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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 2401–2450 of 3687 papers

TitleStatusHype
Learnable Dependency-based Double Graph Structure for Aspect-based Sentiment Analysis—0
Robust and flexible learning of a high-dimensional classification rule using auxiliary outcomes—0
Learning quantum properties from short-range correlations using multi-task networks—0
Learning an Interpretable Graph Structure in Multi-Task Learning—0
Learning Answer-Entailing Structures for Machine Comprehension—0
Learning attention for historical text normalization by learning to pronounce—0
Learning-based Bone Quality Classification Method for Spinal Metastasis—0
Learning behavioral context recognition with multi-stream temporal convolutional networks—0
Learning Boost by Exploiting the Auxiliary Task in Multi-task Domain—0
Learning Cancer Outcomes from Heterogeneous Genomic Data Sources: An Adversarial Multi-task Learning Approach—0
Learning Cross-Task Attribute - Attribute Similarity for Multi-task Attribute-Value Extraction—0
Learning curves for multi-task Gaussian process regression—0
Learning Disentangled Representations for Counterfactual Regression via Mutual Information Minimization—0
Learning distributed event representations with a multi-task approach—0
Learning Domain Representation for Multi-Domain Sentiment Classification—0
Learning Feature Selection Dependencies in Multi-task Learning—0
Learning Flexible Translation between Robot Actions and Language Descriptions—0
Learning from Auxiliary Sources in Argumentative Revision Classification—0
Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization—0
Learning from Multiway Data: Simple and Efficient Tensor Regression—0
Learning from Synthetic Data: Facial Expression Classification based on Ensemble of Multi-task Networks—0
Learning Gaussian Maps for Dense Object Detection—0
Learning heuristics for A*—0
Learning More for Free - A Multi Task Learning Approach for Improved Pathology Classification in Capsule Endoscopy—0
Learning Multiple Tasks with Multilinear Relationship Networks—0
Learning Multiple Visual Tasks while Discovering their Structure—0
Learning Multi-Tasks with Inconsistent Labels by using Auxiliary Big Task—0
Learning Multi-Task Transferable Rewards via Variational Inverse Reinforcement Learning—0
Learning Optical Flow, Depth, and Scene Flow without Real-World Labels—0
Learning Partially Aligned Item Representation for Cross-Domain Sequential Recommendation—0
Learning Predictive, Online Approximations of Explanatory, Offline Algorithms—0
Learning Rates for Multi-task Regularization Networks—0
Learning Representation for Multitask learning through Self Supervised Auxiliary learning—0
Learning representations for sentiment classification using Multi-task framework—0
Learning Representations for Text-level Discourse Parsing—0
Learning Shared Dynamics with Meta-World Models—0
Learning Task Grouping and Overlap in Multi-task Learning—0
Learning Task Relatedness in Multi-Task Learning for Images in Context—0
Learning to Branch for Multi-Task Learning—0
Learning to Generate Questions by Learning What not to Generate—0
Learning to Initialize: Can Meta Learning Improve Cross-task Generalization in Prompt Tuning?—0
Learning to Jointly Generate and Separate Reflections—0
Learning to Learn Weight Generation via Local Consistency Diffusion—0
Learning to Multi-Task Learn for Better Neural Machine Translation—0
Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps—0
Learning to optimize by multi-gradient for multi-objective optimization—0
Learning to Profile: User Meta-Profile Network for Few-Shot Learning—0
Learning to Push by Grasping: Using multiple tasks for effective learning—0
Learning to Recommend with Multiple Cascading Behaviors—0
Learning to segment clustered amoeboid cells from brightfield microscopy via multi-task learning with adaptive weight selection—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