SOTAVerified

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 AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1676–1700 of 3687 papers

TitleStatusHype
KOAS: Korean Text Offensiveness Analysis System—0
Annotations Matter: Leveraging Multi-task Learning to Parse UD and SUD—0
Learning to Jointly Generate and Separate Reflections—0
Learning to Learn Weight Generation via Local Consistency Diffusion—0
Learning to Recommend with Multiple Cascading Behaviors—0
Learning to Branch for Multi-Task Learning—0
Columbia at SemEval-2019 Task 7: Multi-task Learning for Stance Classification and Rumour Verification—0
Learning to Generate Questions by Learning What not to Generate—0
Label Budget Allocation in Multi-Task Learning—0
ColNet: Collaborative Optimization in Decentralized Federated Multi-task Learning Systems—0
Automatic Expert Selection for Multi-Scenario and Multi-Task Search—0
College Student Retention Risk Analysis From Educational Database using Multi-Task Multi-Modal Neural Fusion—0
Language-Independent Representor for Neural Machine Translation—0
Language Modelling as a Multi-Task Problem—0
Federated Multi-Objective Learning—0
Federated Multi-Task Learning for THz Wideband Channel and DoA Estimation—0
Large Dimensional Analysis and Improvement of Multi Task Learning—0
Anno-incomplete Multi-dataset Detection—0
Large Language Model as a Universal Clinical Multi-task Decoder—0
Large Language Models as Foundations for Next-Gen Dense Retrieval: A Comprehensive Empirical Assessment—0
Federated Learning for Estimating Heterogeneous Treatment Effects—0
Developing a High-performance Framework for Speech Emotion Recognition in Naturalistic Conditions Challenge for Emotional Attribute Prediction—0
Large Margin Multi-Task Metric Learning—0
A Benchmark for Federated Hetero-Task Learning—0
Collaborative Optimization in Financial Data Mining Through Deep Learning and ResNeXt—0
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All datasetsQM9Cityscapes testNYUv2OMNIGLOTCelebAUTKFacewireframe dataset

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PCGrad∆m%125.7—Unverified
2CAGrad∆m%112.8—Unverified
3IMTL-G∆m%77.2—Unverified
4Nash-MTL∆m%62—Unverified
5BayesAgg-MTL∆m%53.7—Unverified
#ModelMetricClaimedVerifiedStatus
1SwinMTLmIoU76.41—Unverified
2Nash-MTLmIoU75.41—Unverified
3MultiObjectiveOptimizationmIoU66.63—Unverified
#ModelMetricClaimedVerifiedStatus
1SwinMTLMean IoU58.14—Unverified
2Nash-MTLMean IoU40.13—Unverified
#ModelMetricClaimedVerifiedStatus
1Gumbel-Matrix RoutingAverage Accuracy93.52—Unverified
2Mixture-of-ExpertsAverage Accuracy92.19—Unverified
#ModelMetricClaimedVerifiedStatus
1MGDA-UBError8.25—Unverified
#ModelMetricClaimedVerifiedStatus
1BayesAgg-MTLdelta_m-2.23—Unverified
#ModelMetricClaimedVerifiedStatus
1LETRFH83.3—Unverified