Online learning represents a family of machine learning methods, where a learner attempts
to tackle some predictive (or any type of decision-making) task by learning from a sequence …

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 …

Multi-task learning can leverage information learned by one task to benefit the training of
other tasks. Despite this capacity, naively training all tasks together in one model often …

Multi-task learning (MTL) is a subfield of machine learning in which multiple tasks are
simultaneously learned by a shared model. Such approaches offer advantages like …

With the advent of deep learning, many dense prediction tasks, ie, tasks that produce pixel-
level predictions, have seen significant performance improvements. The typical approach is …

Federated learning (FL) is currently the most widely adopted framework for collaborative
training of (deep) machine learning models under privacy constraints. Albeit its popularity, it …

Modern federated networks, such as those comprised of wearable devices, mobile phones,
or autonomous vehicles, generate massive amounts of data each day. This wealth of data …

Abstract Inspired by Lottery Ticket Hypothesis that competitive subnetworks exist within a
dense network, we propose a continual learning method referred to as Winning …

We propose a novel multi-task learning architecture, which allows learning of task-specific
feature-level attention. Our design, the Multi-Task Attention Network (MTAN), consists of a …

Transfer learning, which allows a source task to affect the inductive bias of the target task, is
widely used in computer vision. The typical way of conducting transfer learning with deep …