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Paper Citation Record · LEDGER

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics

As of 6 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2508.13979.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2508.13979 v1

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T18:54:03.743164Z

measured 52 of 52 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

52 of 52 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f900f92d-2171-4a92-8687-f3522a18f5b2 · outbound

This paper cites Bayesian uncertainty for gradient aggre- gation in multi-task learning, 2024.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Bayesian uncertainty for gradient aggre- gation in multi-task learning, 2024

Reference 1

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Observation c80858c0-6259-4e95-8534-843306be7e33 · outbound

This paper cites Fair Resource Allocation in Multi-Task Learning.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Fair Resource Allocation in Multi-Task Learning

Reference 2

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Observation 5580ad34-2415-4bff-b480-ed23eac922b7 · outbound

This paper cites nuscenes: A multi- modal dataset for autonomous driving.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics nuscenes: A multi- modal dataset for autonomous driving

Reference 3

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Observation 5a25ffe7-e749-420e-bca4-46d3e4270f18 · outbound

This paper cites Three-way trade-off in multi-objective learning: Op- timization, generalization and conflict-avoidance.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Three-way trade-off in multi-objective learning: Op- timization, generalization and conflict-avoidance

Reference 4

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Observation 6d1af4aa-3bcb-4f2b-a597-c87c96fbfff8 · outbound

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

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks

Reference 5

Resolution
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Observation 936f95cc-1759-48dd-8e01-33c92727111a · outbound

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

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Just pick a sign: Optimizing deep multitask models with gra- dient sign dropout

Reference 6

Resolution
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Observation 9c026344-7faa-4ef3-99f8-1c611c329b55 · outbound

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

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Multinet++: Multi-stream feature ag- gregation and geometric loss strategy for multi-task learning

Reference 7

Resolution
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Observation b385cc20-c187-4f7b-b23b-4c56eea9b880 · outbound

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

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics The cityscapes dataset for semantic urban scene understanding

Reference 8

Resolution
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Observation 3d4234b5-ac6f-416b-b5bd-9191eae2e4d7 · outbound

This paper cites Instance-aware se- mantic segmentation via multi-task network cascades.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Instance-aware se- mantic segmentation via multi-task network cascades

Reference 9

Resolution
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Observation 4754b42c-6e53-4417-9501-77c323b8f579 · outbound

This paper cites K ¨ohler, and Lukas Schott.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics K ¨ohler, and Lukas Schott

Reference 10

Resolution
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Observation c9613478-39b9-475c-95a8-e1a7ad5c2104 · outbound

This paper cites Mitigating gradi- ent bias in multi-objective learning: A provably convergent approach.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Mitigating gradi- ent bias in multi-objective learning: A provably convergent approach

Reference 11

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Observation db37671b-7a8e-401c-9878-0b11023f5be4 · outbound

This paper cites Dynamic task prioritization for multitask learning.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Dynamic task prioritization for multitask learning

Reference 12

Resolution
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Observation ad8a90bf-6c79-49f7-8eb0-07dafad3e3af · outbound

This paper cites Robust Multi-Task Learning with Excess Risks.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Robust Multi-Task Learning with Excess Risks

Reference 13

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Source-reported events for the cited work

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Observation 0e4b2c3b-e995-47c6-b057-10e8daf7b59e · outbound

This paper cites Revisiting scalarization in multi-task learning: A theoretical perspective.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Revisiting scalarization in multi-task learning: A theoretical perspective

Reference 14

Resolution
verified fuzzy
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Source-reported events for the cited work

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Observation 80f618b6-30a9-4fdd-ae22-0f60b343a156 · outbound

This paper cites Fuller: Unified multi-modality multi-task 3d perception via multi-level gradient calibration.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Fuller: Unified multi-modality multi-task 3d perception via multi-level gradient calibration

Reference 15

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Observation 66c28cd5-3e3c-4e90-ab00-c46c9796cb2b · outbound

This paper cites Online knowledge distillation for multi-task learning.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Online knowledge distillation for multi-task learning

Reference 16

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Observation d0decbc1-dc0b-44c3-9cc9-d8861638b9bf · outbound

This paper cites Selective task group updates for multi-task optimization, 2025.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Selective task group updates for multi-task optimization, 2025

Reference 17

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Observation c29256cc-0a6c-4247-a54d-d8376c1c7d45 · outbound

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

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Multi-task learning using uncertainty to weigh losses for scene geome- try and semantics

Reference 18

Resolution
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Observation f578907a-b8e0-472a-8e5f-378ed91359e4 · outbound

This paper cites A software package for sequential quadratic programming.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics A software package for sequential quadratic programming

Reference 19

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Observation ec8bc640-9b8e-4bec-8ec8-d13bbf3732c5 · outbound

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

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics In defense of the uni- tary scalarization for deep multi-task learning

Reference 20

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Observation a60f9866-34c4-4b43-9b4e-10010f62859d · outbound

This paper cites Deep asymmetric multi-task feature learning.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Deep asymmetric multi-task feature learning

