This review covers computer-assisted analysis of images in the field of medical imaging.
Recent advances in machine learning, especially with regard to deep learning, are hel** …

Abstract Big Data Analytics and Deep Learning are two high-focus of data science. Big Data
has become important as many organizations both public and private have been collecting …

TensorFlow is a machine learning system that operates at large scale and in heterogeneous
environments. Tensor-Flow uses dataflow graphs to represent computation, shared state …

An introduction to a broad range of topics in deep learning, covering mathematical and
conceptual background, deep learning techniques used in industry, and research …

Deep learning takes advantage of large datasets and computationally efficient training
algorithms to outperform other approaches at various machine learning tasks. However …

Learning sophisticated feature interactions behind user behaviors is critical in maximizing
CTR for recommender systems. Despite great progress, existing methods seem to have a …

Despite their great success, there is still no comprehensive theoretical understanding of
learning with Deep Neural Networks (DNNs) or their inner organization. Previous work …

The success of machine learning algorithms generally depends on data representation, and
we hypothesize that this is because different representations can entangle and hide more or …

Secondary use of electronic health records (EHRs) promises to advance clinical research
and better inform clinical decision making. Challenges in summarizing and representing …

We explore an original strategy for building deep networks, based on stacking layers of
denoising autoencoders which are trained locally to denoise corrupted versions of their …