Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

In the rapidly evolving domain of machine learning, the ability to adapt to unforeseen
circumstances and novel data types is of paramount importance. The deployment of Artificial …

Efficiently executing inference tasks of deep neural networks on devices with limited
resources poses a significant load in IoT systems. To alleviate the load, one innovative …

In the pursuit of refining precise perception models for fully autonomous driving, continual
online model training becomes essential. Federated Learning (FL) within vehicular networks …

Offline reinforcement learning (RL) aims to learn policies entirely from passively collected
datasets, making it a data‐driven decision method. One of the main challenges in offline RL …

The diverse nature of dialects presents challenges for models trained on specific linguistic
patterns, rendering them susceptible to errors when confronted with unseen or out-of …

Offline RL suffers from the distribution shift problem. One way to address this issue is to
constrain the divergence between the target policy and the behavior policy. However …