We present a research study aimed at testing of applicability of machine learn-ing
techniques for permeability prediction. We prepare a training set containing. 3D scans of …

In response to the growing importance of geospatial data, its analysis including semantic
segmentation becomes an increasingly popular task in computer vision today. Convolutional …

The general de novo solution of the crystallographic phase problem is difficult and only
possible under certain conditions. This paper develops an initial pathway to a deep learning …

In the field of psychoneurology, analysis of neuroimaging data aimed at extracting distinctive
patterns of pathologies, such as epilepsy and depression, is well known to represent a …

When major disaster occurs the questions are raised how to estimate the damage in time to
support the decision making process and relief efforts by local authorities or humanitarian …

Learning robust and discriminative representations is essential for 3D object retrieval. In this
paper, we present an improved Multi-view Convolutional Neural Network (MVCNN) for view …

Deep learning convolution neural networks have proved to be a powerful tool for MRI
analysis. In current work, we explore the potential of the deformable convolution deep neural …

In many branches of earth sciences, the problem of rock study on the micro-level arises.
However, a significant number of representative samples is not always feasible. Thus the …

We develop a data-driven model, introducing recent advances in machine learning to
reservoir simulation. We use a conventional reservoir modeling tool to generate training set …

As machine learning continues to gain momentum in the neuroscience community, we
witness the emergence of novel applications such as diagnostics, characterization, and …