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 …

In e-commerce, user reviews can play a significant role in determining the revenue of an
organisation. Online users rely on reviews before making decisions about any product and …

The increasing size of data generated by smartphones and IoT devices motivated the
development of Federated Learning (FL), a framework for on-device collaborative training of …

In multi-task learning, multiple tasks are solved jointly, sharing inductive bias between them.
Multi-task learning is inherently a multi-objective problem because different tasks may …

We propose a new optimization formulation for training federated learning models. The
standard formulation has the form of an empirical risk minimization problem constructed to …

Collaborative filtering often suffers from sparsity and cold start problems in real
recommendation scenarios, therefore, researchers and engineers usually use side …

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 …

Federated learning poses new statistical and systems challenges in training machine
learning models over distributed networks of devices. In this work, we show that multi-task …

Transfer learning (TL) utilizes data or knowledge from one or more source domains to
facilitate learning in a target domain. It is particularly useful when the target domain has very …

Object detection, scene graph generation and region captioning, which are three scene
understanding tasks at different semantic levels, are tied together: scene graphs are …