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Multi-Task Learning

Multi-task learning aims to learn multiple different tasks simultaneously while maximizing performance on one or all of the tasks.

( Image credit: Cross-stitch Networks for Multi-task Learning )

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 3241–3250 of 3687 papers

Title	Date	Tasks	Status	Hype
iACOS: Advancing Implicit Sentiment Extraction with Informative and Adaptive Negative Examples	Nov 7, 2023	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available	0
Eliciting and Understanding Cross-Task Skills with Task-Level Mixture-of-Experts	May 25, 2022	Mixture-of-ExpertsMulti-Task Learning	CodeCode Available	0
Hyperparameter Auto-tuning in Self-Supervised Robotic Learning	Oct 16, 2020	DiversityMulti-Task Learning	CodeCode Available	0
MaChAmp at SemEval-2022 Tasks 2, 3, 4, 6, 10, 11, and 12: Multi-task Multi-lingual Learning for a Pre-selected Set of Semantic Datasets	Jul 1, 2022	Multi-Task LearningSentence	CodeCode Available	0
Named Entity Recognition via Machine Reading Comprehension: A Multi-Task Learning Approach	Sep 20, 2023	Machine Reading ComprehensionMulti-Task Learning	CodeCode Available	0
Supervised Learning with Evolving Tasks and Performance Guarantees	Jan 9, 2025	Continual LearningMulti-Task Learning	CodeCode Available	0
Machine learning for structure-guided materials and process design	Dec 22, 2023	Multi-Task Learning	CodeCode Available	0
Efficient Relation-aware Neighborhood Aggregation in Graph Neural Networks via Tensor Decomposition	Dec 11, 2022	Contrastive LearningGeneral Knowledge	CodeCode Available	0
Contextual Classification Using Self-Supervised Auxiliary Models for Deep Neural Networks	Jan 7, 2021	General ClassificationImage Classification	CodeCode Available	0
Navigating the Landscape of Large Language Models: A Comprehensive Review and Analysis of Paradigms and Fine-Tuning Strategies	Apr 13, 2024	Few-Shot LearningKnowledge Distillation	CodeCode Available	0

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All datasets QM9 Cityscapes test NYUv2 OMNIGLOT CelebA UTKFace wireframe dataset

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	PCGrad	∆m%	125.7	—	Unverified
2	CAGrad	∆m%	112.8	—	Unverified
3	IMTL-G	∆m%	77.2	—	Unverified
4	Nash-MTL	∆m%	62	—	Unverified
5	BayesAgg-MTL	∆m%	53.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SwinMTL	mIoU	76.41	—	Unverified
2	Nash-MTL	mIoU	75.41	—	Unverified
3	MultiObjectiveOptimization	mIoU	66.63	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SwinMTL	Mean IoU	58.14	—	Unverified
2	Nash-MTL	Mean IoU	40.13	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Gumbel-Matrix Routing	Average Accuracy	93.52	—	Unverified
2	Mixture-of-Experts	Average Accuracy	92.19	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MGDA-UB	Error	8.25	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BayesAgg-MTL	delta_m	-2.23	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LETR	FH	83.3	—	Unverified