The Open Radio Access Network (RAN) and its embodiment through the O-RAN Alliance
specifications are poised to revolutionize the telecom ecosystem. O-RAN promotes …

Continual learning (CL) is a particular machine learning paradigm where the data
distribution and learning objective change through time, or where all the training data and …

Fine-tuning large-scale pre-trained vision models to downstream tasks is a standard
technique for achieving state-of-the-art performance on computer vision benchmarks …

Early cancer identification is regarded as a challenging problem in cancer prevention for the
healthcare community. In addition, ensuring privacy-preserving healthcare data becomes …

The two-way information exchange between customers and the utility in smart grids enables
demand-response programs of customers and the integration of distributed renewable …

Neural networks and deep learning algorithms are designed to function similarly to
biological synaptic structures. However, classical deep learning algorithms fail to fully …

The great success that deep models have achieved in the past is mainly owed to large
amounts of labeled training data. However, the acquisition of labeled data for new tasks …

Uncertainty awareness is crucial to develop reliable machine learning models. In this work,
we propose the Natural Posterior Network (NatPN) for fast and high-quality uncertainty …

Recurrent Neural Networks (RNNs) are a class of machine learning algorithms used for
applications with time-series and sequential data. Recently, there has been a strong interest …

Various rice diseases threaten the growth of rice. It is of great importance to achieve the
rapid and accurate detection of rice diseases for precise disease prevention and control …