DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep
learning in the field of earth sciences, from four leading voices Deep learning is a …

In recent years, the enormous demand for computing resources resulting from massive data
and complex network models has become the limitation of deep learning. In the large-scale …

Deep feed-forward convolutional neural networks (CNNs) have become ubiquitous in
virtually all machine learning and computer vision challenges; however, advancements in …

We consider deep neural networks with rectifier activations and max-pooling from a signal
representation perspective. In this view, such representations mark the transition from using …

The constraint-based approach has been proven useful for inducing bilingual lexicons for
closely-related low-resource languages. When we want to create multiple bilingual …

This paper addresses the land cover classification task for remote sensing images by deep
self-taught learning. Our self-taught learning approach learns suitable feature …

Object encoding and identification are crucial for many robotic tasks such as autonomous
exploration and semantic relocalization. Existing works heavily rely on the tracking of …

The model and training method of multilayer feature extractor and decision rules for a
malware traffic detector is proposed. The feature extractor model is based on a convolutional …

Model-based on multilayer convolutional sparse coding feature extractor and information-
extreme decision rules for malware traffic detection is presented in the paper. Growing …

Мета роботи–підвищення функціональної ефективності бортової системи безпілотного
літального апарату, що здійснює у автономному режимі локальну навігацію та …