As a promising area in machine learning, multi-task learning (MTL) aims to improve the
performance of multiple related learning tasks by leveraging useful information among them …

Recently, deep learning has achieved great success in visual tracking. The goal of this
paper is to review the state-of-the-art tracking methods based on deep learning. First, we …

Deep multitask networks, in which one neural network produces multiple predictive outputs,
can offer better speed and performance than their single-task counterparts but are …

Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to
leverage useful information contained in multiple related tasks to help improve the …

In this paper, we develop a deep learning based semantic communication system for
speech transmission, named DeepSC-ST. We take the speech recognition and speech …

We propose dynamic task prioritization for multitask learning. This allows a model to
dynamically prioritize difficult tasks during training, where difficulty is inversely proportional …

Learning to leverage the relationship among diverse image restoration tasks is quite
beneficial for unraveling the intrinsic ingredients behind the degradation. Recent years have …

In this paper, we study a novel knowledge transfer task in the domain of graph neural
networks (GNNs). We strive to train a multi-talented student GNN, without accessing human …

We introduce the Merlin speech synthesis toolkit for neural network-based speech synthesis.
The system takes linguistic features as input, and employs neural networks to predict …

Abstract Multi-task learning (MTL), which optimizes multiple related learning tasks at the
same time, has been widely used in various applications, including natural language …