SOTAVerified|Agents Browse Leaderboard About

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 1231–1240 of 3687 papers

Title	Date	Tasks	Status	Hype
Bayesian Meta-Learning for Improving Generalizability of Health Prediction Models With Similar Causal Mechanisms	Oct 19, 2023	Meta-LearningMulti-Task Learning	CodeCode Available	0
Learning quantum properties from short-range correlations using multi-task networks	Oct 18, 2023	Multi-Task Learning	—Unverified	0
Prototype-based HyperAdapter for Sample-Efficient Multi-task Tuning	Oct 18, 2023	Multi-Task Learningparameter-efficient fine-tuning	CodeCode Available	0
DCRNN: A Deep Cross approach based on RNN for Partial Parameter Sharing in Multi-task Learning	Oct 18, 2023	Multi-Task LearningRecommendation Systems	—Unverified	0
Experimental Results of Underwater Sound Speed Profile Inversion by Few-shot Multi-task Learning	Oct 18, 2023	Compressive SensingFew-Shot Learning	—Unverified	0
Interpreting and Exploiting Functional Specialization in Multi-Head Attention under Multi-task Learning	Oct 16, 2023	Multi-Task LearningTransfer Learning	CodeCode Available	0
Adaptive Neural Ranking Framework: Toward Maximized Business Goal for Cascade Ranking Systems	Oct 16, 2023	Learning-To-RankMulti-Task Learning	—Unverified	0
Learning visual-based deformable object rearrangement with local graph neural networks	Oct 16, 2023	Graph Neural NetworkMulti-Task Learning	CodeCode Available	0
New Advances in Body Composition Assessment with ShapedNet: A Single Image Deep Regression Approach	Oct 15, 2023	Multi-Task Learning	—Unverified	0
Federated Multi-Objective Learning	Oct 15, 2023	Federated LearningMulti-Task Learning	—Unverified	0

Show:10 25 50

← PrevPage 124 of 369Next →

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