Spiking neural networks (SNNs) process time-series data via internal event-driven neural
dynamics. The energy consumption of an SNN depends on the number of spikes exchanged …

When used in complex engineered systems, such as communication networks, artificial
intelligence (AI) models should be not only as accurate as possible, but also well calibrated …

Conformal risk control (CRC) is a recently proposed technique that applies post-hoc to a
conventional point predictor to provide calibration guarantees. Generalizing conformal …

The dynamic scheduling of ultra-reliable and low-latency communication traffic (URLLC) in
the uplink can significantly enhance the efficiency of coexisting services, such as enhanced …

The safe integration of machine learning modules in decision-making processes hinges on
their ability to quantify uncertainty. A popular technique to achieve this goal is conformal …

Semantic and goal-oriented (SGO) communication is an emerging technology that only
transmits significant information for a given task. Semantic communication encounters many …

Deep learning models, including modern systems like large language models, are well
known to offer unreliable estimates of the uncertainty of their decisions. In order to improve …

Accurate uncertainty quantification in graph neural networks (GNNs) is essential, especially
in high-stakes domains where GNNs are frequently employed. Conformal prediction (CP) …

As a category of recurrent neural networks, echo state networks (ESNs) have been the topic
of in-depth investigations and extensive applications in a diverse array of fields, with …

Machine learning and genomic medicine are the mainstays of research in delivering
personalized healthcare services for disease diagnosis, risk stratification, tailored treatment …