Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels | Paper Citation Record

Observation 1e40b76d-98d0-4e15-85b8-acda9915a59a · outbound

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

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Asymmetric loss functions for learning with noisy labels,

Reference 2

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Observation 8ed0522b-4c8d-4daa-bfc1-3cef6288925c · outbound

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

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Label distributionally robust losses for multi-class classification: Consistency, robustness and adaptivity,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.977938Z

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Observation 053e2d75-c832-4b12-9077-8d85c1976fa3 · outbound

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

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Asymmetric loss functions for noise-tolerant learning: Theory and applications,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.963072Z

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Observation 73718063-76d8-4482-abc6-8d534d4308f8 · outbound

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

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels ϵ-softmax: Approximating one- hot vectors for mitigating label noise,

Reference 5

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Observation a7db535d-ee84-45c3-ae66-11718bf991a8 · outbound

This paper cites Learning with noisy labels,.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Learning with noisy labels,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.931891Z

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Observation 4ab22b12-287a-4a84-a3e0-3e1b369044cf · outbound

This paper cites Classification with noisy labels by importance reweighting,.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Classification with noisy labels by importance reweighting,

Reference 7

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Observation ca30c358-b221-42b5-82ef-ccce2a6003e7 · outbound

This paper cites Are anchor points really indispensable in label-noise learning?,.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Are anchor points really indispensable in label-noise learning?,

Reference 9

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Observation b53c419e-ae76-40d0-a98f-668db5c1873b · outbound

This paper cites Dirichlet-based per-sample weighting by transi- tion matrix for noisy label learning,.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Dirichlet-based per-sample weighting by transi- tion matrix for noisy label learning,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.864215Z

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Observation 3598fed4-7304-47c3-9216-0a889c07770d · outbound

This paper cites Contrast to divide: Self-supervised pre-training for learning with noisy labels,.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Contrast to divide: Self-supervised pre-training for learning with noisy labels,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.848308Z

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Observation 095cd6b3-c958-41e3-8086-7ed616b50095 · outbound

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

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Early-learning regularization prevents memorization of noisy labels,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.831209Z

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Observation bdb32e78-9c1f-40a7-8a38-b57d0a2c97cc · outbound

This paper cites mixup: Beyond empirical risk minimization,.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels mixup: Beyond empirical risk minimization,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.815452Z

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Observation d1bb8be4-f55a-4ab6-b9d6-9d6bcdaec46c · outbound

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

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Dividemix: Learning with noisy labels as semi-supervised learning,

Reference 14

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Observation 04bca06b-f0d8-44a6-b64a-cd8dbed1575c · outbound

This paper cites Robust training of deep neural networks with extremely noisy labels,.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Robust training of deep neural networks with extremely noisy labels,

Reference 15

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Observation 4409c445-b92c-4e8f-bccb-d653a4c60d20 · outbound

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

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Robust training under label noise by over-parameterization,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.771873Z

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Observation e04b0f24-5b23-4d59-8290-d2c401ea5834 · outbound

This paper cites Csot: Curriculum and structure-aware optimal transport for learning with noisy labels,.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Csot: Curriculum and structure-aware optimal transport for learning with noisy labels,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.756404Z

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Observation d6b316eb-ee56-49c5-acc6-daf027c9614d · outbound

This paper cites Fixmatch: Simplifying semi-supervised learning with consistency and confidence,.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Fixmatch: Simplifying semi-supervised learning with consistency and confidence,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.740653Z

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Observation 7abfe8c2-d3ef-4743-ad03-5e512b5026c2 · outbound

This paper cites L2B: Learning to bootstrap robust models for combating label noise,.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels L2B: Learning to bootstrap robust models for combating label noise,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.724794Z

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Observation 2b169b21-d2f4-490b-847b-2b6a44715fcd · outbound

This paper cites Badlabel: A robust perspective on evaluating and enhancing label-noise learning,.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Badlabel: A robust perspective on evaluating and enhancing label-noise learning,

Reference 20

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Observation 64de6773-31e4-4859-951a-72ee7df6602c · outbound

This paper cites Towards deep learning models resistant to adversarial attacks,.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Towards deep learning models resistant to adversarial attacks,

Reference 21

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Observation d32f72f3-9b48-4ff0-945a-6aed6278d05c · outbound

This paper cites Learning to reweight examples for robust deep learning,.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Learning to reweight examples for robust deep learning,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.679970Z

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Observation 9a3aa928-e818-4114-b555-170b0a98b079 · outbound

This paper cites Combating noisy labels with sample selection by mining high-discrepancy examples,.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Combating noisy labels with sample selection by mining high-discrepancy examples,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.664972Z

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Observation 6d5eb326-1907-43ca-84a3-9597610da3ab · outbound

This paper cites Focal loss for dense object detection,.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Focal loss for dense object detection,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.650251Z

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Observation 50ef488f-c405-4ebe-81f8-4671e11321d4 · outbound

