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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 1476–1500 of 3687 papers

TitleStatusHype
Selective Inference for Sparse Multitask Regression with Applications in Neuroimaging—0
Acoustic-to-articulatory Speech Inversion with Multi-task Learning—0
Cascading Residual Graph Convolutional Network for Multi-Behavior RecommendationCode1
Eliciting and Understanding Cross-Task Skills with Task-Level Mixture-of-ExpertsCode0
Primitive3D: 3D Object Dataset Synthesis from Randomly Assembled Primitives—0
Building a Dialogue Corpus Annotated with Expressed and Experienced EmotionsCode0
ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft PromptsCode1
ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterest—0
uGLAD: Sparse graph recovery by optimizing deep unrolled networksCode1
On Measuring Social Biases in Prompt-Based Multi-Task LearningCode1
All Birds with One Stone: Multi-task Text Classification for Efficient Inference with One Forward Pass—0
Multi-task Learning for Gaussian Graphical Regressions with High Dimensional Covariates—0
DELATOR: Money Laundering Detection via Multi-Task Learning on Large Transaction Graphs—0
Let the Model Decide its Curriculum for Multitask Learning—0
Improving Multi-Task Generalization via Regularizing Spurious Correlation—0
BEVerse: Unified Perception and Prediction in Birds-Eye-View for Vision-Centric Autonomous DrivingCode2
Flexible Modeling and Multitask Learning using Differentiable Tree Ensembles—0
Wojood: Nested Arabic Named Entity Corpus and Recognition using BERTCode1
Persian Natural Language Inference: A Meta-learning approachCode0
When to Use Multi-Task Learning vs Intermediate Fine-Tuning for Pre-Trained Encoder Transfer LearningCode0
Mitigating Hidden Confounding Effects for Causal Recommendation—0
Multi Task Learning For Zero Shot Performance Prediction of Multilingual Models—0
Towards Unified Prompt Tuning for Few-shot Text Classification—0
An Empirical Study Of Self-supervised Learning Approaches For Object Detection With TransformersCode0
Over-the-Air Federated Multi-Task Learning via Model Sparsification and Turbo Compressed Sensing—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