8f25-69b1-45bf-9bed-5d72f7013bcf · outbound

This paper cites Not all samples are created equal: Deep learning with importance sampling.

Instance-dependent Early Stopping Not all samples are created equal: Deep learning with importance sampling

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:17.452730Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9b9e0dda-3d9f-42ab-a6c3-77964b0a0c84 · outbound

This paper cites On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima.

Instance-dependent Early Stopping On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.276790Z

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Observation e4579b8b-28a8-49f7-aaa8-6d84e5ac514a · outbound

This paper cites Uniform convergence of rank-weighted learning.

Instance-dependent Early Stopping Uniform convergence of rank-weighted learning

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:17.440273Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e8210d92-d3bc-4aa2-ac50-0d49290a7ced · outbound

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

Instance-dependent Early Stopping Grad-match: Gradient matching based data subset selection for efficient deep model training

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:17.428930Z

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Observation 7447e734-1bde-4176-a78b-2fc86c0c930f · outbound

This paper cites Glister: Generalization based data subset selection for efficient and robust learning.

Instance-dependent Early Stopping Glister: Generalization based data subset selection for efficient and robust learning

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.287740Z

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Observation 98f3422f-c7a7-44f9-8b86-d97536d7d811 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Instance-dependent Early Stopping Adam: A Method for Stochastic Optimization

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.291458Z

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Observation 9683d4b2-7572-42d5-836e-7331213e6511 · outbound

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

Instance-dependent Early Stopping Learning multiple layers of features from tiny images

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.295039Z

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Observation 0d04276f-de90-4bfd-aa94-7e24db2bb57c · outbound

This paper cites Self-paced learning for latent variable models.

Instance-dependent Early Stopping Self-paced learning for latent variable models

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:17.404371Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c4ab8aa4-83ed-4320-94ac-54d713f21185 · outbound

This paper cites Caltech 101, 2022.

Instance-dependent Early Stopping Caltech 101, 2022

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:17.392531Z

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Observation e18b8a1e-1114-4660-900c-e9a4e45d8ba6 · outbound

This paper cites Towards realistic model selection for semi-supervised learning.

Instance-dependent Early Stopping Towards realistic model selection for semi-supervised learning

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:17.380560Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4d62e6a2-c6ff-449d-bfca-aeaa1a289969 · outbound

This paper cites Stochastic modified equations and adaptive stochastic gradient algorithms.

Instance-dependent Early Stopping Stochastic modified equations and adaptive stochastic gradient algorithms

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:17.368633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a3e99755-66b2-4246-b47f-05bdb8fd7346 · outbound

This paper cites LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models.

Instance-dependent Early Stopping LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.314808Z

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Observation 8646e34d-009b-4d3a-96b5-5458a7756761 · outbound

This paper cites Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks.

Instance-dependent Early Stopping Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.318880Z

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Observation 4705eb20-3a05-4abe-96ad-687878463417 · outbound

This paper cites On the over-memorization during natural, robust and catastrophic overfitting.

Instance-dependent Early Stopping On the over-memorization during natural, robust and catastrophic overfitting

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:17.356152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a55ca637-413a-4d44-a476-e2878444b7a1 · outbound

This paper cites Layer-Aware Analysis of Catastrophic Overfitting: Revealing the Pseudo-Robust Shortcut Dependency.

Instance-dependent Early Stopping Layer-Aware Analysis of Catastrophic Overfitting: Revealing the Pseudo-Robust Shortcut Dependency

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.326389Z

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Observation 2dd4433f-3c78-4cb8-a17d-27bd97845a66 · outbound

This paper cites Eliminating catastrophic overfitting via abnormal adversarial examples regularization.

Instance-dependent Early Stopping Eliminating catastrophic overfitting via abnormal adversarial examples regularization

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:17.343162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 06c20fff-11e0-4068-812f-9aca8e2f61af · outbound

This paper cites Cs-isolate: Extracting hard confident examples by content and style isolation.