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

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Robust loss functions under label noise for deep neural networks,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.635029Z

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Observation eab4654a-876d-44f6-a98c-014de55183f4 · outbound

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

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Generalized cross entropy loss for training deep neural networks with noisy labels,

Reference 26

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Observation 201bc24b-ce76-4a21-9bbf-61652cd22ccd · outbound

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

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Symmetric cross entropy for robust learning with noisy labels,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.607394Z

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Observation b282c5ed-6257-4b9a-89a4-c0a6b428e3ec · outbound

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

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Normalized loss functions for deep learning with noisy labels,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:51.007881Z

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Observation 73a5b8b0-8193-4ba4-9ce5-19e6b1a57c41 · outbound

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

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Mitigating memorization of noisy labels by clipping the model prediction,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.590942Z

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Observation 77e90f1b-1a71-4693-af49-bb6eee7f82b3 · outbound

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

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels When optimizing f-divergence is robust with label noise,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.575406Z

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Observation 985bfdfa-34f9-4c4d-85c9-7bfc909af5b7 · outbound

This paper cites Mitigating the Impact of Labeling Errors on Training via Rockafellian Relaxation.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Mitigating the Impact of Labeling Errors on Training via Rockafellian Relaxation

Reference 32

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Observation 6ebf05fa-4361-4151-90a7-5f1b014e641f · outbound

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

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels How does disagreement help generalization against label corruption?,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.561026Z

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Observation e922b30f-3be4-4ab5-8a74-1bc2b52e41c5 · outbound

This paper cites A general class of coefficients of divergence of one distribution from another,.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels A general class of coefficients of divergence of one distribution from another,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.545294Z

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Observation 29a3c3a7-4969-48e1-9ec4-e0571e2790e0 · outbound

This paper cites On information-type measure of difference of probability distributions and indirect observations,.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels On information-type measure of difference of probability distributions and indirect observations,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.530311Z

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Observation c2c31bc2-6e02-441f-9f87-f3ffe556fc8a · outbound

This paper cites Minimization of divergences on sets of signed measures,.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Minimization of divergences on sets of signed measures,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.514313Z

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Observation 31c229f3-7a94-455f-a085-c1e2145b8dbb · outbound

This paper cites Estimating divergence functionals and the likelihood ratio by convex risk minimization,.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Estimating divergence functionals and the likelihood ratio by convex risk minimization,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.498704Z

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Observation 591aabac-287d-4d2b-b3f3-6d1f85292d7c · outbound

This paper cites (f, Γ)-divergences: Interpolating between f-divergences and integral probability metrics,.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels (f, Γ)-divergences: Interpolating between f-divergences and integral probability metrics,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.483328Z

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Observation 9128a903-e568-4095-b0b7-7dd50ae90fac · outbound

This paper cites On divergences and informations in statistics and information theory,.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels On divergences and informations in statistics and information theory,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.467991Z

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Observation 08df948b-4379-42a7-8d5c-4f612103ee75 · outbound

This paper cites Ponstein, Approaches to the Theory of Optimization.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Ponstein, Approaches to the Theory of Optimization

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.452180Z

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Observation 1069cc57-7bce-4b85-961b-632259462679 · outbound

This paper cites An old-new concept of convex risk measures: The optimized certainty equivalent,.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels An old-new concept of convex risk measures: The optimized certainty equivalent,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.436639Z

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Observation fbbee183-559b-499c-8053-dec696a0e9ba · outbound

This paper cites Entropic value-at-risk: A new coherent risk measure,.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Entropic value-at-risk: A new coherent risk measure,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.420137Z

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Observation 6c8a4428-5748-4a02-b5e4-fa4f77e105da · outbound

This paper cites Theoretically principled trade-off between robustness and accuracy,.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Theoretically principled trade-off between robustness and accuracy,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.404168Z

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Observation 7c90df48-931e-4144-895a-4ae47d67bef4 · outbound

This paper cites Improving adversarial robustness requires revisiting misclassified examples,.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Improving adversarial robustness requires revisiting misclassified examples,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.388817Z

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Observation 252e1bd7-f3f2-44c4-b3d0-eb0958a6b38b · outbound

This paper cites A unified Wasserstein distributional robustness framework for adversarial training,.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels A unified Wasserstein distributional robustness framework for adversarial training,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.371555Z

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Observation 30fa6b75-c114-4a73-8f08-9044ea89e7b3 · outbound

This paper cites Optimal Transport Regularized Divergences: Application to Adversarial Robustness.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Optimal Transport Regularized Divergences: Application to Adversarial Robustness

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-08-05T22:45:49.793371Z

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Observation 64f02eb4-9b04-4aa5-87c9-c5b9a2abb715 · outbound

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

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Nlnl: Negative learning for noisy labels,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.350630Z

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Observation 6bddb5ea-8768-4767-8b0a-045df919cbfe · outbound

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

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Making deep neural networks robust to label noise: A loss correction approach,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.899220Z

