Earth system models (ESMs) are our main tools for quantifying the physical state of the Earth
and predicting how it might change in the future under ongoing anthropogenic forcing. In …

Functional correspondences between deep convolutional neural networks (DCNNs) and the
mammalian visual system support a hierarchical account in which successive stages of …

Recent advancements in machine learning and deep learning (DL) based neural decoders
have significantly improved decoding capabilities using scalp electroencephalography …

Deep neural networks (DNNs) have become increasingly successful in applications from
biology to cosmology to social science. Trained DNNs, moreover, correspond to models that …

The term robustness is ubiquitous in modern Machine Learning (ML). However, its meaning
varies depending on context and community. Researchers either focus on narrow technical …

Unsupervised learning algorithms are widely used for many important statistical tasks with
numerous applications in science and industry. Yet despite their prevalence, they have …

Text-guided image generation models can be prompted to generate images using nonce
words adversarially designed to robustly evoke specific visual concepts. Two approaches for …

Testing and evaluation is a crucial step in the development and deployment of connected
and automated vehicles (CAVs). To comprehensively evaluate the performance of CAVs, it …

Despite progress in the field, significant parts of current XAI research are still not on solid
conceptual, ethical, or methodological grounds. Unfortunately, these unfounded parts are …

Evaluating the decision-making system is indispensable in developing autonomous
vehicles, while realistic and challenging safety-critical test scenarios play a crucial role …