Reference 21

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Observation cdd831d0-f166-4805-a240-6d54f924f8d0 · outbound

This paper cites Reasonable Effectiveness of Random Weighting: A Litmus Test for Multi-Task Learning.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Reasonable Effectiveness of Random Weighting: A Litmus Test for Multi-Task Learning

Reference 22

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Observation b7382b9c-4778-451a-a11c-a6c120bcddbc · outbound

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AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Unresolved cited work

Reference 23

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Observation e2e67e37-4968-49cf-a42d-69823d829780 · outbound

This paper cites Smooth Tchebycheff Scalarization for Multi-Objective Optimization.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Smooth Tchebycheff Scalarization for Multi-Objective Optimization

Reference 24

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Observation fa1e09da-1c74-4178-9d95-a58f98855dee · outbound

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

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Conflict-averse gradient descent for multi-task learn- ing

Reference 25

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Source-reported events for the cited work

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Observation 5286a524-30a4-49f9-aac2-8a8c2d7bd2de · outbound

This paper cites Famo: Fast adaptive multitask optimization.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Famo: Fast adaptive multitask optimization

Reference 26

Resolution
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Source-reported events for the cited work

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Observation f6bb418a-fd81-405d-bbb4-4c38b2096801 · outbound

This paper cites Towards impartial multi-task learning.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Towards impartial multi-task learning

Reference 27

Resolution
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Observation 46cea83d-32dd-4ae5-8b4d-b5ffeb209b9f · outbound

This paper cites Online mirror descent for tchebycheff scalarization in multi-objective optimization.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Online mirror descent for tchebycheff scalarization in multi-objective optimization

Reference 28

Resolution
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Source-reported events for the cited work

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Observation fccbec69-fd97-49f3-b69a-0b23b39eb7df · outbound

This paper cites End- to-end multi-task learning with attention.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics End- to-end multi-task learning with attention

Reference 29

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation 8358954b-8c45-4d34-9855-994c3f4c0d88 · outbound

This paper cites End- to-end multi-task learning with attention.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics End- to-end multi-task learning with attention

Reference 30

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation 72128008-cd30-4983-be96-67fa888c84e1 · outbound

This paper cites Auto-Lambda: Disentangling Dynamic Task Relationships.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Auto-Lambda: Disentangling Dynamic Task Relationships

Reference 31

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation aa3d4d8b-d91c-4b59-a99a-3b6feb507aa4 · outbound

This paper cites Learning multiple tasks with multilinear relationship net- works.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Learning multiple tasks with multilinear relationship net- works

Reference 32

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation 16ff64fc-7802-4741-9454-8517eaf11994 · outbound

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

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Cross-stitch networks for multi-task learning

Reference 33

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation 87b76ee2-2401-4d95-b3d9-4bdc3e77cafe · outbound

This paper cites Multi-Task Learning as a Bargaining Game.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Multi-Task Learning as a Bargaining Game

Reference 34

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Source-reported events for the cited work

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Observation df9e1297-8ff3-4a71-b927-e70c0a455e16 · outbound

This paper cites Jacobian Descent for Multi-Objective Optimization.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Jacobian Descent for Multi-Objective Optimization

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-05T18:54:01.575701Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:54:01.575701Z digest=sha256:8fcb356746a3adbf0000bb6fe40093e19c41b34ab5512386e6e1a863a6f25dfb

Observation 655399a1-4a7d-42ab-a582-ee8f0068f7e9 · outbound

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

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Scalarization for multi-task and multi- domain learning at scale

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:54:08.158506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-05T18:54:01.693598Z digest=sha256:e1e6e37981d3e176bca21b7a74d8e0fb535e1fdfcf4ac1f0971955ee0d8e8489

Observation 5d3f4278-b225-4324-9e20-bfb7ece10e03 · outbound

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

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Multi-task learning as multi-objective optimization

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:54:07.925430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-05T18:54:01.823347Z digest=sha256:c1cb4717b5f240e376efaec2e0ab04698d1f9fc3c14cab09e041f835fd3dd06b

Observation b99fee47-6ae6-43ee-9c27-4ba1e011d513 · outbound

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

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Independent component alignment for multi-task learning

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:54:07.693237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-05T18:54:01.965350Z digest=sha256:c84c9bd0f4523a7c2f117bacccf473db22095e22f961f6adcfbfa3fbcb9231b1

Observation e070d9e3-16b1-4595-9fbb-3a6d24557ac4 · outbound

This paper cites Go4align: Group optimization for multi-task alignment, 2024.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Go4align: Group optimization for multi-task alignment, 2024

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:54:07.472517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-05T18:54:02.133785Z digest=sha256:239f77fa1738e7ff756a3812931768f498cfb4d932e9a5df44cf9e6ff2bcaa08

Observation bd225054-f212-4211-b3bb-445f4b9e35f8 · outbound

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

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Indoor segmentation and support inference from rgbd images

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:54:07.222167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-05T18:54:02.267689Z digest=sha256:f187f4a54711b661875e995c3c9f18217f8fbdf92d7918b9299ce7e164b5644b

