Deep learning algorithms, such as convolutional neural networks (CNNs), have achieved remarkable results on a variety of tasks, including those that involve recognizing specific people or objects in images. A task that computer scientists have often tried to tackle...
Virtual assistants and robots are becoming increasingly sophisticated, interactive and human-like. To fully replicate human communication, however, artificial intelligence (AI) agents should not only be able to determine what users are saying and produce adequate...
Over the past decade, researchers have developed a growing number of deep neural networks that can be trained to complete a variety of tasks, including recognizing people or objects in images. While many of these computational techniques have achieved remarkable...
Predicting the properties of complex quantum systems is a crucial step in the development of advanced quantum technologies. While research teams worldwide have already devised a number of techniques to study the characteristics of quantum systems, most of these have...
Over the past few years, researchers have been trying to apply quantum physics theory to a variety of fields, including robotics, biology and cognitive science. Computational techniques that draw inspiration from quantum systems, also known as quantum-like (QL)...
Over the past decade or so, researchers have been developing increasingly advanced artificial intelligence (AI) systems for a wide range of applications. This includes computational techniques that can interact with humans, analyze large quantities of data, identify...
Hyperdimensional computing (HDC) is an emerging computing approach inspired by patterns of neural activity in the human brain. This unique type of computing can allow artificial intelligence systems to retain memories and process new information based on data or...
Deep neural networks (DNNs) have so far proved to be highly promising for a wide range of applications, including image and audio classification. Nonetheless, their performance heavily relies on the amount of data used to train them, and large datasets are not always...
Network science is an academic field that aims to unveil the structure and dynamics behind networks, such as telecommunication, computer, biological and social networks. One of the fundamental problems that network scientists have been trying to solve in recent years...
In recent years, machine learning algorithms have attained remarkable results in a variety of tasks, including the classification of both images and audio files. A class of algorithms that has proven to be particularly promising are deep neural networks (DNNs) that...