Instance-dependent Early Stopping Cs-isolate: Extracting hard confident examples by content and style isolation

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:17.330649Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1d6bab42-033a-4b73-a117-c13784992721 · outbound

This paper cites Learning the latent causal structure for modeling label noise.

Instance-dependent Early Stopping Learning the latent causal structure for modeling label noise

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:17.318101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0e108370-ccde-47cd-8b07-06b8a525e037 · outbound

This paper cites Online Batch Selection for Faster Training of Neural Networks.

Instance-dependent Early Stopping Online Batch Selection for Faster Training of Neural Networks

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.340993Z

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Observation 44f5b713-3f8a-4533-a507-ae77f2fb7e3d · outbound

This paper cites Decoupled Weight Decay Regularization.

Instance-dependent Early Stopping Decoupled Weight Decay Regularization

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.344632Z

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Observation 4ec730b7-e923-47a3-901f-516999309902 · outbound

This paper cites o ren Mindermann, Jan M Brauner, Muhammed T Razzak, Mrinank Sharma, Andreas Kirsch, Winnie Xu, Benedikt H \.

Instance-dependent Early Stopping o ren Mindermann, Jan M Brauner, Muhammed T Razzak, Mrinank Sharma, Andreas Kirsch, Winnie Xu, Benedikt H \

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:17.305822Z

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Observation 658f97fa-f9a2-41a2-95cb-90cf85ca9bbf · outbound

This paper cites o sung. ZAMM-Journal of Applied Mathematics and Mechanics/Zeitschrift f \.

Instance-dependent Early Stopping o sung. ZAMM-Journal of Applied Mathematics and Mechanics/Zeitschrift f \

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:17.293764Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bc993e4a-9f4c-4c6b-908d-04826110cdad · outbound

This paper cites Deep double descent: Where bigger models and more data hurt.

Instance-dependent Early Stopping Deep double descent: Where bigger models and more data hurt

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.355549Z

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Observation 7257081c-295f-4b78-9327-1e1a2eca2d27 · outbound

This paper cites Exploring generalization in deep learning.

Instance-dependent Early Stopping Exploring generalization in deep learning

Reference 64

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Observation eaaec879-2b9c-46d8-9de7-b3b4acce36f7 · outbound

This paper cites Deep learning on a data diet: Finding important examples early in training.

Instance-dependent Early Stopping Deep learning on a data diet: Finding important examples early in training

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:17.266900Z

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Observation da546e06-0393-488b-bde5-6df35e1488bb · outbound

This paper cites Some methods of speeding up the convergence of iteration methods.

Instance-dependent Early Stopping Some methods of speeding up the convergence of iteration methods

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:17.254372Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9677783a-737b-4bc6-a7c8-4ac02478f2fa · outbound

This paper cites Early stopping-but when? In Neural Networks: Tricks of the trade, pp.\ 55--69.

Instance-dependent Early Stopping Early stopping-but when? In Neural Networks: Tricks of the trade, pp.\ 55--69

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:17.242734Z

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Observation 8421adad-a751-4c78-af0e-5b187f025f60 · outbound

This paper cites InfoBatch: Lossless Training Speed Up by Unbiased Dynamic Data Pruning.

Instance-dependent Early Stopping InfoBatch: Lossless Training Speed Up by Unbiased Dynamic Data Pruning

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.373663Z

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Observation d37c84e6-aec6-448b-8477-35f9e1d23efd · outbound

This paper cites Accelerating Deep Learning with Dynamic Data Pruning.

Instance-dependent Early Stopping Accelerating Deep Learning with Dynamic Data Pruning

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.377619Z

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Observation 002392b3-8fa1-4cd4-9f1f-0d41cfa598f4 · outbound

This paper cites Early stopping and non-parametric regression: an optimal data-dependent stopping rule.

Instance-dependent Early Stopping Early stopping and non-parametric regression: an optimal data-dependent stopping rule

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:17.228959Z

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Observation 5f5e2ed1-265a-41b2-8d64-14ef3d068d09 · outbound

This paper cites Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.

