To cope with real-world dynamics, an intelligent system needs to incrementally acquire,
update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as …

As the societal impact of Deep Neural Networks (DNNs) grows, the goals for advancing
DNNs become more complex and diverse, ranging from improving a conventional model …

Unlike previous studies on the Metaverse based on Second Life, the current Metaverse is
based on the social value of Generation Z that online and offline selves are not different …

DARPA formulated the Explainable Artificial Intelligence (XAI) program in 2015 with the goal
to enable end users to better understand, trust, and effectively manage artificially intelligent …

Modern machine learning pipelines are limited due to data availability, storage quotas,
privacy regulations, and expensive annotation processes. These constraints make it difficult …

Rehearsal approaches enjoy immense popularity with Continual Learning (CL)
practitioners. These methods collect samples from previously encountered data distributions …

In contrast to batch learning where all training data is available at once, continual learning
represents a family of methods that accumulate knowledge and learn continuously with data …

Continual learning tackles the setting of learning different tasks sequentially. Despite the lots
of previous solutions, most of them still suffer significant forgetting or expensive memory …

Although pretrained language models (PTLMs) contain significant amounts of world
knowledge, they can still produce inconsistent answers to questions when probed, even …

Deep learning techniques have become one of the main propellers for solving engineering
problems effectively and efficiently. For instance, Predictive Maintenance methods have …