Paper Citation Record · LEDGER

Robust Losses from Univariate Base Functions for Noisy-Label Learning

As of 10 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2607.16768.

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pith.paper-citation-record.v1
2607.16768 v1

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

50 of 50 outbound references displayed

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External citation measurements

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Outbound references

Observation 7ee2a873-a509-44f9-9131-d4f64f0edf60 · outbound

This paper cites Deep learning,.

Robust Losses from Univariate Base Functions for Noisy-Label Learning Deep learning,

Reference 1

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Observation 8740d402-7e73-4160-a3fe-9933b626fdbe · outbound

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

Robust Losses from Univariate Base Functions for Noisy-Label Learning A Survey of Label-noise Representation Learning: Past, Present and Future

Reference 2

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Observation a1ea2c58-89ea-44fb-a5a2-b79f69018964 · outbound

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

Robust Losses from Univariate Base Functions for Noisy-Label Learning A closer look at memorization in deep networks,

Reference 3

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Observation 16fe2221-121e-41dd-889b-f72c2eb3e933 · outbound

This paper cites Weak-to-strong gen- eralization: Eliciting strong capabilities with weak su- pervision,.

Robust Losses from Univariate Base Functions for Noisy-Label Learning Weak-to-strong gen- eralization: Eliciting strong capabilities with weak su- pervision,

Reference 4

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Observation 744fb19b-8892-4abd-94d3-95bed84ad36a · outbound

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

Robust Losses from Univariate Base Functions for Noisy-Label Learning Co-teaching: Robust training of deep neural networks with extremely noisy labels,

Reference 5

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Observation 26982c42-193f-4532-8a52-8694f2ee56f7 · outbound

This paper cites Decoupling.

Robust Losses from Univariate Base Functions for Noisy-Label Learning Decoupling

Reference 6

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Observation e3be02e8-3838-4e9a-a83a-5316bf1f5b90 · outbound

This paper cites How does disagreement help generalization against label corruption?.

Robust Losses from Univariate Base Functions for Noisy-Label Learning How does disagreement help generalization against label corruption?

Reference 7

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Observation 19a48e59-9310-44aa-9641-07f7da39e8aa · outbound

This paper cites L DMI: A novel information-theoretic loss function for training deep nets robust to label noise,.

Robust Losses from Univariate Base Functions for Noisy-Label Learning L DMI: A novel information-theoretic loss function for training deep nets robust to label noise,

Reference 8

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Observation 0dea0746-f250-46b4-b660-549ee0736afd · outbound

This paper cites Cleannet: Transfer learning for scalable image classifier training with label noise,.

Robust Losses from Univariate Base Functions for Noisy-Label Learning Cleannet: Transfer learning for scalable image classifier training with label noise,

Reference 9

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Observation 7544534d-5f99-4a6e-91d7-68b84f9ebef4 · outbound

This paper cites Toward robustness against label noise in training deep discriminative neural networks,.

Robust Losses from Univariate Base Functions for Noisy-Label Learning Toward robustness against label noise in training deep discriminative neural networks,

Reference 10

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Observation 5614e583-ec72-412b-b1c9-a80dc6a7fb36 · outbound

This paper cites Learning from noisy labels with distillation,.

Robust Losses from Univariate Base Functions for Noisy-Label Learning Learning from noisy labels with distillation,

Reference 11

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Observation b0e5ebdc-213c-443f-91db-007e4a106632 · outbound

This paper cites Learning from noisy large-scale datasets with minimal supervision,.

Robust Losses from Univariate Base Functions for Noisy-Label Learning Learning from noisy large-scale datasets with minimal supervision,

Reference 12

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Observation e426b1c2-b02c-4d19-bf92-0922a4726247 · outbound

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

Robust Losses from Univariate Base Functions for Noisy-Label Learning Making deep neural networks robust to label noise: A loss correction approach,

Reference 13

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Observation a34b3ed6-5102-4a71-8684-0ab72f687830 · outbound

This paper cites Masking: A new perspective of noisy supervision,.

