$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise | Paper Citation Record

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This paper cites Deep learning.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Deep learning

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This paper cites A Survey of Label-noise Representation Learning: Past, Present and Future.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise A Survey of Label-noise Representation Learning: Past, Present and Future

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

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise A closer look at memorization in deep networks

Reference 3

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This paper cites Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision

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This paper cites Robust loss functions under label noise for deep neural networks.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Robust loss functions under label noise for deep neural networks

Reference 5

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This paper cites Normalized loss functions for deep learning with noisy labels.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Normalized loss functions for deep learning with noisy labels

Reference 6

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This paper cites Asymmetric loss functions for learning with noisy labels.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Asymmetric loss functions for learning with noisy labels

Reference 7

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This paper cites Label distributionally robust losses for multi-class classification: Consistency, robustness and adaptivity.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Label distributionally robust losses for multi-class classification: Consistency, robustness and adaptivity

Reference 8

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This paper cites Noise tolerance under risk minimization.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Noise tolerance under risk minimization

Reference 9

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This paper cites Learning with symmetric label noise: The importance of being unhinged.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Learning with symmetric label noise: The importance of being unhinged

Reference 10

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This paper cites Generalized cross entropy loss for training deep neural networks with noisy labels.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Generalized cross entropy loss for training deep neural networks with noisy labels

Reference 11

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Observation 987a7090-3d69-4577-94f7-d62353e8dc15 · outbound

This paper cites Symmetric cross entropy for robust learning with noisy labels.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Symmetric cross entropy for robust learning with noisy labels

Reference 12

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This paper cites Generalized jensen-shannon divergence loss for learning with noisy labels.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Generalized jensen-shannon divergence loss for learning with noisy labels

Reference 13

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This paper cites Learning with noisy labels via sparse regularization.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Learning with noisy labels via sparse regularization

Reference 14

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This paper cites Non-negative matrix factorization with sparseness constraints.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Non-negative matrix factorization with sparseness constraints

Reference 15

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This paper cites Variance-enlarged poisson learning for graph-based semi-supervised learning with extremely sparse labeled data.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Variance-enlarged poisson learning for graph-based semi-supervised learning with extremely sparse labeled data

Reference 16

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This paper cites On the consistency of top-k surrogate losses.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise On the consistency of top-k surrogate losses

Reference 17

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This paper cites Convexity, classification, and risk bounds.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Convexity, classification, and risk bounds

Reference 18

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This paper cites Focal loss for dense object detection.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Focal loss for dense object detection

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This paper cites Understanding and improving early stopping for learning with noisy labels.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Understanding and improving early stopping for learning with noisy labels

Reference 20

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This paper cites Co-teaching: Robust training of deep neural networks with extremely noisy labels.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Co-teaching: Robust training of deep neural networks with extremely noisy labels

Reference 21

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This paper cites Fixmatch: Simplifying semi- supervised learning with consistency and confidence.Advances in neural information processing systems, 33:596–608, 2020.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Fixmatch: Simplifying semi- supervised learning with consistency and confidence.Advances in neural information processing systems, 33:596–608, 2020

Reference 22

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This paper cites mixup: Beyond empirical risk minimization.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise mixup: Beyond empirical risk minimization

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

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Learning multiple layers of features from tiny images

Reference 24

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This paper cites Nlnl: Negative learning for noisy labels.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Nlnl: Negative learning for noisy labels

Reference 25

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This paper cites Mitigating memorization of noisy labels by clipping the model prediction.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Mitigating memorization of noisy labels by clipping the model prediction

Reference 26

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This paper cites Visualizing data using t-sne.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Visualizing data using t-sne

Reference 27

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This paper cites Learning with noisy labels revisited: A study using real-world human annotations.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Learning with noisy labels revisited: A study using real-world human annotations

Reference 28

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This paper cites Making deep neural networks robust to label noise: A loss correction approach.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Making deep neural networks robust to label noise: A loss correction approach

Reference 29

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This paper cites Are anchor points really indispensable in label-noise learning? Advances in neural information processing systems, 32, 2019.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Are anchor points really indispensable in label-noise learning? Advances in neural information processing systems, 32, 2019

Reference 30

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This paper cites Peer loss functions: Learning from noisy labels without knowing noise rates.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Peer loss functions: Learning from noisy labels without knowing noise rates

Reference 31

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This paper cites When optimizing f-divergence is robust with label noise.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise When optimizing f-divergence is robust with label noise

Reference 32

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This paper cites To smooth or not? when label smoothing meets noisy labels.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise To smooth or not? when label smoothing meets noisy labels

Reference 33

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This paper cites Provably end-to-end label-noise learning without anchor points.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Provably end-to-end label-noise learning without anchor points

