Despite their success, deep networks are used as black-box models with outputs that are not
easily explainable during the learning and the prediction phases. This lack of interpretability …

The field of natural language processing has seen impressive progress in recent years, with
neural network models replacing many of the traditional systems. A plethora of new models …

Recent years have seen important advances in the quality of state-of-the-art models, but this
has come at the expense of models becoming less interpretable. This survey presents an …

Attention mechanisms play a central role in NLP systems, especially within recurrent neural
network (RNN) models. Recently, there has been increasing interest in whether or not the …

In the past few decades, artificial intelligence (AI) technology has experienced swift
developments, changing everyone's daily life and profoundly altering the course of human …

Attention mechanisms have seen wide adoption in neural NLP models. In addition to
improving predictive performance, these are often touted as affording transparency: models …

We present a labeled large-scale, high resolution chest x-ray dataset for the automated
exploration of medical images along with their associated reports. This dataset includes …

Objective To conduct a systematic review of deep learning models for electronic health
record (EHR) data, and illustrate various deep learning architectures for analyzing different …

Motivation Graph embedding learning that aims to automatically learn low-dimensional
node representations, has drawn increasing attention in recent years. To date, most recent …

Word embeddings are effective intermediate representations for capturing semantic
regularities between words, when learning the representations of text sequences. We …