Despite its astounding success in learning deeper multi-dimensional data, the performance
of deep learning declines on new unseen tasks mainly due to its focus on same-distribution …

The ability to adapt quickly to a new task or data distribution based on only a few examples
is a challenge in AI and highly relevant for various domains. In digital pathology, slight …

Simple Summary A lower tumor–stroma ratio within a tumor correlates with a poorer
outcome, ie, with a higher risk of death. The assessment of this ratio by humans is prone to …

Word embeddings are trained to predict word cooccurrence statistics, which leads them to
possess different lexical properties (syntactic, semantic, etc.) depending on the notion of …

A vast multitude of tasks in histopathology could potentially benefit from the support of
artificial intelligence (AI). Many examples have been shown in the literature and first …

The exponential growth of digital image data has given rise to the need of efficient content
management and retrieval tools. Currently, there is a lack of tools for processing the …

Uncontextualized word embeddings are reliable feature representations of words used to
obtain high quality results for various NLP applications. Given the historical success of word …

Advanced mobility research centers capture large amounts of data from ground vehicle
systems during development and experimentation in both manned and autonomous …

This paper proposes inverse feature learning (IFL) as a novel supervised feature learning
technique that learns a set of high-level features for classification based on an error …

Cardiovascular disease (CVD) is the first rank of mortality in the world. The most prevalent
cardiac conditions are arrhythmias, a class of impulse generation or conduction disorders …