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 1926–1950 of 3687 papers

TitleStatusHype
MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling—0
MaxGNR: A Dynamic Weight Strategy via Maximizing Gradient-to-Noise Ratio for Multi-Task Learning—0
Towards Generalized Models for Task-oriented Dialogue Modeling on Spoken Conversations—0
MaZO: Masked Zeroth-Order Optimization for Multi-Task Fine-Tuning of Large Language Models—0
MB-HGCN: A Hierarchical Graph Convolutional Network for Multi-behavior Recommendation—0
MCapsNet: Capsule Network for Text with Multi-Task Learning—0
MCNE: An End-to-End Framework for Learning Multiple Conditional Network Representations of Social Network—0
MD-MTL: An Ensemble Med-Multi-Task Learning Package for DiseaseScores Prediction and Multi-Level Risk Factor Analysis—0
Measuring and Harnessing Transference in Multi-Task Learning—0
AutoTaskFormer: Searching Vision Transformers for Multi-task Learning—0
Addressing Negative Transfer in Diffusion Models—0
SANSformers: Self-Supervised Forecasting in Electronic Health Records with Attention-Free Models—0
AutoSTL: Automated Spatio-Temporal Multi-Task Learning—0
Medusa: Universal Feature Learning via Attentional Multitasking—0
Addressing Limitations of State-Aware Imitation Learning for Autonomous Driving—0
AutoSeM: Automatic Task Selection and Mixing in Multi-Task Learning—0
Merging Smarter, Generalizing Better: Enhancing Model Merging on OOD Data—0
Automatic quality assessment for 2D fetal sonographic standard plane based on multi-task learning—0
Automatic Plane Adjustment of Orthopedic Intra-operative Flat Panel Detector CT-Volumes—0
MetaBEV: Solving Sensor Failures for 3D Detection and Map Segmentation—0
Automatic Noun Compound Interpretation using Deep Neural Networks and Word Embeddings—0
Towards Improving Abstractive Summarization via Entailment Generation—0
Metadata-Enhanced Speech Emotion Recognition: Augmented Residual Integration and Co-Attention in Two-Stage Fine-Tuning—0
MetaGPT: Merging Large Language Models Using Model Exclusive Task Arithmetic—0
Automatic Expert Selection for Multi-Scenario and Multi-Task Search—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