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Observation 38796b1a-1caf-4f16-947f-7b45ebbfde63 · outbound

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

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Peer loss functions: Learning from noisy labels without knowing noise rates,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.332846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 66f35d8d-93f4-46ac-9182-c71273508708 · outbound

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

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Provably end-to-end label-noise learning without anchor points,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.313798Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 47febcf2-e3ae-44a9-9900-2033f7127c67 · outbound

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

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels To smooth or not? when label smoothing meets noisy labels,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.295259Z

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Observation 3f19fe15-ec64-4642-96f6-c3e7acacc617 · outbound

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

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Rethinking Noisy Label Learning in Real-world Annotation Scenarios from the Noise-type Perspective

Reference 52

Resolution: verified exact; local_arxiv, observed 2026-08-05T22:45:49.634224Z

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Observation eada9ec6-cb45-48cc-9e0b-0abc9cc6f35c · outbound

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

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Understanding and improving early stopping for learning with noisy labels,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.279871Z

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Observation 0cda5d92-9715-4f68-b539-325b02e7fe5d · outbound

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

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Learning with instance-dependent label noise: A sample sieve approach,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.263347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8322d324-812f-4e24-8967-624cec688b77 · outbound

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

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels A second-order approach to learning with instance-dependent label noise,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.245924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d7b112ea-e86f-4439-819b-f36af949a8b7 · outbound

This paper cites Luenberger, Optimization by Vector Space Methods.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Luenberger, Optimization by Vector Space Methods

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.230642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8f63c7fc-147c-4c97-858f-0a4f7e264491 · outbound

This paper cites Optnet: Differentiable optimization as a layer in neural networks,.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Optnet: Differentiable optimization as a layer in neural networks,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.214812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d4b19295-3680-4877-b33f-ceb07f6e5c74 · outbound

This paper cites Differentiable convex optimization layers,.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Differentiable convex optimization layers,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.199803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b699c94c-6555-4c39-83d8-ceb7eb43238a · outbound

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

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Dual t: Reducing estimation error for transition matrix in label-noise learning,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.184361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f071798b-90f4-49cc-b7cb-ec68a51ff0ea · outbound

This paper cites an unresolved cited work.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Unresolved cited work

Reference 60

Resolution: unresolved; raw_fallback, observed 2026-08-05T22:45:50.168823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b8e2caf7-81c5-44b1-b7b3-e4680f6f9b10 · outbound

This paper cites an unresolved cited work.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Unresolved cited work

Reference 61

Resolution: unresolved; raw_fallback, observed 2026-08-05T22:45:50.153671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f439574c-501f-4092-9e53-13ef50f8fe7d · outbound

This paper cites The training objective loss is defined by Lθ(x, y) := L(hθ(x), y).

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels The training objective loss is defined by Lθ(x, y) := L(hθ(x), y)

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.138747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0df46c72-09ce-40da-aaf1-68c5527ba185 · outbound

This paper cites an unresolved cited work.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-08-05T22:45:50.124131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5b1320c8-912c-48cf-8e25-db4112a8ad61 · outbound

This paper cites (23) Note that the latter condition automatically follows from the former if r <1/2.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels (23) Note that the latter condition automatically follows from the former if r <1/2

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.109668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b4c276c9-22b7-4504-8e43-b48818488689 · outbound

This paper cites We also suppose that there exists θ∗ ∈ Θ such that hθ∗ = h∗ (again, note that we identify each label with its corresponding one-hot vector).

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels We also suppose that there exists θ∗ ∈ Θ such that hθ∗ = h∗ (again, note that we identify each label with its corresponding one-hot vector)

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.093210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 089da0ca-4e8e-45be-9a58-5f1f6aea0953 · outbound

This paper cites an unresolved cited work.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Unresolved cited work

Reference 66

Resolution: unresolved; raw_fallback, observed 2026-08-05T22:45:50.077013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation feabe5f7-3e9f-44bc-a137-00224506c89f · outbound

This paper cites an unresolved cited work.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Unresolved cited work

Reference 67

Resolution: unresolved; raw_fallback, observed 2026-08-05T22:45:50.061004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 05b7f104-a64a-47f5-bdd3-13aac780cef8 · outbound

This paper cites Let δ := rf (0) + (1 − r)f (1/(1 − r)).

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Let δ := rf (0) + (1 − r)f (1/(1 − r))

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:45:50.046603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 58c31b20-9a35-45ad-b5f4-2d441f835a71 · outbound

This paper cites an unresolved cited work.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Unresolved cited work

Reference 69

Resolution: unresolved; raw_fallback, observed 2026-08-05T22:45:50.030232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b332187e-d5c7-4b6a-93f0-26772c5ce39a · outbound

This paper cites an unresolved cited work.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Unresolved cited work

Reference 70

Resolution: unresolved; raw_fallback, observed 2026-08-05T22:45:49.978069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Reference resolution

Outbound references

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