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 3601–3625 of 3687 papers

TitleStatusHype
Matrix and Tensor Factorization Methods for Natural Language Processing—0
Learning Representations for Text-level Discourse Parsing—0
Multi-Task Learning for Multiple Language Translation—0
Question Answering over Freebase with Multi-Column Convolutional Neural Networks—0
Learning Answer-Entailing Structures for Machine Comprehension—0
Framework for Multi-task Multiple Kernel Learning and Applications in Genome Analysis—0
Learning Multiple Tasks with Multilinear Relationship Networks—0
Automatically Scoring Freshman Writing: A Preliminary Investigation—0
ECNU: Using Traditional Similarity Measurements and Word Embedding for Semantic Textual Similarity Estimation—0
Lifelong Machine Learning for Topic Modeling and Beyond—0
Deeply Learned Attributes for Crowded Scene Understanding—0
Multi-Feature Max-Margin Hierarchical Bayesian Model for Action Recognition—0
Multi-Task Deep Visual-Semantic Embedding for Video Thumbnail Selection—0
Hyper-Class Augmented and Regularized Deep Learning for Fine-Grained Image Classification—0
Classifying Tweet Level Judgements of Rumours in Social Media—0
Deep sparse multi-task learning for feature selection in Alzheimer’s disease diagnosis—0
Multi-task additive models with shared transfer functions based on dictionary learning—0
Representation Learning Using Multi-Task Deep Neural Networks for Semantic Classification and Information Retrieval—0
Multi-Task Word Alignment Triangulation for Low-Resource Languages—0
Convex Learning of Multiple Tasks and their StructureCode0
Learning Multiple Visual Tasks while Discovering their Structure—0
Automatic Noun Compound Interpretation using Deep Neural Networks and Word Embeddings—0
A Unified Perspective on Multi-Domain and Multi-Task Learning—0
Deep Structured Output Learning for Unconstrained Text Recognition—0
Curriculum Learning of Multiple Tasks—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