Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning | Paper Citation Record

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This paper cites Saliency-regularized deep multi-task learning.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Saliency-regularized deep multi-task learning

Reference 1

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This paper cites Fair resource allocation in multi-task learning.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Fair resource allocation in multi-task learning

Reference 2

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Observation 8dbb8dc9-b794-4595-a539-4d20ba5c0283 · outbound

This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 3

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This paper cites Multitask learning.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Multitask learning

Reference 4

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This paper cites Encoder-decoder with atrous separable convolution for semantic image segmentation.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Encoder-decoder with atrous separable convolution for semantic image segmentation

Reference 5

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This paper cites A simple framework for contrastive learning of visual representations.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning A simple framework for contrastive learning of visual representations

Reference 6

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This paper cites Big self-supervised mod- els are strong semi-supervised learners.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Big self-supervised mod- els are strong semi-supervised learners

Reference 7

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This paper cites GradNorm: Gradient normalization for adaptive loss balancing in deep multitask networks.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning GradNorm: Gradient normalization for adaptive loss balancing in deep multitask networks

Reference 8

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This paper cites Just pick a sign: Optimizing deep multitask models with gra- dient sign dropout.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Just pick a sign: Optimizing deep multitask models with gra- dient sign dropout

Reference 9

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This paper cites MultiNet++: Multi-stream feature ag- gregation and geometric loss strategy for multi-task learning.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning MultiNet++: Multi-stream feature ag- gregation and geometric loss strategy for multi-task learning

Reference 10

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This paper cites The cityscapes dataset for semantic urban scene understanding.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning The cityscapes dataset for semantic urban scene understanding

Reference 11

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This paper cites Improvable gap bal- ancing for multi-task learning.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Improvable gap bal- ancing for multi-task learning

Reference 12

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This paper cites Multiple-gradient descent algorithm (MGDA) for multiobjective optimization.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Multiple-gradient descent algorithm (MGDA) for multiobjective optimization

Reference 13

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This paper cites Multi-task self- supervised visual learning.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Multi-task self- supervised visual learning

Reference 14

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This paper cites Miti- gating gradient bias in multi-objective learning: A provably convergent approach.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Miti- gating gradient bias in multi-objective learning: A provably convergent approach

Reference 15

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This paper cites Bootstrap your own latent-a new approach to self-supervised learning.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Bootstrap your own latent-a new approach to self-supervised learning

Reference 16

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Observation aee67976-9ec9-4695-bbd2-3468b88f9e2c · outbound

This paper cites Deep residual learning for image recognition.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Deep residual learning for image recognition

Reference 17

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This paper cites Momentum contrast for unsupervised visual rep- resentation learning.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Momentum contrast for unsupervised visual rep- resentation learning

Reference 18

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This paper cites MetaBalance: improving multi-task recommendations via adapting gradient magnitudes of aux- iliary tasks.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning MetaBalance: improving multi-task recommendations via adapting gradient magnitudes of aux- iliary tasks

Reference 19

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This paper cites Position: The platonic representation hypothesis.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Position: The platonic representation hypothesis

Reference 20

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This paper cites Rotograd: Gradient ho- mogenization in multitask learning.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Rotograd: Gradient ho- mogenization in multitask learning

Reference 21

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

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Multi-task learning using uncertainty to weigh losses for scene geome- try and semantics

Reference 22

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This paper cites Segment any- thing.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Segment any- thing

Reference 23

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This paper cites In defense of the uni- tary scalarization for deep multi-task learning.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning In defense of the uni- tary scalarization for deep multi-task learning

Reference 24

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This paper cites Unveiling the Backbone-Optimizer Coupling Bias in Visual Representation Learning.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Unveiling the Backbone-Optimizer Coupling Bias in Visual Representation Learning

Reference 25

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Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Unresolved cited work

Reference 26

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This paper cites Libmtl: A python library for deep multi-task learning.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Libmtl: A python library for deep multi-task learning