Instance-dependent Early Stopping Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.385835Z

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Observation e6e40fe2-bb69-4698-aa27-ef0554216471 · outbound

This paper cites Overfitting in adversarially robust deep learning.

Instance-dependent Early Stopping Overfitting in adversarially robust deep learning

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:17.216866Z

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Observation b8fd6cc1-b0f0-4be9-80cd-530e12608e08 · outbound

This paper cites A stochastic approximation method.

Instance-dependent Early Stopping A stochastic approximation method

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.393977Z

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Observation 4b321480-faa9-4f4e-9410-12edc7bde2b8 · outbound

This paper cites An investigation of why overparameterization exacerbates spurious correlations.

Instance-dependent Early Stopping An investigation of why overparameterization exacerbates spurious correlations

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:17.196860Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 613fb2a4-9cd9-42ff-a914-1f346b003ab2 · outbound

This paper cites Data parameters: A new family of parameters for learning a differentiable curriculum.

Instance-dependent Early Stopping Data parameters: A new family of parameters for learning a differentiable curriculum

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:17.183441Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 59bc6b80-2d66-4cb1-a280-24bc65ca1d4b · outbound

This paper cites Prioritized Experience Replay.

Instance-dependent Early Stopping Prioritized Experience Replay

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.406337Z

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Observation cb3a4bbb-01e0-411b-9ab2-2387bbd01919 · outbound

This paper cites Diversity-Aware Batch Active Learning for Dependency Parsing.

Instance-dependent Early Stopping Diversity-Aware Batch Active Learning for Dependency Parsing

Reference 77

Resolution: verified exact; local_arxiv, observed 2026-08-08T12:26:16.635497Z

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Observation ed2681d6-1327-409b-b062-aa8b9c15a32f · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Instance-dependent Early Stopping Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.414153Z

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Observation ee6519ce-7241-4f1f-8734-74d6d325066e · outbound

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

Instance-dependent Early Stopping Beyond neural scaling laws: beating power law scaling via data pruning

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:17.168693Z

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Observation b6150a52-a145-40bb-9200-b4a7ad0ba3be · outbound

This paper cites Regression shrinkage and selection via the lasso.

Instance-dependent Early Stopping Regression shrinkage and selection via the lasso

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.422297Z

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Observation 2698bd54-f74a-49e4-94de-a93ac828b3a8 · outbound

This paper cites An Empirical Study of Example Forgetting during Deep Neural Network Learning.

Instance-dependent Early Stopping An Empirical Study of Example Forgetting during Deep Neural Network Learning

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.426997Z

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Observation 883f0432-4377-4bed-a129-a547ef66acd5 · outbound

This paper cites Kakurenbo: Adaptively hiding samples in deep neural network training.

Instance-dependent Early Stopping Kakurenbo: Adaptively hiding samples in deep neural network training

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:17.149629Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 662f6b12-edcd-49ee-aa1d-a37f878ef558 · outbound

This paper cites Normalized flat minima: Exploring scale invariant definition of flat minima for neural networks using pac-bayesian analysis.

Instance-dependent Early Stopping Normalized flat minima: Exploring scale invariant definition of flat minima for neural networks using pac-bayesian analysis

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:17.137379Z

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Observation dd1794ec-8da7-4762-9b37-0961d062a7f2 · outbound

This paper cites Optimizing data usage via differentiable rewards.

Instance-dependent Early Stopping Optimizing data usage via differentiable rewards

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:17.126323Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 85b13524-9895-4141-b47d-86e1ca356696 · outbound

This paper cites Computation-efficient deep learning for computer vision: A survey.

Instance-dependent Early Stopping Computation-efficient deep learning for computer vision: A survey

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:17.115108Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0bb0a0b5-6327-42bf-ae7e-05b55b31c5b9 · outbound

This paper cites Efficienttrain++: Generalized curriculum learning for efficient visual backbone training.

