MTL-UE: Learning to Learn Nothing for Multi-Task Learning | Paper Citation Record

Observation 906edc2a-172d-4a41-8aeb-a47aab0de92f · outbound

This paper cites Bayesian uncertainty for gradient aggregation in multi-task learning.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Bayesian uncertainty for gradient aggregation in multi-task learning

Reference 1

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Observation 14e085c8-c720-43eb-bb2b-a68ab6ac32a4 · outbound

This paper cites and Ji, K.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning and Ji, K

Reference 2

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Observation a620d2b3-2570-4507-9501-e0c62ce5cc49 · outbound

This paper cites D., and Tygar, J.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning D., and Tygar, J

Reference 3

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This paper cites A model of inductive bias learning.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning A model of inductive bias learning

Reference 4

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This paper cites Poisoning attacks against support vector machines.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Poisoning attacks against support vector machines

Reference 5

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This paper cites Facial biometrics training dataset leads to bipa lawsuits against amazon, alphabet and microsoft, jul 2020.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Facial biometrics training dataset leads to bipa lawsuits against amazon, alphabet and microsoft, jul 2020

Reference 6

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Observation 63f66ffb-473d-483f-85d8-05b0cf4a5cb2 · outbound

This paper cites Multitask learning: A knowledge-based source of inductive bias.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Multitask learning: A knowledge-based source of inductive bias

Reference 7

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Observation e65f5480-1fb3-4b2d-8d86-4da2edf25d52 · outbound

This paper cites One for all: A universal generator for concept unlearnability via multi-modal alignment.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning One for all: A universal generator for concept unlearnability via multi-modal alignment

Reference 8

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Observation 4d361237-510a-46e0-bd9d-2837b12f0f67 · outbound

This paper cites Multi-Task Learning in Natural Language Processing: An Overview.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Multi-Task Learning in Natural Language Processing: An Overview

Reference 9

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Observation 3a81f670-e833-4298-8e67-b8628dd06914 · outbound

This paper cites Self-ensemble protection: Training checkpoints are good data protectors.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Self-ensemble protection: Training checkpoints are good data protectors

Reference 10

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Observation b6fe6e37-6a5d-4185-86fa-dcc7dff0ba65 · outbound

This paper cites Multi-task learning for dangerous object detection in autonomous driving.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Multi-task learning for dangerous object detection in autonomous driving

Reference 11

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Observation 24d352f3-c607-4bdb-b32e-3ecc791c22a2 · outbound

This paper cites Multinet: Multi-modal multi-task learning for autonomous driving.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Multinet: Multi-modal multi-task learning for autonomous driving

Reference 12

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Observation 0e1634ea-4a08-41b3-bcc6-1614caf09191 · outbound

This paper cites Improvable gap balancing for multi-task learning.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Improvable gap balancing for multi-task learning

Reference 13

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Observation c884c52d-4d1c-4ff2-9838-7494b6db7730 · outbound

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

MTL-UE: Learning to Learn Nothing for Multi-Task Learning An image is worth 16x16 words: Transformers for image recognition at scale

Reference 14

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Observation 50ddce1d-7690-4849-97d2-a3f48e34b89c · outbound

This paper cites Learning to confuse: generating training time adversarial data with auto-encoder.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Learning to confuse: generating training time adversarial data with auto-encoder

Reference 15

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Observation 77c8b851-8e1e-4f0e-9b4e-b674a269846a · outbound

This paper cites Efficiently identifying task groupings for multi-task learning.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Efficiently identifying task groupings for multi-task learning

Reference 16

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This paper cites W., and Shi, M.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning W., and Shi, M

Reference 17

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Observation e3bc4c29-c1f2-4402-84d9-576a0a1b240f · outbound

This paper cites Adversarial examples make strong poisons.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Adversarial examples make strong poisons

Reference 18

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Observation 711eca46-8911-45ff-922a-2b41d43d4cb7 · outbound

This paper cites Robust unlearnable examples: Protecting data privacy against adversarial learning.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Robust unlearnable examples: Protecting data privacy against adversarial learning

Reference 19

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Observation 1e366748-c90e-482f-970a-5c57e19b9d0d · outbound