Reference 27

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

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Rea- sonable effectiveness of random weighting: A litmus test for multi-task learning

Reference 28

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

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Rea- sonable effectiveness of random weighting: A litmus test for multi-task learning

Reference 29

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This paper cites Dual-balancing for multi-task learning, 2024.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Dual-balancing for multi-task learning, 2024

Reference 30

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This paper cites Smooth tchebycheff scalarization for multi-objective optimization.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Smooth tchebycheff scalarization for multi-objective optimization

Reference 31

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This paper cites Conflict-averse gradient descent for multi-task learning.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Conflict-averse gradient descent for multi-task learning

Reference 32

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This paper cites Famo: Fast adaptive multitask optimization.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Famo: Fast adaptive multitask optimization

Reference 33

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This paper cites Towards impartial multi-task learning.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Towards impartial multi-task learning

Reference 34

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Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Unresolved cited work

Reference 35

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This paper cites Auto-lambda: Disentangling dynamic task relation- ships.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Auto-lambda: Disentangling dynamic task relation- ships

Reference 36

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This paper cites Unified-io: A unified model for vision, language, and multi-modal tasks.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Unified-io: A unified model for vision, language, and multi-modal tasks

Reference 37

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Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Unified-io 2: Scaling autoregressive multimodal models with vision language audio and action

Reference 38

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This paper cites Modeling task relationships in multi-task learning with multi-gate mixture-of-experts.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Modeling task relationships in multi-task learning with multi-gate mixture-of-experts

Reference 39

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This paper cites Entire space multi-task model: An effective approach for estimating post-click conversion rate.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Entire space multi-task model: An effective approach for estimating post-click conversion rate

Reference 40

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This paper cites Traditional and heavy tailed self regularization in neural network models.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Traditional and heavy tailed self regularization in neural network models

Reference 41

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This paper cites MTAdam: Automatic balancing of multiple training loss terms.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning MTAdam: Automatic balancing of multiple training loss terms

Reference 42

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This paper cites Robust Analysis of Multi-Task Learning Efficiency: New Benchmarks on Light-Weighed Backbones and Effective Measurement of Multi-Task Learning Challenges by Feature Disentanglement.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Robust Analysis of Multi-Task Learning Efficiency: New Benchmarks on Light-Weighed Backbones and Effective Measurement of Multi-Task Learning Challenges by Feature Disentanglement

Reference 43

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This paper cites Martin and Michael W.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Martin and Michael W

Reference 44

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This paper cites Implicit self- regularization in deep neural networks: Evidence from ran- dom matrix theory and implications for learning.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Implicit self- regularization in deep neural networks: Evidence from ran- dom matrix theory and implications for learning

Reference 45

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This paper cites Predicting trends in the quality of state-of-the-art neural net- works without access to training or testing data.Nature Com- munications, 12(1):4122, 2021.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Predicting trends in the quality of state-of-the-art neural net- works without access to training or testing data.Nature Com- munications, 12(1):4122, 2021

Reference 46

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

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Cross-stitch networks for multi-task learning

Reference 47

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This paper cites Can Optimization Trajectories Explain Multi-Task Transfer?.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Can Optimization Trajectories Explain Multi-Task Transfer?

Reference 48

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

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Multi- task learning as a bargaining game

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:06:05.622864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9d1d22f3-58e6-4eda-83be-18da664c06ec · outbound

This paper cites Language models are unsu- pervised multitask learners.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Language models are unsu- pervised multitask learners

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:06:05.574005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5c8947db-27f3-44dc-abf4-09f3d7fb45ee · outbound

This paper cites Latent multi-task architecture learning.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Latent multi-task architecture learning

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:06:05.535648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 88771dcc-f67f-4ded-81a2-37a77a7a1527 · outbound

This paper cites Adapting visual category models to new domains.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Adapting visual category models to new domains

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:06:05.495439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c5d8959f-31f3-4f7a-914f-c256f4dba19c · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Grad-cam: Visual explanations from deep networks via gradient-based localization