Instance-dependent Early Stopping Efficienttrain++: Generalized curriculum learning for efficient visual backbone training

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:17.103369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bedcad5d-82a7-405c-8018-42c7be15a3e3 · outbound

This paper cites Minimal Effort Back Propagation for Convolutional Neural Networks.

Instance-dependent Early Stopping Minimal Effort Back Propagation for Convolutional Neural Networks

Reference 87

Resolution: verified exact; local_arxiv, observed 2026-08-08T12:26:16.597460Z

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Observation 0c4afe7b-9da1-4650-a3c0-b58f39906134 · outbound

This paper cites Self-filtering: A noise-aware sample selection for label noise with confidence penalization.

Instance-dependent Early Stopping Self-filtering: A noise-aware sample selection for label noise with confidence penalization

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:17.091704Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 02903d95-5d02-4914-8010-2ab8a6325105 · outbound

This paper cites Curriculum learning by transfer learning: Theory and experiments with deep networks.

Instance-dependent Early Stopping Curriculum learning by transfer learning: Theory and experiments with deep networks

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:17.079698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation dd9df9f3-525e-413d-a81e-0c89f533f55c · outbound

This paper cites Benign Overfitting in Classification: Provably Counter Label Noise with Larger Models.

Instance-dependent Early Stopping Benign Overfitting in Classification: Provably Counter Label Noise with Larger Models

Reference 90

Resolution: verified exact; local_arxiv, observed 2026-08-08T12:26:16.579418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1bc7f5ce-70ab-4086-b59c-2c65e02bc2a6 · outbound

This paper cites Sharpness minimization algorithms do not only minimize sharpness to achieve better generalization.

Instance-dependent Early Stopping Sharpness minimization algorithms do not only minimize sharpness to achieve better generalization

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:17.068545Z

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Observation 48b949b9-a58d-4c5e-97c5-fc01174b4225 · outbound

This paper cites When do curricula work?, 2021.

Instance-dependent Early Stopping When do curricula work?, 2021

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:17.057989Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4e150756-43a7-47b3-9bc4-a1e9cc3a00be · outbound

This paper cites Mitigating label noise on graphs via topological sample selection.

Instance-dependent Early Stopping Mitigating label noise on graphs via topological sample selection

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:17.046435Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 552c73c2-cff1-4383-b936-9384fa681dbf · outbound

This paper cites Robust early-learning: Hindering the memorization of noisy labels.

Instance-dependent Early Stopping Robust early-learning: Hindering the memorization of noisy labels

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:17.033728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a69a56fe-5505-4844-989a-982dc0f9df21 · outbound

This paper cites Part-dependent label noise: Towards instance-dependent label noise.

Instance-dependent Early Stopping Part-dependent label noise: Towards instance-dependent label noise

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:17.021555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 032aeb4f-c5fb-47ad-acf4-5e1eb662bb4f · outbound

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

Instance-dependent Early Stopping Moderate coreset: A universal method of data selection for real-world data-efficient deep learning

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:17.008732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 394f3f0f-cb89-4a2e-a787-275dfd51d9bc · outbound

This paper cites Refined coreset selection: Towards minimal coreset size under model performance constraints.

Instance-dependent Early Stopping Refined coreset selection: Towards minimal coreset size under model performance constraints

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:16.996100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ae66dbd2-7ae9-4639-9a31-101ec6ed821b · outbound

This paper cites Rethinking bias-variance trade-off for generalization of neural networks.

Instance-dependent Early Stopping Rethinking bias-variance trade-off for generalization of neural networks

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:16.983428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 25b13720-49b4-4142-b011-7c625c1d862d · outbound

This paper cites Dual t: Reducing estimation error for transition matrix in label-noise learning.

Instance-dependent Early Stopping Dual t: Reducing estimation error for transition matrix in label-noise learning

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.498641Z

Source-reported events for the cited work

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Observation 48ea4d41-8f85-4fdb-9f12-60adc4ba774c · outbound

This paper cites Instance-dependent label-noise learning under a structural causal model.

Instance-dependent Early Stopping Instance-dependent label-noise learning under a structural causal model

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:26:16.964748Z

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