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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 1301–1325 of 3687 papers

TitleStatusHype
GPT Meets Graphs and KAN Splines: Testing Novel Frameworks on Multitask Fine-Tuned GPT-2 with LoRA—0
GR-2: A Generative Video-Language-Action Model with Web-Scale Knowledge for Robot Manipulation—0
Copula-based conformal prediction for Multi-Target Regression—0
Gradient Adversarial Training of Neural Networks—0
HyperPrompt: Prompt-based Task-Conditioning of Transformers—0
Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation—0
Gradient Coordination for Quantifying and Maximizing Knowledge Transference in Multi-Task Learning—0
Gradient Deconfliction via Orthogonal Projections onto Subspaces For Multi-task Learning—0
Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection—0
A Joint Learning Framework With BERT for Spoken Language Understanding—0
HyperLoader: Integrating Hypernetwork-Based LoRA and Adapter Layers into Multi-Task Transformers for Sequence Labelling—0
FULLER: Unified Multi-modality Multi-task 3D Perception via Multi-level Gradient Calibration—0
Constrained Multi-Task Learning for Automated Essay Scoring—0
Hyper-Class Augmented and Regularized Deep Learning for Fine-Grained Image Classification—0
Granite-Function Calling Model: Introducing Function Calling Abilities via Multi-task Learning of Granular Tasks—0
Grape Cold Hardiness Prediction via Multi-Task Learning—0
Consistency Training of Multi-exit Architectures for Sensor Data—0
Graph-BAS3Net: Boundary-Aware Semi-Supervised Segmentation Network With Bilateral Graph Convolution—0
Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks—0
From Task-Specific Models to Unified Systems: A Review of Model Merging Approaches—0
Graph-Driven Generative Models for Heterogeneous Multi-Task Learning—0
An Unsupervised Multiple-Task and Multiple-Teacher Model for Cross-lingual Named Entity Recognition—0
GraphERE: Jointly Multiple Event-Event Relation Extraction via Graph-Enhanced Event Embeddings—0
Dual-Balancing for Multi-Task Learning—0
HyperGrid: Efficient Multi-Task Transformers with Grid-wise Decomposable Hyper Projections—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