Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T18:35:19.070107Z
Paper Citation Record · LEDGER
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation
As of 15 August 2026, this Paper Citation Record lists 74 of 74 outbound references and 1 inbound Pith citation observation for arXiv:2507.07883.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-18T11:58:39.003926Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-18T12:01:21.196409Z
Reference resolution
74 of 74 outbound references displayed
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation e33062e6-89bf-4334-bf8e-c5858911a8b1 · outbound
This paper cites Sharp-maml: Sharpness-aware model-agnostic meta learning.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Sharp-maml: Sharpness-aware model-agnostic meta learning
Reference 1
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation c6f11d75-fe87-4572-9c9c-79579532270d · outbound
This paper cites Towards understanding sharpness-aware minimization.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Towards understanding sharpness-aware minimization
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:20.641545Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 27d90dd9-4970-4617-aabf-552ec5f686a9 · outbound
This paper cites Sharpness-aware minimization leads to low-rank features.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Sharpness-aware minimization leads to low-rank features
Reference 3
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation e6837323-cb7c-40b5-a353-e84fac682b7d · outbound
This paper cites Segnet: A deep convolutional encoder-decoder architecture for image segmentation.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Segnet: A deep convolutional encoder-decoder architecture for image segmentation
Reference 4
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 0e06c027-00eb-4ca1-8d5c-4a8dd90dff09 · outbound
This paper cites Sharpness-aware minimization improves language model generalization.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Sharpness-aware minimization improves language model generalization
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:20.596159Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 1d9636e4-0ba2-466b-8d4c-84caeef858a3 · outbound
This paper cites Fair resource allocation in multi-task learning.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Fair resource allocation in multi-task learning
Reference 6
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 37e5197d-86a8-4f2f-a8df-4a197e44e30f · outbound
This paper cites Automated Search for Resource-Efficient Branched Multi-Task Networks.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Automated Search for Resource-Efficient Branched Multi-Task Networks
Reference 7
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T18:35:19.515872Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 5ebf41b9-2e84-40b0-8a48-95d6bb861a35 · outbound
This paper cites Swad: Domain generalization by seeking flat minima.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Swad: Domain generalization by seeking flat minima
Reference 8
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation d7d65f22-669b-4ea7-bdfb-6f256dfb3e68 · outbound
This paper cites Multi-task learning in natural language processing: An overview.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Multi-task learning in natural language processing: An overview
Reference 9
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 2375f3da-7836-4cf5-acb5-f899629d6857 · outbound
This paper cites Gradient-Based Multi-Objective Deep Learning: Algorithms, Theories, Applications, and Beyond.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Gradient-Based Multi-Objective Deep Learning: Algorithms, Theories, Applications, and Beyond
Reference 10
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation caab80ec-0365-4d02-a166-e0289a4a1279 · outbound
This paper cites Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks
Reference 11
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 4cd14d1e-1805-4cf6-b291-b12157fa31e4 · outbound
This paper cites Just pick a sign: Optimizing deep multitask models with gra- dient sign dropout.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Just pick a sign: Optimizing deep multitask models with gra- dient sign dropout
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:20.516874Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation c28a26b6-0e87-48fc-bf83-a96d869ae05f · outbound
This paper cites Mod-squad: Designing mixtures of experts as modular multi-task learners.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Mod-squad: Designing mixtures of experts as modular multi-task learners
Reference 13
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 54967643-b106-45c4-90f2-e7599fabf4db · outbound
This paper cites Why does sharpness- aware minimization generalize better than sgd? Advances in Neural Information Processing Systems, 36:72325–72376,.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Why does sharpness- aware minimization generalize better than sgd? Advances in Neural Information Processing Systems, 36:72325–72376,
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:20.482807Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 45a5bc96-558d-4ae4-be23-3da8319d1842 · outbound
This paper cites Deep learning in video multi-object tracking: A survey.Neu- rocomputing, 381:61–88, 2020.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Deep learning in video multi-object tracking: A survey.Neu- rocomputing, 381:61–88, 2020
Reference 15
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 79a9cb08-558c-4d66-adf4-f6d5a295aaa6 · outbound
This paper cites The cityscapes dataset for semantic urban scene understanding.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation The cityscapes dataset for semantic urban scene understanding
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:20.452483Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 650d8a24-84da-4efd-9d1e-081968e39be0 · outbound