Reference 53

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Observation 61e86221-88e4-4513-aaf8-7a2337dc1d74 · outbound

This paper cites Multi-task learning as multi-objective optimization.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Multi-task learning as multi-objective optimization

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:06:05.400700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ea5d10d6-7cf1-40be-8132-b9971c612fad · outbound

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

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Independent component alignment for multi-task learning

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:06:05.353616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 66e3c54e-52e4-4130-bda3-19497df7d3ad · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-06T13:06:04.521076Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9957492c-597b-4d8e-8b6b-40dc31ed30ca · outbound

This paper cites GO4Align: Group Optimization for Multi-Task Alignment.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning GO4Align: Group Optimization for Multi-Task Alignment

Reference 57

Resolution: verified exact; local_arxiv, observed 2026-08-06T13:06:04.786699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9d29d92b-c17e-4e1a-b65a-71154682e705 · outbound

This paper cites Recon: Reducing Conflicting Gradients from the Root for Multi-Task Learning.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Recon: Reducing Conflicting Gradients from the Root for Multi-Task Learning

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T13:06:04.533911Z

Source-reported events for the cited work

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Observation add72438-13ab-465e-8258-be3b8f7d7b8a · outbound

This paper cites Indoor segmentation and support inference from RGBD images.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Indoor segmentation and support inference from RGBD images

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:06:05.315388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bbd4a819-4655-4b18-a747-64dd731eb8e5 · outbound

This paper cites Which tasks should be learned together in multi-task learning? In International conference on machine learning, pages 9120–9132.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Which tasks should be learned together in multi-task learning? In International conference on machine learning, pages 9120–9132

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:06:05.283789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f63a16f0-3b70-4cac-a56f-af21a82344e0 · outbound

This paper cites Progressive layered extraction (ple): A novel multi-task learning (mtl) model for personalized recommendations.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Progressive layered extraction (ple): A novel multi-task learning (mtl) model for personalized recommendations

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:06:05.258411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 082f0a24-1017-4c7f-831a-878be9ba166f · outbound

This paper cites Deep hashing network for unsupervised domain adaptation.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Deep hashing network for unsupervised domain adaptation

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:06:05.220897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 67737ff5-5b94-4a9c-998a-a23aa5517b77 · outbound

This paper cites Gradient vaccine: Investigating and improving multi-task optimization in massively multilingual models.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Gradient vaccine: Investigating and improving multi-task optimization in massively multilingual models

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:06:05.188074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d29815b6-c9bc-4bdf-a355-6950be5657f7 · outbound

This paper cites Do current multi-task optimization methods in deep learning even help? Advances in neural information processing systems, 35:13597–13609, 2022.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Do current multi-task optimization methods in deep learning even help? Advances in neural information processing systems, 35:13597–13609, 2022

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:06:05.153888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ff39cf77-5cdf-4b23-a5c7-ed3ef6692f63 · outbound

This paper cites Cross-task knowledge distil- lation in multi-task recommendation.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Cross-task knowledge distil- lation in multi-task recommendation

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:06:05.128069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 524f9f35-a96a-434b-841f-5858c8977066 · outbound

This paper cites Gradient surgery for multi-task learning.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Gradient surgery for multi-task learning

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:06:05.099716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d56f3ba5-7ca3-4dcc-ac48-d8f5d9add21d · outbound

This paper cites A survey on negative transfer.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning A survey on negative transfer

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:06:05.063704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 719da8d5-07f3-4fc8-bc30-811e4ec7cb52 · outbound

This paper cites Rep-MTL w/o CA.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Rep-MTL w/o CA

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:06:05.038689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1299cea7-3ad3-4982-9a4a-c0f9b3367ac5 · outbound

This paper cites an unresolved cited work.

Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning Unresolved cited work

Reference 2022

Resolution: parse uncertain; raw_fallback, observed 2026-08-06T13:06:05.601707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Reference resolution

Outbound references

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