Multi-Task Learning
1098 papers with code • 6 benchmarks • 55 datasets
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 )
Libraries
Use these libraries to find Multi-Task Learning models and implementations
Cityscapes
CelebA
NYUv2
Clotho
ETHICS
KP20k
Hypersim
CELEX
Latest papers with no code
FedAuxHMTL: Federated Auxiliary Hard-Parameter Sharing Multi-Task Learning for Network Edge Traffic Classification
Federated Learning (FL) has garnered significant interest recently due to its potential as an effective solution for tackling many challenges in diverse application scenarios, for example, data privacy in network edge traffic classification.
GraSAME: Injecting Token-Level Structural Information to Pretrained Language Models via Graph-guided Self-Attention Mechanism
Pretrained Language Models (PLMs) benefit from external knowledge stored in graph structures for various downstream tasks.
Interplay of Machine Translation, Diacritics, and Diacritization
We examine these two questions in both high-resource (HR) and low-resource (LR) settings across 55 different languages (36 African languages and 19 European languages).
Enhanced Radar Perception via Multi-Task Learning: Towards Refined Data for Sensor Fusion Applications
Radar and camera fusion yields robustness in perception tasks by leveraging the strength of both sensors.
Panoptic Perception: A Novel Task and Fine-grained Dataset for Universal Remote Sensing Image Interpretation
Experimental results on FineGrip demonstrate the feasibility of the panoptic perception task and the beneficial effect of multi-task joint optimization on individual tasks.
Multi-Task Learning for Lung sound & Lung disease classification
In this work, a novel approach using multi-task learning (MTL) for the simultaneous classification of lung sounds and lung diseases is proposed.
Multi-task learning via robust regularized clustering with non-convex group penalties
This provides an interpretation of the robustness of MTLRRC against outlier tasks.
Multimodal hierarchical multi-task deep learning framework for jointly predicting and explaining Alzheimer disease progression
We proposed a multimodal hierarchical multi-task learning approach which can monitor the risk of disease progression at each timepoint of the visit trajectory.
M3TCM: Multi-modal Multi-task Context Model for Utterance Classification in Motivational Interviews
Accurate utterance classification in motivational interviews is crucial to automatically understand the quality and dynamics of client-therapist interaction, and it can serve as a key input for systems mediating such interactions.
Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term Retention
In the landscape of Recommender System (RS) applications, reinforcement learning (RL) has recently emerged as a powerful tool, primarily due to its proficiency in optimizing long-term rewards.