This paper cites Multi-Task Learning with Deep Neural Networks: A Survey.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Multi-Task Learning with Deep Neural Networks: A Survey
Reference 17
- Resolution
- unresolved
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Source-reported events for the cited work
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Observation e696a276-330e-4657-9560-08ac3b3de06a · outbound
This paper cites Instance-aware se- mantic segmentation via multi-task network cascades.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Instance-aware se- mantic segmentation via multi-task network cascades
Reference 18
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 62d6ad54-3a17-45a1-9f1c-9f831ce68a3e · outbound
This paper cites Imagenet: A large-scale hierarchical image database.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Imagenet: A large-scale hierarchical image database
Reference 19
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation b562fbc8-fc64-4c83-8275-4a0a905fa6a1 · outbound
This paper cites Multiple-gradient descent algorithm (mgda) for multiobjective optimization.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Multiple-gradient descent algorithm (mgda) for multiobjective optimization
Reference 20
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 4b7520ae-5d75-43d2-a536-ecb367f432b4 · outbound
This paper cites Sharp minima can generalize for deep nets.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Sharp minima can generalize for deep nets
Reference 21
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 54b9a183-c2d2-49fb-9476-b7a71dc52e35 · outbound
This paper cites Efficient sharpness-aware minimization for improved training of neu- ral networks.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Efficient sharpness-aware minimization for improved training of neu- ral networks
Reference 22
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 9016d573-bc4b-47d1-9b51-34f657734d13 · outbound
This paper cites M 3vit: Mixture-of-experts vision transformer for efficient multi- task learning with model-accelerator co-design.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation M 3vit: Mixture-of-experts vision transformer for efficient multi- task learning with model-accelerator co-design
Reference 23
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation fc124a2b-02f1-4117-acbf-4d9d66bf16f0 · outbound
This paper cites Miti- gating gradient bias in multi-objective learning: A provably convergent approach.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Miti- gating gradient bias in multi-objective learning: A provably convergent approach
Reference 24
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 92d8a557-e2b1-4258-8340-0ea6be9d102a · outbound
This paper cites Fast Graph Representation Learning with PyTorch Geometric.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Fast Graph Representation Learning with PyTorch Geometric
Reference 25
- Resolution
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Source-reported events for the cited work
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Observation bad788bc-9b5a-4a12-85d2-a045a3972b4a · outbound
This paper cites Sharpness-aware minimization for efficiently improving generalization.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Sharpness-aware minimization for efficiently improving generalization
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:20.306910Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation d161caf9-27ec-469d-bd85-a3bd93cb664f · outbound
This paper cites Nddr-cnn: Layerwise feature fusing in multi-task cnns by neural discriminative dimensionality reduction.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Nddr-cnn: Layerwise feature fusing in multi-task cnns by neural discriminative dimensionality reduction
Reference 27
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- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation c3c02ebb-0e6d-4bad-ab44-391663942e48 · outbound
This paper cites Mtl-nas: Task-agnostic neural architecture search towards general-purpose multi-task learning.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Mtl-nas: Task-agnostic neural architecture search towards general-purpose multi-task learning
Reference 28
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 5ca29ac1-0e48-4b2e-9dca-8c2cf228c215 · outbound
This paper cites Variance-reduced zeroth-order methods for fine-tuning language models.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Variance-reduced zeroth-order methods for fine-tuning language models
Reference 29
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation be862475-d417-49e9-b3f5-25e0d4846a45 · outbound
This paper cites Deep residual learning for image recognition.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Deep residual learning for image recognition
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:20.241535Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation b41ccdec-41f7-4e85-a078-c20b52b6fdca · outbound
This paper cites Simplifying neu- ral nets by discovering flat minima.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Simplifying neu- ral nets by discovering flat minima
Reference 31
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 99207ce2-f846-4331-820c-7c49eff23079 · outbound
This paper cites Flat minima.Neu- ral Computation, 9(1):1–42, 1997.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Flat minima.Neu- ral Computation, 9(1):1–42, 1997
Reference 32
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation c6f9e05c-beb0-4b2d-98de-3f375becfd61 · outbound
This paper cites Lora: Low- rank adaptation of large language models.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Lora: Low- rank adaptation of large language models
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 2026-08-15T06:32:42.880941+00:00.
