Biological organisms learn from interactions with their environment throughout their lifetime.
For artificial systems to successfully act and adapt in the real world, it is desirable to similarly …

Due to advancements in data collection, storage, and processing techniques, machine
learning has become a thriving and dominant paradigm. However, one of its main …

Despite the advancement of machine learning techniques in recent years, state-of-the-art
systems lack robustness to “real world” events, where the input distributions and tasks …

One approach to meet the challenges of deep lifelong reinforcement learning (LRL) is
careful management of the agent's learning experiences, to learn (without forgetting) and …

In streaming data applications, the incoming samples are processed and discarded, and
therefore, intelligent decision-making is crucial for the performance of lifelong learning …

Processing big data streams through machine learning algorithms has various challenges,
such as little time to train the models, hardware memory constraints, and concept drift. In this …

Class incremental learning is committed to solving representation learning and classification
assignments while avoiding catastrophic forgetting in scenarios where categories are …

Recent research has illuminated the complexity and importance of the thalamocortical
system but it has been difficult to identify what computational functions it performs …

An environment that supports lifelong learning contributes to the sustainability of the
organisations in a Smart City, their stakeholders and ultimately, the city itself. Workplace …

Lifelong learning in the 21st century is a very important skill for every human being in the
face of an era of rapidly developing disruption. Rapid changes in information technology are …