This paper cites Dynamic channel pruning: Feature boosting and suppression.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Dynamic channel pruning: Feature boosting and suppression

Reference 20

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Observation e47fd4aa-78bf-4a47-93e7-c9d67ff8003d · outbound

This paper cites Mora: Improving ensemble robustness evaluation with model reweighing attack.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Mora: Improving ensemble robustness evaluation with model reweighing attack

Reference 21

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Observation b00ca94f-134b-41f3-871e-ec2721f3c903 · outbound

This paper cites Dataset security for machine learning: Data poisoning, backdoor attacks, and defenses.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Dataset security for machine learning: Data poisoning, backdoor attacks, and defenses

Reference 22

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Observation e109bd10-abad-4598-83de-193bfa62fbba · outbound

This paper cites BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain

Reference 23

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Observation 3874721b-cb9a-47b4-9bc0-7e3da3b79072 · outbound

This paper cites Dynamic task prioritization for multitask learning.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Dynamic task prioritization for multitask learning

Reference 24

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Observation 227df6e5-a6ea-471f-953c-f5fdc41d1034 · outbound

This paper cites Learning to branch for multi-task learning.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Learning to branch for multi-task learning

Reference 25

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Observation b28ad85a-f33c-4cd1-84fb-fc82e144897a · outbound

This paper cites S., and Osadchy, R.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning S., and Osadchy, R

Reference 26

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Observation f9e9c1ec-4c5a-4255-b464-783c1f3c6004 · outbound

This paper cites Indiscriminate poisoning attacks on unsupervised contrastive learning.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Indiscriminate poisoning attacks on unsupervised contrastive learning

Reference 27

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This paper cites Deep residual learning for image recognition.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Deep residual learning for image recognition

Reference 28

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Observation e85f3b35-4f9a-489d-9791-ae4063a5b082 · outbound

This paper cites Lead: Exploring logit space evolution for model selection.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Lead: Exploring logit space evolution for model selection

Reference 29

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MTL-UE: Learning to Learn Nothing for Multi-Task Learning Unresolved cited work

Reference 30

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Observation ca6c34ab-6ec7-48af-b3e9-59f21d0765c5 · outbound

This paper cites M., Bailey, J., and Wang, Y.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning M., Bailey, J., and Wang, Y

Reference 31

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MTL-UE: Learning to Learn Nothing for Multi-Task Learning Unresolved cited work

Reference 32

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Observation 3c1b734e-9734-444a-875a-b0ba294443d4 · outbound

This paper cites Two Heads are Better Than One: Test-time Scaling of Multi-agent Collaborative Reasoning.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Two Heads are Better Than One: Test-time Scaling of Multi-agent Collaborative Reasoning

Reference 33

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MTL-UE: Learning to Learn Nothing for Multi-Task Learning and Joo, J

Reference 34

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This paper cites Multi-task learning using uncertainty to weigh losses for scene geometry and semantics.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Multi-task learning using uncertainty to weigh losses for scene geometry and semantics

Reference 35

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MTL-UE: Learning to Learn Nothing for Multi-Task Learning Unresolved cited work

Reference 36

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Observation cbd0dc49-6e37-437d-b95f-cb3d4416f6f3 · outbound

This paper cites Reasonable effectiveness of random weighting: A litmus test for multi-task learning.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Reasonable effectiveness of random weighting: A litmus test for multi-task learning

Reference 37

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Observation a3088efe-f9e1-4217-9b49-dfcd0c02214b · outbound

This paper cites Pareto multi-task learning.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Pareto multi-task learning

Reference 38

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This paper cites Safeguarding Medical Image Segmentation Datasets against Unauthorized Training via Contour- and Texture-Aware Perturbations.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Safeguarding Medical Image Segmentation Datasets against Unauthorized Training via Contour- and Texture-Aware Perturbations

Reference 39

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MTL-UE: Learning to Learn Nothing for Multi-Task Learning Unresolved cited work

Reference 40

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MTL-UE: Learning to Learn Nothing for Multi-Task Learning Unresolved cited work

Reference 41

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Observation 4033f4ca-10ea-4f54-8483-330098c514e6 · outbound

