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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 2271–2280 of 3687 papers

Title	Date	Tasks	Status	Hype
Using Eye-tracking Data to Predict the Readability of Brazilian Portuguese Sentences in Single-task, Multi-task and Sequential Transfer Learning Approaches	Dec 1, 2020	Multi-Task LearningSentence	—Unverified	0
Multi-task Learning of Spoken Language Understanding by Integrating N-Best Hypotheses with Hierarchical Attention	Dec 1, 2020	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
Multitask Learning-Based Neural Bridging Reference Resolution	Dec 1, 2020	coreference-resolutionCoreference Resolution	CodeCode Available	0
Knowledge Graph Enhanced Neural Machine Translation via Multi-task Learning on Sub-entity Granularity	Dec 1, 2020	Machine TranslationMulti-Task Learning	—Unverified	0
Knowledge Aware Emotion Recognition in Textual Conversations via Multi-Task Incremental Transformer	Dec 1, 2020	Emotion RecognitionGraph Attention	—Unverified	0
A Large-Scale Corpus of E-mail Conversations with Standard and Two-Level Dialogue Act Annotations	Dec 1, 2020	Multi-Task Learning	—Unverified	0
Multi-Task Learning for Knowledge Graph Completion with Pre-trained Language Models	Dec 1, 2020	Knowledge Graph CompletionKnowledge Graphs	CodeCode Available	1
TableGPT: Few-shot Table-to-Text Generation with Table Structure Reconstruction and Content Matching	Dec 1, 2020	Few-Shot LearningLanguage Modeling	CodeCode Available	1
NUT-RC: Noisy User-generated Text-oriented Reading Comprehension	Dec 1, 2020	Answer SelectionArticles	CodeCode Available	0
Modeling Local Contexts for Joint Dialogue Act Recognition and Sentiment Classification with Bi-channel Dynamic Convolutions	Dec 1, 2020	Language ModelingLanguage Modelling	—Unverified	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