SOTAVerified

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 376–400 of 3687 papers

TitleStatusHype
Query-by-Example Keyword Spotting Using Spectral-Temporal Graph Attentive Pooling and Multi-Task Learning—0
NeuroLM: A Universal Multi-task Foundation Model for Bridging the Gap between Language and EEG SignalsCode2
Detailed delineation of the fetal brain in diffusion MRI via multi-task learningCode0
Can Optimization Trajectories Explain Multi-Task Transfer?—0
SEQ+MD: Learning Multi-Task as a SEQuence with Multi-Distribution Data—0
Large Language Models as Foundations for Next-Gen Dense Retrieval: A Comprehensive Empirical Assessment—0
WCEbleedGen: A wireless capsule endoscopy dataset and its benchmarking for automatic bleeding classification, detection, and segmentationCode0
DTN: Deep Multiple Task-specific Feature Interactions Network for Multi-Task Recommendation—0
Robust Long-Range Perception Against Sensor Misalignment in Autonomous Vehicles—0
Hokoff: Real Game Dataset from Honor of Kings and its Offline Reinforcement Learning BenchmarksCode2
Customizing Language Models with Instance-wise LoRA for Sequential RecommendationCode1
TeamLoRA: Boosting Low-Rank Adaptation with Expert Collaboration and CompetitionCode0
Elite360M: Efficient 360 Multi-task Learning via Bi-projection Fusion and Cross-task Collaboration—0
MaskBEV: Towards A Unified Framework for BEV Detection and Map Segmentation—0
Multi-task Learning Approach for Intracranial Hemorrhage PrognosisCode0
Dynamic Adaptive Optimization for Effective Sentiment Analysis Fine-Tuning on Large Language Models—0
Lifelong Reinforcement Learning via Neuromodulation—0
Analytical Uncertainty-Based Loss Weighting in Multi-Task Learning—0
Multi-task Heterogeneous Graph Learning on Electronic Health RecordsCode1
Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and OpportunitiesCode4
HoME: Hierarchy of Multi-Gate Experts for Multi-Task Learning at Kuaishou—0
VITA: Towards Open-Source Interactive Omni Multimodal LLMCode7
Exploring Query Understanding for Amazon Product Search—0
Synergistic Learning with Multi-Task DeepONet for Efficient PDE Problem SolvingCode0
Table Transformers for Imputing Textual AttributesCode0
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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