Reference 34

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This paper cites Asymmetric loss functions for noise-tolerant learning: Theory and applications.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Asymmetric loss functions for noise-tolerant learning: Theory and applications

Reference 35

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This paper cites How does disagreement help generalization against label corruption? In International Conference on Machine Learning, pages 7164–7173.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise How does disagreement help generalization against label corruption? In International Conference on Machine Learning, pages 7164–7173

Reference 36

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This paper cites Combating noisy labels by agreement: A joint training method with co-regularization.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Combating noisy labels by agreement: A joint training method with co-regularization

Reference 37

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This paper cites Early- learning regularization prevents memorization of noisy labels.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Early- learning regularization prevents memorization of noisy labels

Reference 38

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This paper cites Dividemix: Learning with noisy labels as semi-supervised learning.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Dividemix: Learning with noisy labels as semi-supervised learning

Reference 39

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This paper cites Learning with instance-dependent label noise: A sample sieve approach.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Learning with instance-dependent label noise: A sample sieve approach

Reference 40

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Observation b26cb3e4-a8c8-4d06-acc3-2b3ec9fe12fc · outbound

This paper cites A second-order approach to learning with instance- dependent label noise.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise A second-order approach to learning with instance- dependent label noise

Reference 41

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This paper cites Robust training under label noise by over- parameterization.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Robust training under label noise by over- parameterization

Reference 42

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This paper cites Rethinking Noisy Label Learning in Real-world Annotation Scenarios from the Noise-type Perspective.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Rethinking Noisy Label Learning in Real-world Annotation Scenarios from the Noise-type Perspective

Reference 43

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This paper cites WebVision Database: Visual Learning and Understanding from Web Data.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise WebVision Database: Visual Learning and Understanding from Web Data

Reference 44

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

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Imagenet: A large- scale hierarchical image database

Reference 45

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This paper cites Learning from massive noisy labeled data for image classification.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Learning from massive noisy labeled data for image classification

Reference 46

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$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Randaugment: Practical automated data augmentation with a reduced search space

Reference 47

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This paper cites mean±std.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise mean±std

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This paper cites Specifically, we provide a simple yet effective method for mitigating label noise with elaborated descriptions and theoretical results.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Specifically, we provide a simple yet effective method for mitigating label noise with elaborated descriptions and theoretical results

Reference 49

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This paper cites Limitations.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Limitations

Reference 50

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Observation c8fd7ad1-56e0-43db-a911-b5fb19c1470c · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include theoretical results.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Guidelines: • The answer NA means that the paper does not include theoretical results

Reference 51

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This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Guidelines: • The answer NA means that the paper does not include experiments

Reference 52

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This paper cites And the datasets are obtained from open source.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise And the datasets are obtained from open source

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:04:10.558466Z

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Observation 5e56663a-5d1d-4311-8978-2e9b0dcee743 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Guidelines: • The answer NA means that the paper does not include experiments

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:04:10.369007Z

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 19c91a79-1487-483b-990f-387aa0c7a912 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Guidelines: • The answer NA means that the paper does not include experiments

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:04:09.348617Z

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 cb18239b-862e-4dac-8148-0ff7c4197c78 · outbound

This paper cites All experiments are implemented by PyTorch and are conducted on NVIDIA GeForce RTX 4090.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise All experiments are implemented by PyTorch and are conducted on NVIDIA GeForce RTX 4090

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:04:08.560678Z

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 b4189224-db90-4b85-b137-b4937abe927b · outbound

This paper cites Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:04:08.424698Z

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 a73316d2-2863-4c2e-8965-8f0f1dd1ebe8 · outbound

This paper cites Guidelines: • The answer NA means that there is no societal impact of the work performed.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Guidelines: • The answer NA means that there is no societal impact of the work performed

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:04:08.185749Z

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 cb39a0dd-9ac2-47a0-9e82-be3f0a34b9e8 · outbound

This paper cites Guidelines: • The answer NA means that the paper poses no such risks.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Guidelines: • The answer NA means that the paper poses no such risks

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:04:08.028313Z

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 8130c382-108e-41fd-8ea5-1f5f1ac3718b · outbound

This paper cites Guidelines: • The answer NA means that the paper does not use existing assets.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Guidelines: • The answer NA means that the paper does not use existing assets

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:04:07.853795Z

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 1953efd3-1d93-4a3d-927d-2318c5b42c31 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not release new assets.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Guidelines: • The answer NA means that the paper does not release new assets

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:04:07.700507Z

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 10eaddad-30e7-475c-9d89-a0e46a1de2dd · outbound

This paper cites Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:04:07.456468Z

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 4a6a4d11-2e34-4ce0-8236-005db7f14cc2 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:04:07.253518Z

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.

Coverage vector

Reference resolution

Outbound references

Pith citing papers