Robust Losses from Univariate Base Functions for Noisy-Label Learning Masking: A new perspective of noisy supervision,

Reference 14

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Observation 1bee97e6-cc70-4008-a828-f721ee0eec0c · outbound

This paper cites Rethinking the inception architecture for computer vision,.

Robust Losses from Univariate Base Functions for Noisy-Label Learning Rethinking the inception architecture for computer vision,

Reference 15

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Observation aa8360af-7359-49e6-beae-d113c80031ab · outbound

This paper cites Training deep neural-networks using a noise adaptation layer,.

Robust Losses from Univariate Base Functions for Noisy-Label Learning Training deep neural-networks using a noise adaptation layer,

Reference 16

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Observation c4e879db-26ec-4892-8b9f-13d24ceea44f · outbound

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

Robust Losses from Univariate Base Functions for Noisy-Label Learning Robust loss functions under label noise for deep neural networks,

Reference 17

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Observation 77fdc007-c33b-46ed-afee-3b5828879326 · outbound

This paper cites Asymmet- ric loss functions for noise-tolerant learning: Theory and applications,.

Robust Losses from Univariate Base Functions for Noisy-Label Learning Asymmet- ric loss functions for noise-tolerant learning: Theory and applications,

Reference 18

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Observation d84b38fb-96f9-452e-a4c3-d5369f5f9cad · outbound

This paper cites Student loss: Towards the probability assumption in inaccurate su- pervision,.

Robust Losses from Univariate Base Functions for Noisy-Label Learning Student loss: Towards the probability assumption in inaccurate su- pervision,

Reference 19

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Observation 8f492f7f-46ed-446f-a6ba-dfa679d515ef · outbound

This paper cites Joint Asymmetric Loss for Learning with Noisy Labels,.

Robust Losses from Univariate Base Functions for Noisy-Label Learning Joint Asymmetric Loss for Learning with Noisy Labels,

Reference 20

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Observation 1d69574b-7a64-4a25-9d6a-b61026fd2880 · outbound

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

Robust Losses from Univariate Base Functions for Noisy-Label Learning Normalized loss functions for deep learning with noisy labels,

Reference 21

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Observation ca5f3836-937e-4b06-91bc-86c9b2f47e92 · outbound

This paper cites Asym- metric loss functions for learning with noisy labels,.

Robust Losses from Univariate Base Functions for Noisy-Label Learning Asym- metric loss functions for learning with noisy labels,

Reference 22

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Observation fc958500-6d83-4ee3-bb32-21731d2e9aac · outbound

This paper cites Variation-bounded loss for noise-tolerant learning,.

Robust Losses from Univariate Base Functions for Noisy-Label Learning Variation-bounded loss for noise-tolerant learning,

Reference 23

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Observation 51c5f9fa-ecc9-4c11-b263-1f57c92f0046 · outbound

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

Robust Losses from Univariate Base Functions for Noisy-Label Learning Generalized cross entropy loss for training deep neural networks with noisy labels,

Reference 24

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Observation 916ce742-25b5-469a-aeb6-937548f67f03 · outbound

This paper cites Secost: Sequential co-supervision for large scale weakly labeled audio event detection.

Robust Losses from Univariate Base Functions for Noisy-Label Learning Secost: Sequential co-supervision for large scale weakly labeled audio event detection

Reference 25

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Observation 31665937-eac3-4a2c-99da-0ebe3b609e60 · outbound

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

Robust Losses from Univariate Base Functions for Noisy-Label Learning Learning from massive noisy labeled data for image classification,

Reference 26

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Observation 7846f98b-bc9d-48d8-aa91-7f72b78584a7 · outbound

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

Robust Losses from Univariate Base Functions for Noisy-Label Learning Mitigating memorization of noisy labels by clipping the model prediction,

Reference 27

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Observation 16ee7d3a-5946-411f-a6a8-2ed7f59fd2df · outbound

This paper cites Noise tolerance under risk minimization,.

