With recent developments, the performance of automotive radar has improved significantly.
The next generation of 4D radar can achieve imaging capability in the form of high …

Pharmacometrics is a multidisciplinary field utilizing mathematical models of physiology,
pharmacology, and disease to describe and quantify the interactions between medication …

Web-crawled datasets have enabled remarkable generalization capabilities in recent image-
text models such as CLIP (Contrastive Language-Image pre-training) or Flamingo, but little …

Generating synthetic data through generative models is gaining interest in the ML
community and beyond, promising a future where datasets can be tailored to individual …

A robust approach for real-time analysis of the scanning transmission electron microscopy
(STEM) data streams, based on ensemble learning and iterative training (ELIT) of deep …

When faced with distribution shift at test time, deep neural networks often make inaccurate
predictions with unreliable uncertainty estimates. While improving the robustness of neural …

Deep learning is gaining increasing popularity for spatiotemporal forecasting. However,
prior works have mostly focused on point estimates without quantifying the uncertainty of the …

The central tenet of reinforcement learning (RL) is that agents seek to maximize the sum of
cumulative rewards. In contrast, active inference, an emerging framework within cognitive …

Over the last few decades, various methods have been proposed for estimating prediction
intervals in regression settings, including Bayesian methods, ensemble methods, direct …

This work presents a novel approach to address a challenging and still unsolved problem of
neural network based load forecasting systems, that despite the significant results reached …