Observation ac076f90-f9c2-46a3-b033-6ccf25b211e2 · outbound
This paper cites The break-even point on optimization trajectories of deep neural networks.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation The break-even point on optimization trajectories of deep neural networks
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:20.170014Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 090dd21c-6b99-46d3-b4ad-784e0fc09335 · outbound
This paper cites Rotograd: Gradient ho- mogenization in multitask learning.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Rotograd: Gradient ho- mogenization in multitask learning
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:20.154781Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 2b811bde-0735-40d0-8d86-2ab221da71da · outbound
This paper cites Fantastic generalization mea- sures and where to find them.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Fantastic generalization mea- sures and where to find them
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:20.134323Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 80a5a0a0-8380-464c-a7bf-549bb800daf1 · outbound
This paper cites Multi-task learning using uncertainty to weigh losses for scene geome- try and semantics.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Multi-task learning using uncertainty to weigh losses for scene geome- try and semantics
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:20.118530Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 53516355-5398-4633-bf7b-108a4ec3b644 · outbound
This paper cites On large- batch training for deep learning: Generalization gap and sharp minima.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation On large- batch training for deep learning: Generalization gap and sharp minima
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:20.098830Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation e700efeb-d0eb-4424-b67f-14340531b8f7 · outbound
This paper cites Visualizing the loss landscape of neural nets.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Visualizing the loss landscape of neural nets
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:20.081056Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 3693d2c7-eeb6-4745-b94d-21620156d47b · outbound
This paper cites LibMTL: A Python library for multi-task learning.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation LibMTL: A Python library for multi-task learning
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:20.065317Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation ff705fbe-3072-470c-a7b4-b83c57019013 · outbound
This paper cites Rea- sonable effectiveness of random weighting: A litmus test for multi-task learning.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Rea- sonable effectiveness of random weighting: A litmus test for multi-task learning
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:20.046046Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 1100dc17-c112-4f13-bc4b-47beb36f42ec · outbound
This paper cites Conflict-averse gradient descent for multi-task learn- ing.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Conflict-averse gradient descent for multi-task learn- ing
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:20.029007Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation a56f2d5d-df94-4525-9e7f-cf98561c8f68 · outbound
This paper cites Famo: Fast adaptive multitask optimization.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Famo: Fast adaptive multitask optimization
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:20.011451Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation ee9988c7-ec11-4a3a-a363-60de7f4ea222 · outbound
This paper cites Towards impartial multi-task learning.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Towards impartial multi-task learning
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:19.996986Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation f2bb6f0b-42f0-453c-9b31-99d6a26913ae · outbound
This paper cites End- to-end multi-task learning with attention.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation End- to-end multi-task learning with attention
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:19.982162Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 1d80aa6d-c43a-4072-9578-75fe0b120fa3 · outbound
This paper cites Towards efficient and scalable sharpness-aware minimization.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Towards efficient and scalable sharpness-aware minimization
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:19.963580Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation f74d18e6-beca-44a5-8359-50954dda0b8f · outbound
This paper cites Deep learning face attributes in the wild.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Deep learning face attributes in the wild
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:19.947869Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 0875f457-39be-41f3-9cd3-4d5e811a2493 · outbound
This paper cites Multi- task learning as a bargaining game.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Multi- task learning as a bargaining game
Reference 48
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:35:18.900900Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6273e28b-8e2c-482d-b4f9-f7286b2ba954 · outbound
This paper cites Improving multi-task learning via seeking task-based flat regions.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Improving multi-task learning via seeking task-based flat regions
Reference 49
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:35:18.907095Z
Source-reported events for the cited work
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Observation fffb0fe3-1e54-4a0d-8e8b-353ae58ddd46 · outbound
This paper cites Quantum chemistry structures and properties of 134 kilo molecules.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Quantum chemistry structures and properties of 134 kilo molecules
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:19.917661Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation f48aeb0e-1afe-4468-a63f-9dfaa077469e · outbound
This paper cites An Overview of Multi-Task Learning in Deep Neural Networks.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation An Overview of Multi-Task Learning in Deep Neural Networks
Reference 51
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:35:18.920879Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2341de5c-8240-46b5-8f0b-a3f41ebfa323 · outbound
This paper cites Latent multi-task architecture learning.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Latent multi-task architecture learning
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:19.897592Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 1b10328f-3869-4394-821f-baa7d052787c · outbound
This paper cites Multi-task learning as multi-objective optimization.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Multi-task learning as multi-objective optimization
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:19.875521Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 4c4892cd-0493-48e9-baf7-f814092cbdb1 · outbound
This paper cites Independent component alignment for multi-task learning.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Independent component alignment for multi-task learning
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:19.856347Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation a73f894d-8f7c-4070-a403-d72831679187 · outbound
This paper cites Indoor segmentation and support inference from rgbd images.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Indoor segmentation and support inference from rgbd images
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:19.839367Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation b43dfc75-c453-40de-82c9-07b4eb997b0b · outbound
