Academic Articles & Research Papers

Deep quantum neural networks on a superconducting processor

Abstract Deep learning and quantum computing have achieved dramatic progresses in recent years. The interplay between these two fast-growing fields gives rise to a new research frontier of quantum machine learning. In this work, we report a...

Recurrent quantum embedding neural network and its application in vulnerability detection

Abstract In recent years, deep learning has been widely used in vulnerability detection with remarkable results. These studies often apply natural language processing (NLP) technologies due to the natural similarity between code and languag...

Forthcoming applications of quantum computing: peeking into the future

We all have been using classical computers for a long time. Quantum computing uses the phenomena of quantum mechanics like superposition and entanglement. Quantum computations can help achieve for the breakthroughs we have been looking for ...

Machine Learning and Deep Learning Methods for Cybersecurity

With the development of the Internet, cyber-attacks are changing rapidly and the cyber security situation is not optimistic. This survey report describes key literature surveys on machine learning (ML) and deep learning (DL) methods for net...

Harnessing Artificial Intelligence Capabilities to Improve Cybersecurity

Cybersecurity is a fast-evolving discipline that is always in the news over the last decade, as the number of threats rises and cybercriminals constantly endeavor to stay a step ahead of law enforcement. Over the years, although the origina...

Machine learning in cybersecurity: a comprehensive survey

Today’s world is highly network interconnected owing to the pervasiveness of small personal devices (e.g., smartphones) as well as large computing devices or services (e.g., cloud computing or online banking), and thereby each passing minut...

Deep Learning in Cybersecurity

In this article, a review and summarization of the emerging scientific approaches of deep learning (DL) on cybersecurity are provided, a structured and comprehensive overview of the various cyberattack detection methods is conducted, existi...

Application of deep learning to cybersecurity: A survey

Explainable artificial intelligence for cybersecurity: a literature survey

Abstract With the extensive application of deep learning (DL) algorithms in recent years, e.g., for detecting Android malware or vulnerable source code, artificial intelligence (AI) and machine learning (ML) are increasingly becoming essent...

THE ROLE OF NATURAL LANGUAGE PROCESSING

Natural Language Processing (NLP) is a rapidly evolving field in the intersection of computer science, artificial intelligence, and linguistics. This article provides an overview of NLP, tracing its historical development from early rule-ba...

Quantifying Uncertainties in Natural Language Processing Tasks

Reliable uncertainty quantification is a first step towards building explainable, transparent, and accountable artificial intelligent systems. Recent progress in Bayesian deep learning has made such quantification realizable. In this paper,...

Neural Network Methods for Natural Language Processing

Deep learning has attracted dramatic attention in recent years, both in academia and industry. The popular term deep learning generally refers to neural network methods. Indeed, many core ideas and methods were born years ago in the era of ...

Natural Language Processing

In the modern age of information and analytics, natural language processing (NLP) is one of the most important technologies out there. Making sense of complex structures in language and deriving insights and actions from it are crucial from...

GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing

We present GluonCV and GluonNLP, the deep learning toolkits for computer vision and natural language processing based on Apache MXNet (incubating). These toolkits provide state-of-the-art pre-trained models, training scripts, and training l...

Impact of Machine Learning in Natural Language Processing: A Review

The recent times have seen unprecedented growth in research and development in the field of Artificial Intelligence and its allied fields, subfields, like Machine Learning, Deep Learning and Natural Language Processing. The new found excite...

A Survey on Neural Network Interpretability

Along with the great success of deep neural networks, there is also growing concern about their black-box nature. The interpretability issue affects people's trust on deep learning systems. It is also related to many ethical problems, e.g.,...

A renaissance of neural networks in drug discovery

INTRODUCTION: Neural networks are becoming a very popular method for solving machine learning and artificial intelligence problems. The variety of neural network types and their application to drug discovery requires expert knowledge to cho...

Pointwise Convolutional Neural Networks

Deep learning with 3D data such as reconstructed point clouds and CAD models has received great research interests recently. However, the capability of using point clouds with convolutional neural network has been so far not fully explored....

Memristor devices for neural networks

Abstract Neural network technologies have taken center stage owing to their powerful computing capability for supporting deep learning in artificial intelligence. However, conventional synaptic devices such as SRAM and DRAM are not satisfac...

Hyperbolic Neural Networks

Hyperbolic spaces have recently gained momentum in the context of machine learning due to their high capacity and tree-likeliness properties. However, the representational power of hyperbolic geometry is not yet on par with Euclidean geomet...