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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 91–100 of 3687 papers

Title	Date	Tasks	Status	Hype	Score
A Comprehensive Evaluation of Multi-task Learning and Multi-task Pre-training on EHR Time-series Data	Jul 20, 2020	Few-Shot LearningMulti-Task Learning	CodeCode Available	1	5
Compressed Context Memory For Online Language Model Interaction	Dec 6, 2023	Language ModelingLanguage Modelling	CodeCode Available	1	5
An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models	Jul 14, 2020	DiversityMulti-Task Learning	CodeCode Available	1	5
An End-to-End Multi-Task Learning Model for Image-based Table Recognition	Mar 15, 2023	Cell DetectionDecoder	CodeCode Available	1	5
An End-to-End Progressive Multi-Task Learning Framework for Medical Named Entity Recognition and Normalization	Aug 1, 2021	Knowledge GraphsMedical Named Entity Recognition	CodeCode Available	1	5
Compressed Interaction Graph based Framework for Multi-behavior Recommendation	Mar 4, 2023	Multi-Task Learning	CodeCode Available	1	5
An Empirical Study of Multi-Task Learning on BERT for Biomedical Text Mining	May 6, 2020	Multi-Task Learningnamed-entity-recognition	CodeCode Available	1	5
An Efficient Framework for Clustered Federated Learning	Jun 7, 2020	ClusteringFederated Learning	CodeCode Available	1	5
CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation	Sep 2, 2021	Clone DetectionCode Generation	CodeCode Available	1	5
CodeTrans: Towards Cracking the Language of Silicon's Code Through Self-Supervised Deep Learning and High Performance Computing	Apr 6, 2021	API Sequence RecommendationCode Comment Generation	CodeCode Available	1	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