This paper cites Multivariate stochastic approximation using a simultaneous perturbation gradient approximation.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Multivariate stochastic approximation using a simultaneous perturbation gradient approximation
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:19.816806Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation d6da1738-653d-4a13-b96d-8fa408a792f8 · outbound
This paper cites Sharpness-aware minimization enhances fea- ture quality via balanced learning.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Sharpness-aware minimization enhances fea- ture quality via balanced learning
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:19.799289Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation a4660136-342b-470a-93f5-f3c9f92bf6b2 · outbound
This paper cites Av-superb: A multi-task eval- uation benchmark for audio-visual representation models.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Av-superb: A multi-task eval- uation benchmark for audio-visual representation models
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:19.783153Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation b619acaf-4997-4e48-abaa-1e392c063d22 · outbound
This paper cites Visualizing data using t-sne.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Visualizing data using t-sne
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:19.765774Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation ad127c43-6c6e-4bbd-83f4-53b25996b016 · outbound
This paper cites Multi-task learning for dense prediction tasks: A survey.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Multi-task learning for dense prediction tasks: A survey
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:19.749347Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 40e3de52-6855-4f18-91e0-07393c91b1f6 · outbound
This paper cites Deep hashing network for unsupervised domain adaptation.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Deep hashing network for unsupervised domain adaptation
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:19.733063Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 196d75e2-5d57-4c15-a2c2-2cd91e3808d7 · outbound
This paper cites Sharpness-aware gradient matching for domain generaliza- tion.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Sharpness-aware gradient matching for domain generaliza- tion
Reference 62
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:35:18.996564Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 855bbd7e-be62-4b08-8509-370694e66372 · outbound
This paper cites Vi- ola: Conditional language models for speech recognition, synthesis, and translation.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Vi- ola: Conditional language models for speech recognition, synthesis, and translation
Reference 63
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:19.704628Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 5e690315-4374-46fb-a2db-fd3689911b98 · outbound
This paper cites Theoretical study of conflict-avoidant multi-objective reinforcement learning.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Theoretical study of conflict-avoidant multi-objective reinforcement learning
Reference 64
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:19.689791Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 85179ca5-f15c-4663-8bd1-eddcb4f990ab · outbound
This paper cites How sharpness- aware minimization minimizes sharpness? In The Eleventh International Conference on Learning Representa- tions, 2023.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation How sharpness- aware minimization minimizes sharpness? In The Eleventh International Conference on Learning Representa- tions, 2023
Reference 65
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:19.674026Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 1ffcb801-47b0-420b-a22a-ff839d1bfeec · outbound
This paper cites Direction-oriented multi-objective learning: Simple and provable stochastic al- gorithms.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Direction-oriented multi-objective learning: Simple and provable stochastic al- gorithms
Reference 66
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:19.658323Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation d89ce3d9-d631-4b6a-8e3f-2e7045e79f97 · outbound
This paper cites Scalable bilevel loss balancing for multi-task learning.arXiv preprint arXiv:2502.08585, 2025.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Scalable bilevel loss balancing for multi-task learning.arXiv preprint arXiv:2502.08585, 2025
Reference 67
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:35:19.026267Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bcff7de8-6c1e-48b6-89e6-0ebc19e76592 · outbound
This paper cites Hessian-based analysis of large batch training and robustness to adversaries.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Hessian-based analysis of large batch training and robustness to adversaries
Reference 68
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:19.640450Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 8ef62c0a-4e19-4369-8c3a-cd2f698647d9 · outbound
This paper cites Unleashing the Power of Multi-Task Learning: A Comprehensive Survey Spanning Traditional, Deep, and Pretrained Foundation Model Eras.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Unleashing the Power of Multi-Task Learning: A Comprehensive Survey Spanning Traditional, Deep, and Pretrained Foundation Model Eras
Reference 69
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:35:19.042575Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4e02bb5d-69a8-4d29-bbf6-a3b44b583f32 · outbound
This paper cites Gradient surgery for multi-task learning.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Gradient surgery for multi-task learning
Reference 70
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:19.622438Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation cbf28cda-b4ca-4339-aa7c-6d936407596b · outbound
This paper cites On the convergence of multi-objective optimization under general- ized smoothness.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation On the convergence of multi-objective optimization under general- ized smoothness
Reference 71
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:19.602886Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 318603a3-5651-48b5-9aa8-ded0c7cbd234 · outbound
This paper cites A survey on multi-task learning.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation A survey on multi-task learning
Reference 72
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:19.584175Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 3c9105f2-a468-440c-862a-511cc8f8a405 · outbound
This paper cites A survey of multi-task learning in natural lan- guage processing: Regarding task relatedness and training methods.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation A survey of multi-task learning in natural lan- guage processing: Regarding task relatedness and training methods
Reference 73
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:19.565538Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 7a4f92d5-ffda-4598-a2e6-9137c76ff201 · outbound
This paper cites Surrogate gap minimization improves sharpness- aware training.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation Surrogate gap minimization improves sharpness- aware training
Reference 74
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:35:19.540743Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Pith citing papers
Observation 237b4d15-59a0-4a4f-9670-19daf03d3532 · inbound
Rethinking Parameter Sharing for LLM Fine-Tuning with Multiple LoRAs cites this paper.
Rethinking Parameter Sharing for LLM Fine-Tuning with Multiple LoRAs SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation
Reference 2
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T12:01:21.199581Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.