Robust Losses from Univariate Base Functions for Noisy-Label Learning Noise tolerance under risk minimization,

Reference 28

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Observation 81c6f214-17c7-4112-ae18-f76d2c11fd82 · outbound

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

Robust Losses from Univariate Base Functions for Noisy-Label Learning Learning with symmetric label noise: The importance of being unhinged,

Reference 29

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

Robust Losses from Univariate Base Functions for Noisy-Label Learning Symmetric cross entropy for robust learning with noisy labels,

Reference 30

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Observation fe7bc080-cd54-42fb-8678-f6ce2026dd99 · outbound

This paper cites Can cross entropy loss be robust to label noise?.

Robust Losses from Univariate Base Functions for Noisy-Label Learning Can cross entropy loss be robust to label noise?

Reference 31

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Observation 86c330dd-6ebe-4cc3-b579-aef1809fef16 · outbound

This paper cites Generalized jensen- shannon divergence loss for learning with noisy la- bels,.

Robust Losses from Univariate Base Functions for Noisy-Label Learning Generalized jensen- shannon divergence loss for learning with noisy la- bels,

Reference 32

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Observation 5ba52dfe-7048-43ab-8b60-358c492113c7 · outbound

This paper cites Learning with noisy labels via sparse regularization,.

Robust Losses from Univariate Base Functions for Noisy-Label Learning Learning with noisy labels via sparse regularization,

Reference 33

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Observation 1ca0c4bb-3ba4-4c46-b218-202b089ca253 · outbound

This paper cites ϵ- softmax: Approximating one-hot vectors for mitigating label noise,.

Robust Losses from Univariate Base Functions for Noisy-Label Learning ϵ- softmax: Approximating one-hot vectors for mitigating label noise,

Reference 34

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This paper cites NLNL: Negative learning for noisy labels,.

Robust Losses from Univariate Base Functions for Noisy-Label Learning NLNL: Negative learning for noisy labels,

Reference 35

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Robust Losses from Univariate Base Functions for Noisy-Label Learning Joint negative and positive learning for noisy labels,

Reference 36

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This paper cites Active negative loss functions for learning with noisy labels,.

Robust Losses from Univariate Base Functions for Noisy-Label Learning Active negative loss functions for learning with noisy labels,

Reference 37

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

Robust Losses from Univariate Base Functions for Noisy-Label Learning To smooth or not? When label smoothing meets noisy labels,

Reference 38

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This paper cites Can gradient clipping mitigate label noise?.

Robust Losses from Univariate Base Functions for Noisy-Label Learning Can gradient clipping mitigate label noise?

Reference 39

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This paper cites On symmetric losses for learning from corrupted labels,.

Robust Losses from Univariate Base Functions for Noisy-Label Learning On symmetric losses for learning from corrupted labels,

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Robust Losses from Univariate Base Functions for Noisy-Label Learning Part-dependent label noise: Towards instance-dependent label noise,

Reference 41

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Robust Losses from Univariate Base Functions for Noisy-Label Learning Con- vexity, classification, and risk bounds,

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Robust Losses from Univariate Base Functions for Noisy-Label Learning Visualizing data using t-sne,

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Robust Losses from Univariate Base Functions for Noisy-Label Learning Learning multiple layers of features from tiny images,

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

Robust Losses from Univariate Base Functions for Noisy-Label Learning Learning with noisy labels revisited: A study using real-world human annotations,

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Robust Losses from Univariate Base Functions for Noisy-Label Learning WebVision Database: Visual Learning and Understanding from Web Data

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Robust Losses from Univariate Base Functions for Noisy-Label Learning Backpropa- gation applied to handwritten zip code recognition,

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Robust Losses from Univariate Base Functions for Noisy-Label Learning Deep residual learning for image recognition,

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Robust Losses from Univariate Base Functions for Noisy-Label Learning MentorNet: Learning data-driven curriculum for very deep neural networks on corrupted labels,

Reference 49

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Robust Losses from Univariate Base Functions for Noisy-Label Learning Focal loss for dense object detection,

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