Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …

Deep learning (DL) has emerged as a powerful subset of machine learning (ML) and
artificial intelligence (AI), outperforming traditional ML methods, especially in handling …

Misalignment between model predictions and intended usage can be detrimental for the
deployment of computer vision models. The issue is exacerbated when the task involves …

The vigorous developments of the Internet of Things make it possible to extend its computing
and storage capabilities to computing tasks in the aerial system with the collaboration of …

Nowadays speech synthesis or text to speech (TTS), an ability of system to produce human
like natural sounding voice from the written text, is gaining popularity in the field of speech …

The growth in the volume of data generated, consumed, and stored, which is estimated to
exceed 180 zettabytes in 2025, represents a major challenge both for organizations and for …

While real-world applications of reinforcement learning (RL) are becoming popular, the
security and robustness of RL systems are worthy of more attention and exploration. In …

With the increasing penetration of renewable energy and flexible loads in smart grids, a
more complicated power system with high uncertainty is gradually formed, which brings …

In recent years, reinforcement learning and bandits have transformed a wide range of real-
world applications including healthcare, finance, recommendation systems, robotics, and …

Enclosing a maneuverable target for Unmanned Aerial Vehicles (UAVs) in a constrained
environment is intractable as existing methods fail to coordinate collision safety and tracking …