This paper cites Game-theoretic unlearnable example generator.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Game-theoretic unlearnable example generator

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:48.345857Z

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Observation a6c4d38f-ad13-4839-adb9-c9aeb211e070 · outbound

This paper cites Breaking free from MMI : A new frontier in rationalization by probing input utilization.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Breaking free from MMI : A new frontier in rationalization by probing input utilization

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:48.334800Z

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Observation 03f71c0b-7b48-4f56-a599-f6cb10d8f06f · outbound

This paper cites Adversarial cooperative rationalization: The risk of spurious correlations in even clean datasets.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Adversarial cooperative rationalization: The risk of spurious correlations in even clean datasets

Reference 44

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Observation 771ee326-8221-4f5d-8fdd-ead17d3cc79d · outbound

This paper cites Multimodal unlearnable examples: Protecting data against multimodal contrastive learning.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Multimodal unlearnable examples: Protecting data against multimodal contrastive learning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:48.308556Z

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Observation a1ddd532-a3af-4c96-8b1a-bcee02f8bd3d · outbound

This paper cites Deep learning face attributes in the wild.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Deep learning face attributes in the wild

Reference 46

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Observation 27b49673-7640-4a37-9df7-22689c19778f · outbound

This paper cites Image shortcut squeezing: Countering perturbative availability poisons with compression.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Image shortcut squeezing: Countering perturbative availability poisons with compression

Reference 47

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Observation 6d0f341a-ecfb-47a4-b87b-de586e09f390 · outbound

This paper cites Exploring the limits of model-targeted indiscriminate data poisoning attacks.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Exploring the limits of model-targeted indiscriminate data poisoning attacks

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:48.271013Z

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

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Towards deep learning models resistant to adversarial attacks

Reference 49

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MTL-UE: Learning to Learn Nothing for Multi-Task Learning Unresolved cited work

Reference 50

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Observation 5ae9741a-f76f-4226-9c59-0d4aead0321b · outbound

This paper cites Cross-stitch networks for multi-task learning.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Cross-stitch networks for multi-task learning

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:48.233413Z

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MTL-UE: Learning to Learn Nothing for Multi-Task Learning Unresolved cited work

Reference 52

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Observation 32a775fa-bb7b-49e9-a4cb-3e6e711b4ff2 · outbound

This paper cites Multi-task learning as a bargaining game.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Multi-task learning as a bargaining game

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:48.210156Z

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Observation 184d0b55-5bda-4386-8747-96b1525599dc · outbound

This paper cites Destruction-restoration suppresses data protection perturbations against diffusion models.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Destruction-restoration suppresses data protection perturbations against diffusion models

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:48.197253Z

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Observation 9145cefa-e988-430b-a43d-e2bb65974845 · outbound

This paper cites Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks

Reference 55

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This paper cites Apbench: A unified availability poisoning attack and defenses benchmark.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Apbench: A unified availability poisoning attack and defenses benchmark

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:48.185670Z

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Observation fcc0882c-45fb-4571-8a36-afcc689c8d69 · outbound

This paper cites Scalarization for multi-task and multi-domain learning at scale.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Scalarization for multi-task and multi-domain learning at scale

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:48.173916Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 56e075ab-4cbc-4499-b9a5-d405a5d4c6e4 · outbound

This paper cites An Overview of Multi-Task Learning in Deep Neural Networks.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning An Overview of Multi-Task Learning in Deep Neural Networks

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-15T23:13:47.420765Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 700ee653-da2f-490b-a997-b1b0d4977dea · outbound

This paper cites Autoregressive perturbations for data poisoning.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Autoregressive perturbations for data poisoning

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:48.162733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cda1c0ff-83df-45ad-84dd-725857fa612e · outbound

This paper cites P., and Goldstein, T.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning P., and Goldstein, T

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:48.150765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e418a39e-7e62-45d3-ade6-eac5e1c2887d · outbound

This paper cites and Koltun, V.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning and Koltun, V

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:48.139156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 42c3bbd8-f33f-4f0f-b4dc-de81ca2173d4 · outbound

This paper cites Independent component alignment for multi-task learning.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Independent component alignment for multi-task learning

