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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 101–125 of 3687 papers

TitleStatusHype
CH-SIMS: A Chinese Multimodal Sentiment Analysis Dataset with Fine-grained Annotation of ModalityCode1
Clinical Risk Prediction with Temporal Probabilistic Asymmetric Multi-Task LearningCode1
A generic physics-informed neural network-based framework for reliability assessment of multi-state systemsCode1
Combinatorial Optimization Perspective based Framework for Multi-behavior RecommendationCode1
Compressed Interaction Graph based Framework for Multi-behavior RecommendationCode1
ComSL: A Composite Speech-Language Model for End-to-End Speech-to-Text TranslationCode1
A Comprehensive Evaluation of Multi-task Learning and Multi-task Pre-training on EHR Time-series DataCode1
Context-Aware Answer Extraction in Question AnsweringCode1
CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and GenerationCode1
Continued Pretraining for Better Zero- and Few-Shot PromptabilityCode1
Causal Inference for Chatting HandoffCode1
DenseMTL: Cross-task Attention Mechanism for Dense Multi-task LearningCode1
CTIN: Robust Contextual Transformer Network for Inertial NavigationCode1
Specialty-Oriented Generalist Medical AI for Chest CT ScreeningCode1
A Gradually Soft Multi-Task and Data-Augmented Approach to Medical Question UnderstandingCode1
Adversarial Multi-task Learning Enhanced Physics-informed Neural Networks for Solving Partial Differential EquationsCode1
Acoustic Prompt Tuning: Empowering Large Language Models with Audition CapabilitiesCode1
A Dependency Syntactic Knowledge Augmented Interactive Architecture for End-to-End Aspect-based Sentiment AnalysisCode1
Deeper Task-Specificity Improves Joint Entity and Relation ExtractionCode1
Deep Homography Estimation for Dynamic ScenesCode1
AdaMSS: Adaptive Multi-Modality Segmentation-to-Survival Learning for Survival Outcome Prediction from PET/CT ImagesCode1
DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CTCode1
CentralNet: a Multilayer Approach for Multimodal FusionCode1
DeMT: Deformable Mixer Transformer for Multi-Task Learning of Dense PredictionCode1
AIONER: All-in-one scheme-based biomedical named entity recognition using deep learningCode1
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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