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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

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Showing 2876–2900 of 3687 papers

TitleStatusHype
UniRQR: A Unified Model for Retrieval Decision, Query, and Response Generation in Internet-Based Knowledge Dialogue Systems—0
Reinforcement Learning Based Emotional Editing Constraint Conversation Generation—0
A Multi-task Learning Approach for Named Entity Recognition using Local Detection—0
Related Tasks can Share! A Multi-task Framework for Affective language—0
A Multi-Task Learning Approach for Meal Assessment—0
A Multi-task Learning Approach for Improving Product Title Compression with User Search Log Data—0
Relation Extraction from Biomedical and Clinical Text: Unified Multitask Learning Framework—0
Relative Geometry-Aware Siamese Neural Network for 6DOF Camera Relocalization—0
Neural Relighting and Expression Transfer On Video Portraits—0
A Multitask Learning Approach for Diacritic Restoration—0
Replay spoofing detection system for automatic speaker verification using multi-task learning of noise classes—0
Representation Learning Using Multi-Task Deep Neural Networks for Semantic Classification and Information Retrieval—0
Representation Retrieval Learning for Heterogeneous Data Integration—0
A Multi-Task Learning Algorithm for Non-personalized Recommendations—0
Representative Task Self-selection for Flexible Clustered Lifelong Learning—0
Repulsive Mixtures—0
A Multi-task Event and Argument Trigger Detection in Hindi using POS Tagging as an Auxiliary Task—0
Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Intervention—0
Residual acoustic echo suppression based on efficient multi-task convolutional neural network—0
A Comprehensive Evaluation of Large Language Models on Aspect-Based Sentiment Analysis—0
Response Selection for Multi-Party Conversations withDynamic Topic Tracking—0
Response Selection for Multi-Party Conversations with Dynamic Topic Tracking—0
Restoring and Mining the Records of the Joseon Dynasty via Neural Language Modeling and Machine Translation—0
A Comparison of Character Neural Language Model and Bootstrapping for Language Identification in Multilingual Noisy Texts—0
Restricted Deformable Convolution based Road Scene Semantic Segmentation Using Surround View Cameras—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