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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 2551–2600 of 3687 papers

TitleStatusHype
Meta-Transfer Learning Empowered Temporal Graph Networks for Cross-City Real Estate Appraisal—0
MetaWeighting: Learning to Weight Tasks in Multi-Task Learning—0
Metric-based Regularization and Temporal Ensemble for Multi-task Learning using Heterogeneous Unsupervised Tasks—0
MFL_COVID19: Quantifying Country-based Factors affecting Case Fatality Rate in Early Phase of COVID-19 Epidemic via Regularised Multi-task Feature Learning—0
Migrating Knowledge between Physical Scenarios based on Artificial Neural Networks—0
MilaNLP @ WASSA: Does BERT Feel Sad When You Cry?—0
MIME: Adapting a Single Neural Network for Multi-task Inference with Memory-efficient Dynamic Pruning—0
Minimax Multi-Task Learning and a Generalized Loss-Compositional Paradigm for MTL—0
Minimizing Communication while Maximizing Performance in Multi-Agent Reinforcement Learning—0
Minimum Latency Training Strategies for Streaming Sequence-to-Sequence ASR—0
SoftNER: Mining Knowledge Graphs From Cloud Incidents—0
Minkowski Tracker: A Sparse Spatio-Temporal R-CNN for Joint Object Detection and Tracking—0
Mint: Matrix-Interleaving for Multi-Task Learning—0
MIRA: A Method of Federated MultI-Task Learning for LaRge LAnguage Models—0
MIRACLE: Multi-task Learning based Interpretable Regulation of Autoimmune Diseases through Common Latent Epigenetics—0
Mirror U-Net: Marrying Multimodal Fission with Multi-task Learning for Semantic Segmentation in Medical Imaging—0
Mitigating Bias for Question Answering Models by Tracking Bias Influence—0
Mitigating Clickbait: An Approach to Spoiler Generation Using Multitask Learning—0
Mitigating Hidden Confounding Effects for Causal Recommendation—0
Mitigating Negative Transfer in Multi-Task Learning with Exponential Moving Average Loss Weighting Strategies—0
MIX : a Multi-task Learning Approach to Solve Open-Domain Question Answering—0
Mix Data or Merge Models? Optimizing for Diverse Multi-Task Learning—0
Mixed-norm Regularization for Brain Decoding—0
Mixed-Supervised Dual-Network for Medical Image Segmentation—0
Mixed Supervised Graph Contrastive Learning for Recommendation—0
Mixpert: Mitigating Multimodal Learning Conflicts with Efficient Mixture-of-Vision-Experts—0
Mixtraining: A Better Trade-Off Between Compute and Performance—0
Mixture of Latent Experts Using Tensor Products—0
Mixture-of-LoRAs: An Efficient Multitask Tuning for Large Language Models—0
Mjölnir: A Deep Learning Parametrization Framework for Global Lightning Flash Density—0
MLLMReID: Multimodal Large Language Model-based Person Re-identification—0
MLR: A Two-stage Conversational Query Rewriting Model with Multi-task Learning—0
MmAP : Multi-modal Alignment Prompt for Cross-domain Multi-task Learning—0
MMGER: Multi-modal and Multi-granularity Generative Error Correction with LLM for Joint Accent and Speech Recognition—0
MM-SFENet: Multi-scale Multi-task Localization and Classification of Bladder Cancer in MRI with Spatial Feature Encoder Network—0
MMSpeech: Multi-modal Multi-task Encoder-Decoder Pre-training for Speech Recognition—0
MMTL: The Meta Multi-Task Learning for Aspect Category Sentiment Analysis—0
Mobility-aware Content Preference Learning in Decentralized Caching Networks—0
ModalTune: Fine-Tuning Slide-Level Foundation Models with Multi-Modal Information for Multi-task Learning in Digital Pathology—0
Model-Agnostic Meta-Learning for Natural Language Understanding Tasks in Finance—0
Model-driven Heart Rate Estimation and Heart Murmur Detection based on Phonocardiogram—0
Modeling Conversation Structure and Temporal Dynamics for Jointly Predicting Rumor Stance and Veracity—0
Modeling Local Contexts for Joint Dialogue Act Recognition and Sentiment Classification with Bi-channel Dynamic Convolutions—0
Modeling Long Context for Task-Oriented Dialogue State Generation—0
Modeling Orders of User Behaviors via Differentiable Sorting: A Multi-task Framework to Predicting User Post-click Conversion—0
Modeling Output-Level Task Relatedness in Multi-Task Learning with Feedback Mechanism—0
Modeling Personal Biases in Language Use by Inducing Personalized Word Embeddings—0
Modeling Prosodic Phrasing with Multi-Task Learning in Tacotron-based TTS—0
Modeling Social Norms Evolution for Personalized Sentiment Classification—0
Modeling with Recurrent Neural Networks for Open Vocabulary Slots—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