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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 3251–3300 of 3687 papers

TitleStatusHype
Hierarchical Multi Task Learning With CTC—0
A Modulation Module for Multi-task Learning with Applications in Image RetrievalCode0
Neural Chinese Word Segmentation with Dictionary Knowledge—0
Kernel-Based Learning for Smart Inverter Control—0
A Personal Privacy Preserving Framework: I Let You Know Who Can See What—0
Multi-Task Learning with Incomplete Data for Healthcare—0
Regularizing Autoencoder-Based Matrix Completion Models via Manifold Learning—0
Polarity and Intensity: the Two Aspects of Sentiment Analysis—0
Discriminative Feature Learning with Foreground Attention for Person Re-Identification—0
Improving part-of-speech tagging via multi-task learning and character-level word representations—0
A Unified Framework for Structured Low-rank Matrix Learning—0
Nonparametric variable importance using an augmented neural network with multi-task learningCode0
Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph—0
Character-level Supervision for Low-resource POS Tagging—0
Multi-task learning for historical text normalization: Size matters—0
A Multi-lingual Multi-task Architecture for Low-resource Sequence LabelingCode0
Supervised Treebank Conversion: Data and Approaches—0
Multi-task learning for interpretable cause of death classification using key phrase prediction—0
Adaptive Knowledge Sharing in Multi-Task Learning: Improving Low-Resource Neural Machine Translation—0
3D RoI-aware U-Net for Accurate and Efficient Colorectal Tumor SegmentationCode0
A Multi-Task Learning Approach for Meal Assessment—0
Semi-Automatic RECIST Labeling on CT Scans with Cascaded Convolutional Neural Networks—0
Explainable Outfit Recommendation with Joint Outfit Matching and Comment Generation—0
Multi-task WaveNet: A Multi-task Generative Model for Statistical Parametric Speech Synthesis without Fundamental Frequency Conditions—0
Gradient Adversarial Training of Neural Networks—0
Standard Plane Detection in 3D Fetal Ultrasound Using an Iterative Transformation NetworkCode0
Dynamic Multi-Level Multi-Task Learning for Sentence Simplification—0
Fast Multiple Landmark Localisation Using a Patch-based Iterative NetworkCode0
GILE: A Generalized Input-Label Embedding for Text ClassificationCode0
A Unified Framework for Generalizable Style Transfer: Style and Content SeparationCode0
Multi-Task Neural Models for Translating Between Styles Within and Across LanguagesCode0
Unsupervised Meta-Learning for Reinforcement Learning—0
Multi-Task Deep Networks for Depth-Based 6D Object Pose and Joint Registration in Crowd Scenarios—0
Multi-task learning of daily work and study round-trips from survey data—0
High Dimensional Data Enrichment: Interpretable, Fast, and Data-Efficient—0
All-in-one: Multi-task Learning for Rumour Verification—0
Explainable Recommendation via Multi-Task Learning in Opinionated Text DataCode0
Domain Adversarial Training for Accented Speech Recognition—0
Multi-Task Active Learning for Neural Semantic Role Labeling on Low Resource Conversational Corpus—0
Gated Multi-Task Network for Text Classification—0
End-to-End Learning of Task-Oriented Dialogs—0
Multi-Task Learning by Maximizing Statistical Dependence—0
Improve Neural Entity Recognition via Multi-Task Data Selection and Constrained Decoding—0
KU-MTL at SemEval-2018 Task 1: Multi-task Identification of Affect in Tweets—0
Word Emotion Induction for Multiple Languages as a Deep Multi-Task Learning ProblemCode0
A Comparison of Character Neural Language Model and Bootstrapping for Language Identification in Multilingual Noisy Texts—0
Predicting misreadings from gaze in children with reading difficulties—0
deepSA2018 at SemEval-2018 Task 1: Multi-task Learning of Different Label for Affect in Tweets—0
UniMelb at SemEval-2018 Task 12: Generative Implication using LSTMs, Siamese Networks and Semantic Representations with Synonym Fuzzing—0
Learning Domain Representation for Multi-Domain Sentiment Classification—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