Frontiers

AM-MTEEG: multi-task EEG classification based on impulsive associative memory

Electroencephalogram-based brain-computer interfaces (BCIs) hold promise for healthcare applications but are hindered by cross-subject variability and limited data. This article proposes a multi-task ...
C&EN

Attention-Enhanced Multi-Task Deep Learning Model for Classification and Segmentation of Esophageal Lesions

Pathophysiology Department, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou 450001, China Tianjian Laboratory of Advanced Biomedical Sciences, Zhengzhou University, Zhengzhou, Henan ...
Scientific Research Publishing

Crawshaw, M. (2020) Multi-Task Learning with Deep Neural Networks: A Survey.

ABSTRACT: Thoracic diseases pose significant risks to an individual's chest health and are among the most perilous medical diseases. They can impact either one or both lungs, which leads to a severe ...
Frontiers

Automatic tongue image quality assessment using a multi-task deep learning model

The quality of tongue images has a significant influence on the performance of tongue diagnosis in Chinese medicine. During the acquisition process, the quality of the tongue image is easily affected ...
GitHub

Transfer and Multi-task Reinforcement Learning

Reinforcement Learning (RL) is a learning paradigm to solve many decision-making problems, which are usually formalized as Markov Decision Processes (MDP). Recently, Deep Reinforcement Learning (DRL) ...
C&EN

Unified Deep Learning Model for Multitask Reaction Predictions with Explanation

There is significant interest and importance to develop robust machine learning models to assist organic chemistry synthesis. Typically, task-specific machine learning models for distinct reaction ...
IEEE

Framework for Deep Learning-Based Language Models Using Multi-Task Learning in Natural Language Understanding: A Systematic Literature Review and Future Directions

Abstract: Learning human languages is a difficult task for a computer. However, Deep Learning (DL) techniques have enhanced performance significantly for almost all-natural language processing (NLP) ...
IEEE

A Hybrid Multi-Task Learning Approach for Optimizing Deep Reinforcement Learning Agents

Abstract: Driven by recent technological advancements within the field of artificial intelligence (AI), deep learning (DL) has been emerged as a promising representation learning technique across ...