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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 3501–3525 of 3687 papers

TitleStatusHype
Explanations from Large Language Models Make Small Reasoners Better—0
Explicitly modeling case improves neural dependency parsing—0
Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling—0
Exploiting Dialogue Act for Knowledge Selection and Response Generation—0
Exploiting Entity BIO Tag Embeddings and Multi-task Learning for Relation Extraction with Imbalanced Data—0
Coupled Hierarchical Transformer for Stance-Aware Rumor Verification in Social Media Conversations—0
Exploiting Linguistic Resources for Neural Machine Translation Using Multi-task Learning—0
Exploiting Low-Resource Code-Switching Data to Mandarin-English Speech Recognition Systems—0
Exploiting Method Names to Improve Code Summarization: A Deliberation Multi-Task Learning Approach—0
Exploiting Multi-typed Treebanks for Parsing with Deep Multi-task Learning—0
Exploiting Mutual Benefits between Syntax and Semantic Roles using Neural Network—0
Exploiting Narrative Context and A Priori Knowledge of Categories in Textual Emotion Classification—0
Correlated Time Series Forecasting using Deep Neural Networks: A Summary of Results—0
Exploiting Source-side Monolingual Data in Neural Machine Translation—0
A Novel Technique for Evidence based Conditional Inference in Deep Neural Networks via Latent Feature Perturbation—0
Exploiting the Syntax-Model Consistency for Neural Relation Extraction—0
Exploiting Transformer-based Multitask Learning for the Detection of Media Bias in News Articles—0
A Federated Learning Approach to Anomaly Detection in Smart Buildings—0
Co-Representation Learning For Classification and Novel Class Detection via Deep Networks—0
Exploring Cross-Lingual Transfer of Morphological Knowledge In Sequence-to-Sequence Models—0
Exploring Logically Dependent Multi-task Learning with Causal Inference—0
Structure-Preserving Image Super-resolution via Contextualized Multi-task Learning—0
Exploring Named Entity Recognition As an Auxiliary Task for Slot Filling in Conversational Language Understanding—0
Exploring Reciprocal Attention for Salient Object Detection by Cooperative Learning—0
Exploring Shared Structures and Hierarchies for Multiple NLP Tasks—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