When testing conditions differ from those represented in training data, so-called out-of-
distribution (OOD) inputs can mar the reliability of learned components in the modern robot …

The behavior of neural networks (NNs) on previously unseen types of data (out-of-
distribution or OOD) is typically unpredictable. This can be dangerous if the network's output …

Brain tumor segmentation (BTS) has been studied from handcrafted engineered features to
conventional machine learning (ML) methods, followed by the cutting-edge deep learning …

In this paper, we propose a method for denoising experimental density matrices that
combines standard quantum state tomography with an attention-based neural network …

Machine Learning (ML) models, such as deep neural networks, are widely applied in
autonomous systems to perform complex perception tasks. New dependability challenges …

Recent research has drawn attention to the ambiguity surrounding the definition and
learnability of Out-of-Distribution recognition. Although the original problem remains …

Detecting out-of-distribution (OOD) samples is a problem of practical importance for a
reliable use of deep neural networks (DNNs) in production settings. The corollary to this …

Trajectory prediction is one of the key components of the autonomous driving software stack.
Accurate prediction for the future movement of surrounding traffic participants is an important …

Accurate trajectory prediction is vital for various applications, including autonomous
vehicles. However, the complexity and limited transparency of many prediction algorithms …

Reliable and robust evaluation methods are a necessary first step towards developing
machine learning models that are themselves robust and reliable. Unfortunately, current …