Observation f6012375-67d5-49a2-adf1-a146879d45a4 · outbound

This paper cites Which tasks should be learned together in multi-task learning? In Proceedings of the 37th International Conference on Machine Learning , pages 9120–9132.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Which tasks should be learned together in multi-task learning? In Proceedings of the 37th International Conference on Machine Learning , pages 9120–9132

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:54:06.916766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-05T18:54:02.403655Z digest=sha256:8dee90f59ad6e97d0e731038e8c005af2633f2073b1ea798fd99ecf33f4f83f2

Observation e8127f29-82b1-4ffb-84db-6d8a080f93a4 · outbound

This paper cites Regularizing Deep Multi-Task Networks using Orthogonal Gradients.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Regularizing Deep Multi-Task Networks using Orthogonal Gradients

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-05T18:54:02.505837Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:54:02.505837Z digest=sha256:8767b2aa1cf9a6a39ace95747a79e91f118b7391e16c53ed32177c696aefd9ca

Observation 6e789953-ec84-41d4-935c-43b1a495be40 · outbound

This paper cites Discovering struc- ture in multiple learning tasks: The tc algorithm.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Discovering struc- ture in multiple learning tasks: The tc algorithm

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:54:06.638264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-05T18:54:02.678971Z digest=sha256:f7ece2340d64502e11659ef49234d12c9c526e48adb45c29f006d32174633283

Observation 911fe922-bf2c-447a-90df-3fe3b5738df7 · outbound

This paper cites Unitr: A unified and efficient multi-modal transformer for bird’s-eye-view repre- sentation.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Unitr: A unified and efficient multi-modal transformer for bird’s-eye-view repre- sentation

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:54:06.411707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-05T18:54:02.793341Z digest=sha256:5ff47abc1680e59f5e6c759153cd8a8e210fd115b964cc0b3bdbf709575c2872

Observation 56fc51f5-7d68-4dd6-aaa0-2da2b0b96f0e · outbound

This paper cites Direction-oriented multi-objective learning: Simple and provable stochastic al- gorithms.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Direction-oriented multi-objective learning: Simple and provable stochastic al- gorithms

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:54:06.114971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-05T18:54:02.916082Z digest=sha256:14b2b7d11bf1e84021df118e1f743a9952176fdacfce9daa359ea91833cd88d8

Observation c952b3b3-16a3-43f1-9ced-5175caa056fa · 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.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Do current multi-task optimization methods in deep learning even help? Advances in neural information processing systems, 35:13597–13609, 2022

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:54:05.798385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-05T18:54:03.043084Z digest=sha256:b9b0c457b7f8ca1f0d997f2e12edb41b9c4c2341240d43385b6b613bc69b4863

Observation eba0eb3a-df0a-4c14-8ac8-2db0e5fcd3cc · outbound

This paper cites Multi-objective meta learning.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Multi-objective meta learning

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:54:05.603417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-05T18:54:03.168537Z digest=sha256:d87346b0f041cb4df06a6a569a27361defc8fcdb5e9812464c0b4c00bd700f8c

Observation ca608c0f-98cd-4bcd-a6c5-e92d786a2bc7 · outbound

This paper cites Taskprompter: Spatial-channel multi-task prompting for dense scene understanding.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Taskprompter: Spatial-channel multi-task prompting for dense scene understanding

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:54:05.307592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-05T18:54:03.251387Z digest=sha256:77b44dd6aa1e1110b306e1909cf3a82c4ed4ea5b17862f31ba6d3a2e8a6200e7

Observation 46b0ac1a-85c5-49be-8f38-7d1e896632e1 · outbound

This paper cites Gradient surgery for multi-task learning.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Gradient surgery for multi-task learning

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:54:05.114923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-05T18:54:03.372500Z digest=sha256:6aa71b8ef7d3195138efa29ffd6470923826dc26a80c42cc2cad0cc1cd4c7c27

Observation 0b2f59c7-487a-49b7-bc8c-751afc8eff94 · outbound

This paper cites Achievement-based training progress balancing for multi-task learning.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Achievement-based training progress balancing for multi-task learning

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:54:04.938839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-05T18:54:03.502365Z digest=sha256:5f9c2c950474650199f77407cd0b75760073cfb715197e23f43cba0ce3812139

Observation 7813c054-7877-472e-84c4-387e03ed7bd6 · outbound

This paper cites Taskonomy: Disentangling task transfer learning.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Taskonomy: Disentangling task transfer learning

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:54:04.755841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-05T18:54:03.620815Z digest=sha256:8dac18d1e82e937561ea3f0e6ccb75652729fc4aa6cbadd71e2f5362699760db

Observation e7e0d97f-5e46-4c6a-b33a-39748061cb3a · outbound

This paper cites Pyramid scene parsing network.

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics Pyramid scene parsing network

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:54:04.509909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-05T18:54:03.743164Z digest=sha256:84a2a9740b81b3ab32eba2fbee6c1205cf7ca07be742c83c01ccabcee1781584

Pith citing papers

No inbound Pith citation observations are available.