In recent years, deep artificial neural networks (including recurrent ones) have won
numerous contests in pattern recognition and machine learning. This historical survey …

Artificial intelligence (AI) is booming. Among various AI approaches, generative models
have received much attention in recent years. Inspired by these successes, researchers are …

We have developed a deep generative model, generative tensorial reinforcement learning
(GENTRL), for de novo small-molecule design. GENTRL optimizes synthetic feasibility …

The self-organized map, an architecture suggested for artificial neural networks, is explained
by presenting simulation experiments and practical applications. The self-organizing map …

This paper describes a data organization system and genuine content-addressable memory
called the WEBSOM. It is a two-layer self-organizing map (SOM) architecture where …

If we take an existing supervised NLP system, a simple and general way to improve
accuracy is to use unsupervised word representations as extra word features. We evaluate …

Though mathematical ideas underpin the study of neural networks, the author presents the
fundamentals without the full mathematical apparatus. All aspects of the field are tackled …

The self-organizing map (SOM) is an automatic data-analysis method. It is widely applied to
clustering problems and data exploration in industry, finance, natural sciences, and …

Advances in data collection and storage capabilities during the past decades have led to an
information overload in most sciences. Researchers working in domains as diverse as …

We present a new self-organizing neural network model that has two variants. The first
variant performs unsupervised learning and can be used for data visualization, clustering …