Many large-scale knowledge bases simultaneously represent two views of knowledge graphs (KGs): an ontology view for abstract and commonsense concepts, and an instance view for specific entities that are instantiated from ontological concepts. Existing KG embedding model...

International audience; Young children can exploit the syntactic context of a novel word to narrow down its probable meaning. But how do they learn which contexts are linked to which semantic features in the first place? We investigate if 3‐ to 4‐year‐old children (n = ...

<jats:p>Domain adaptation is an important but challenging task. Most of the existing domain adaptation methods struggle to extract the domain-invariant representation on the feature space with entangling domain information and semantic information. Different from previo...

Graphical abstract

There has been an increasing interest in examining organisational principles of the cerebral cortex (and subcortical regions) using different MRI features such as structural or functional connectivity. Despite the widespread interest, introductory tutorials on the under...

Coordinate-based meta-analyses (CBMA) allow researchers to combine the results from multiple fMRI experiments with the goal of obtaining results that are more likely to generalise. However, the interpretation of CBMA findings can be impaired by the file drawer problem, ...

<jats:title>Abstract</jats:title> <jats:sec> <jats:title>Objective</jats:title> <jats:p>We sought to demonstrate the feasibility of utilizing deep learning models to extract safety signals related to the use of dietary supplements (DSs) in clinical text.</jats:p> </jats...

Comment: Medical Image Computing and Computer Assisted Interventions (MICCAI) 2020 to be presented at DART 2020. Supplementary material and link to code included

Comment: International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2020)

We study the settings for which deep contextual embeddings (e.g., BERT) give large improvements in performance relative to classic pretrained embeddings (e.g., GloVe), and an even simpler baseline---random word embeddings---focusing on the impact of the training set siz...