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:48.127897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 26f03b6b-09c8-4617-983d-072f36001e28 · outbound

This paper cites and Zisserman, A.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning and Zisserman, A

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:48.115937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2c8718db-73b8-48b4-9634-69e56c0c6624 · outbound

This paper cites Which tasks should be learned together in multi-task learning? In Proc.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Which tasks should be learned together in multi-task learning? In Proc

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:48.103947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 03911c19-11d7-45b2-94ce-be4e47cf20f4 · outbound

This paper cites UnSeg: One Universal Unlearnable Example Generator is Enough against All Image Segmentation.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning UnSeg: One Universal Unlearnable Example Generator is Enough against All Image Segmentation

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-15T23:13:47.449225Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5fcbdee7-9639-481b-8329-677e5a8ec2d9 · outbound

This paper cites Google accused of inappropriate access to medical data in potential class-action lawsuit, jun 2019.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Google accused of inappropriate access to medical data in potential class-action lawsuit, jun 2019

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:48.091092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7e59a513-bcaf-4ba3-8837-3a3680ecd959 · outbound

This paper cites Benchmarking adversarial robustness of image shadow removal with shadow-adaptive attacks.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Benchmarking adversarial robustness of image shadow removal with shadow-adaptive attacks

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:48.079000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 77dd6d80-f3f5-47a0-9c3d-fefade406165 · outbound

This paper cites an unresolved cited work.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Unresolved cited work

Reference 68

Resolution: unresolved; raw_fallback, observed 2026-08-15T23:13:48.066383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5d12a5b4-b65a-4fd9-b2fd-bff26ca01331 · outbound

This paper cites Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-15T23:13:47.465955Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ec219d8-7d53-4d42-86de-6968d87e5402 · outbound

This paper cites A3: Few-shot prompt learning of unlearnable examples with cross-modal adversarial feature alignment.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning A3: Few-shot prompt learning of unlearnable examples with cross-modal adversarial feature alignment

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:48.051802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 05620809-b67d-4be2-b8b6-1ed9016aedc4 · outbound

This paper cites Lie Detector: Unified Backdoor Detection via Cross-Examination Framework.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Lie Detector: Unified Backdoor Detection via Cross-Examination Framework

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-15T23:13:47.473794Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b9dbe64-a740-4a4a-98c9-1f4b225307b8 · outbound

This paper cites One-pixel shortcut: On the learning preference of deep neural networks.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning One-pixel shortcut: On the learning preference of deep neural networks

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:48.040347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5ae105ad-fe37-4e12-8281-10027232a8f2 · outbound

This paper cites Transferable adversarial attacks on sam and its downstream models.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Transferable adversarial attacks on sam and its downstream models

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:48.028018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6e01d0f9-be3f-4a5b-99b8-82b85c08e7f5 · outbound

This paper cites Mitigating the curse of dimensionality for certified robustness via dual randomized smoothing.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Mitigating the curse of dimensionality for certified robustness via dual randomized smoothing

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:48.014864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8d1c173a-6132-498b-9f3b-12e6d50f06ca · outbound

This paper cites Theoretical Insights in Model Inversion Robustness and Conditional Entropy Maximization for Collaborative Inference Systems.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Theoretical Insights in Model Inversion Robustness and Conditional Entropy Maximization for Collaborative Inference Systems

Reference 75

Resolution: verified exact; local_arxiv, observed 2026-08-15T23:13:47.655394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 43df5350-ca80-4ce8-a257-7213d7e2d826 · outbound

This paper cites Is feature selection secure against training data poisoning? In Proc.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Is feature selection secure against training data poisoning? In Proc

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:48.002766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1bf7db17-ebfa-434f-a940-ceea8f11db23 · outbound

This paper cites Coding for Intelligence from the Perspective of Category.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Coding for Intelligence from the Perspective of Category

Reference 77

Resolution: verified exact; local_arxiv, observed 2026-08-15T23:13:47.638777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1b4249b8-6d2e-4e01-87b5-a3cc1c1399f6 · outbound

This paper cites Availability attacks create shortcuts.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Availability attacks create shortcuts

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:47.990836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 94594bee-e78a-4417-9223-75fd82ed3454 · outbound

