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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 651–660 of 3687 papers

Title	Date	Tasks	Status	Hype	Score
High-order Joint Constituency and Dependency Parsing	Sep 21, 2023	Dependency ParsingMulti-Task Learning	CodeCode Available	0	5
Is POS Tagging Necessary or Even Helpful for Neural Dependency Parsing?	Mar 6, 2020	Dependency ParsingFeature Engineering	CodeCode Available	0	5
Intrinsically Motivated Open-Ended Multi-Task Learning Using Transfer Learning to Discover Task Hierarchy	Feb 19, 2021	Active LearningHierarchical Reinforcement Learning	CodeCode Available	0	5
It Takes Two Flints to Make a Fire: Multitask Learning of Neural Relation and Explanation Classifiers	Apr 25, 2022	Multi-Task LearningRelation	CodeCode Available	0	5
AXNet: ApproXimate computing using an end-to-end trainable neural network	Jul 27, 2018	Multi-Task LearningPhilosophy	CodeCode Available	0	5
An Approach for Process Model Extraction By Multi-Grained Text Classification	May 16, 2019	General ClassificationManagement	CodeCode Available	0	5
Interpetable Target-Feature Aggregation for Multi-Task Learning based on Bias-Variance Analysis	Jun 12, 2024	ClusteringDimensionality Reduction	CodeCode Available	0	5
Instruction Tuned Models are Quick Learners	May 17, 2023	In-Context LearningMulti-Task Learning	CodeCode Available	0	5
Integrating Form and Meaning: A Multi-Task Learning Model for Acoustic Word Embeddings	Sep 14, 2022	FormMulti-Task Learning	CodeCode Available	0	5
Auxiliary Tasks in Multi-task Learning	May 16, 2018	Depth EstimationMulti-Task Learning	CodeCode Available	0	5

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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