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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 AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2601–2625 of 3687 papers

TitleStatusHype
Syntax-Aware Opinion Role Labeling with Dependency Graph Convolutional Networks—0
Sentiment and Emotion help Sarcasm? A Multi-task Learning Framework for Multi-Modal Sarcasm, Sentiment and Emotion Analysis—0
Multi-Task Supervised Pretraining for Neural Domain Adaptation—0
Dynamic Sampling Strategies for Multi-Task Reading Comprehension—0
Multi-Task Variational Information Bottleneck—0
Investigating the effect of auxiliary objectives for the automated grading of learner English speech transcriptions—0
Simple Compounded-Label Training for Fact Extraction and Verification—0
Multi-Cell Compositional LSTM for NER Domain Adaptation—0
Go Figure! Multi-task transformer-based architecture for metaphor detection using idioms: ETS team in 2020 metaphor shared task—0
ZPR2: Joint Zero Pronoun Recovery and Resolution using Multi-Task Learning and BERT—0
How to Ask Good Questions? Try to Leverage Paraphrases—0
Estimating the influence of auxiliary tasks for multi-task learning of sequence tagging tasks—0
Tchebycheff Procedure for Multi-task Text Classification—0
Multi-Domain Named Entity Recognition with Genre-Aware and Agnostic Inference—0
Integrating Graph-Based and Transition-Based Dependency Parsers in the Deep Contextualized Era—0
A BERT-based One-Pass Multi-Task Model for Clinical Temporal Relation Extraction—0
Improving Image Captioning with Better Use of Caption—0
Transferable Multi-level Attention Neural Network for Accurate Prediction of Quantum Chemistry Properties via Multi-task Learning—0
ANA at SemEval-2020 Task 4: mUlti-task learNIng for cOmmonsense reasoNing (UNION)Code0
Computation Offloading in Multi-Access Edge Computing Networks: A Multi-Task Learning Approach—0
SLV: Spatial Likelihood Voting for Weakly Supervised Object Detection—0
Auxiliary Learning by Implicit Differentiation—0
Appearance Learning for Image-based Motion Estimation in Tomography—0
Maximum Roaming Multi-Task LearningCode0
Structured and Localized Image Restoration—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