This paper cites Gradient surgery for multi-task learning.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Gradient surgery for multi-task learning

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:47.978007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7e065b3e-0c73-4e4e-be6e-78be7c457016 · outbound

This paper cites Lafeat: Piercing through adversarial defenses with latent features.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Lafeat: Piercing through adversarial defenses with latent features

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:47.966142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 92be5b92-419c-483c-9ca4-6858b2e6ef9d · outbound

This paper cites an unresolved cited work.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Unresolved cited work

Reference 81

Resolution: unresolved; raw_fallback, observed 2026-08-15T23:13:47.953318Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c6081058-3113-4826-87e4-7f75d9859907 · outbound

This paper cites Lafit: Efficient and reliable evaluation of adversarial defenses with latent features.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Lafit: Efficient and reliable evaluation of adversarial defenses with latent features

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:47.941506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 04fea6b3-cd66-4b99-b36a-f9fa8cb851c1 · outbound

This paper cites an unresolved cited work.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Unresolved cited work

Reference 83

Resolution: unresolved; raw_fallback, observed 2026-08-15T23:13:47.929087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 31be5133-30f0-436b-b724-954f2a6fec1e · outbound

This paper cites an unresolved cited work.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Unresolved cited work

Reference 84

Resolution: unresolved; raw_fallback, observed 2026-08-15T23:13:47.918227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3bfb5e09-931e-4ca7-98cc-5f42acdf2129 · outbound

This paper cites an unresolved cited work.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Unresolved cited work

Reference 85

Resolution: unresolved; raw_fallback, observed 2026-08-15T23:13:47.904943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fe5a5467-80ac-4154-865b-ffadccf9cb2c · outbound

This paper cites Unlearnable examples detection via iterative filtering.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Unlearnable examples detection via iterative filtering

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:47.890534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4ebdd889-6dd1-4c49-aa69-1f232eda6111 · outbound

This paper cites Backdoor attacks against no-reference image quality assessment models via a scalable trigger.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Backdoor attacks against no-reference image quality assessment models via a scalable trigger

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:47.878004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation abd921a2-dd44-4ef3-ba1c-26e5a8bb59ec · outbound

This paper cites and Wu, S.-H.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning and Wu, S.-H

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:47.864514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f32e8859-8825-4e08-b440-61bda4c9e151 · outbound

This paper cites and Cho, H.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning and Cho, H

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:47.851552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 954cb499-163b-4285-95af-1106eb5d08c2 · outbound

This paper cites Unlearnable clusters: Towards label-agnostic unlearnable examples.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Unlearnable clusters: Towards label-agnostic unlearnable examples

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:47.839762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5dff599f-423a-4380-8e16-603d13922714 · outbound

This paper cites and Yang, Q.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning and Yang, Q

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-15T23:13:47.551726Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6cb0418-2724-48c2-a456-a191efe41520 · outbound

This paper cites Age progression/regression by conditional adversarial autoencoder.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Age progression/regression by conditional adversarial autoencoder

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:47.820214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 32152f38-1b42-4e14-be27-2d825d90fa69 · outbound

This paper cites and Lao, Y.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning and Lao, Y

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:47.808195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 93c74da6-f18e-49ba-829c-9df00114de99 · outbound

This paper cites Towards physical world backdoor attacks against skeleton action recognition.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Towards physical world backdoor attacks against skeleton action recognition

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:47.795392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3bceb56f-d12b-4152-a74c-1f9209803377 · outbound

This paper cites Toward Availability Attacks in 3D Point Clouds.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Toward Availability Attacks in 3D Point Clouds

Reference 95

Resolution: verified exact; local_arxiv, observed 2026-08-15T23:13:47.620782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 545fa827-6fdc-4ce8-af42-d809e03778cd · outbound

This paper cites Detection and defense of unlearnable examples.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning Detection and defense of unlearnable examples

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:47.782378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 12dfc59a-3625-486b-95b9-37cdf4364585 · outbound

This paper cites write newline.

MTL-UE: Learning to Learn Nothing for Multi-Task Learning write newline

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-15T23:13:47.577731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Pith citing papers