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Multi-Fashion+Multi-MNIST
The dataset used in the paper is a multi-task learning dataset, where the goal is to learn a shared feature extractor and a task-specific predictor for multiple tasks. -
Multi-Task Learning for Federated Classification and Regression
The proposed algorithm allows personalizing the learning model for each participant without sharing the training data and improves the performance, compared to that of the... -
Semisoft Task Clustering for Multi-Task Learning
The proposed STCMTL approach can simultaneously learn an overlapping structure among tasks and perform feature selection. -
A Transformer-Based Deep Learning Approach for Fairly Predicting Post-Liver T...
Liver transplantation is a life-saving procedure for patients with end-stage liver disease. There are two main challenges in liver transplant: finding the best matching patient... -
MiniGPT-v2
MiniGPT-v2 is a vision-language model that uses a unified interface for multi-task learning. -
A Multi-Task Learning Model for Super Resolution of Wireless Channel Characte...
Channel modeling has always been the core part in communication system design and development, especially in 5G and 6G era. Traditional approaches like stochastic channel... -
PASCAL-Context and NYUD-v2 datasets
The PASCAL-Context and NYUD-v2 datasets are used for multi-task learning in dense scene understanding. -
StreamSpeech: Simultaneous Speech-to-Speech Translation with Multi-task Learning
Simultaneous speech-to-speech translation with multi-task learning -
Task-Aware Low-Rank Adaptation of Segment Anything Model
The Segment Anything Model (SAM) has been proven to be a powerful foundation model for image segmentation tasks, which is an important task in computer vision. However, the... -
Interpretable Multi-Task Deep Neural Networks for Dynamic Predictions of Post...
A large retrospective cohort of 43,943 adult patients undergoing 52,529 major inpatient surgeries.
