Academic Articles & Research Papers

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,...

Natural Language Processing for Requirements Engineering: The Best Is Yet to Come

As part of the growing interest in natural language processing for requirements engineering (RE), RE researchers, computational linguists, and industry practitioners met at the First Workshop on Natural Language Processing for Requirements ...

A literature survey of active machine learning in the context of natural language processing

Active learning is a supervised machine learning technique in which the learner is in control of the data used for learning. That control is utilized by the learner to ask an oracle, typically a human with extensive knowledge of the domain ...

Circuit Complexity and Neural Networks

Neural networks usually work adequately on small problems but can run into trouble when they are scaled up to problems involving large amounts of input data. Circuit Complexity and Neural Networks addresses the important question of how wel...

Pattern Recognition with Neural Networks in C++

From the Publisher: The addition of artificial neural network computing to traditionalpattern recognition has given rise to a new, different, and more powerful methodology that is presented in this interesting book. This is a practical gui...

State-of-the-art in artificial neural network applications: A survey

This is a survey of neural network applications in the real-world scenario. It provides a taxonomy of artificial neural networks (ANNs) and furnish the reader with knowledge of current and emerging trends in ANN applications research and ar...

Towards Evaluating the Robustness of Neural Networks

Neural networks provide state-of-the-art results for most machine learning tasks. Unfortunately, neural networks are vulnerable to adversarial examples: given an input x and any target classification t, it is possible to find a new input x'...

Quantum Entanglement in Neural Network States

Machine learning has recently gained attention as a possible way to understand phase transitions in many-body quantum systems. A new entanglement analysis reveals crucial properties of the data structures that encode quantum states in a neu...

Fundamentals of neural networks: architectures, algorithms, and applications

1. Introduction. 2. Simple Neural Nets for Pattern Classification. 3. Pattern Association. 4. Neural Networks Based on Competition. 5. Adaptive Resonance Theory. 6. Backpropagation Neural Net. 7. A Sampler of Other Neural Nets. Glossary. Re...

Neural Networks for Chemical Engineers

Part 1 Theoretical issues: an introduction to artificial neural networks, D.T. Pham unsupervised neural learning, E. Oja back-propagation and its variations, D. Tsaptsinos the general approximation problem for feed-forward neural networks, ...

Neural Networks

Neural Networks, Second Edition provides a complete introduction to neural networks. It describes what they are, what they can do, and how they do it. While some scientific background is assumed, the reader is not expected to have any prior...

Applications of Neural Networks in Electromagnetics

From the Book: Since the early 1990s, a plethora of electromagnetic problems have been tackled using neural networks, some more successfully than others. Because neural networks and electromagnetics are two different scientific fields, not...

Artificial Intelligence

Artificial Intelligence (AI) refers to a set of technologies through which "intelligent machines are gaining the ability to learn, improve and make calculated decisions in ways that enable them to perform tasks previously thought to rely so...

Artificial Intelligence: Principles and Applications

Part 1. Principles of artificial intelligence, John Campbell. Part 2 Tools and techniques: artificial intelligence programming environments and the POPLOG system, John Gibson LISP, lists and pattern-matching, Tony Hasemer. Part 3 Applicatio...

Metamodelling for software engineering

This book focuses on metamodelling as a discipline, exploring its foundations, techniques and results. It presents a comprehensive metamodel that covers process, product and quality issues under a common framework. Issues covered include: *...

Models of motivation in software engineering

Software engineering economics

The field of software economics seeks to develop technical theories, guidelines, and practices of software development based on sound, established, and emerging models of value and value-creation---adapted to the domain of software developm...

The Messy Nature of Fiber Spectra: Star–Quasar Pairs Masquerading as Dual Type 1 AGNs

Theoretical studies predict that the most significant growth of supermassive black holes (SMBHs) occurs in late-stage mergers, coinciding with the manifestation of dual active galactic nuclei (AGNs), and both major and minor mergers are exp...

Multiwavelength Study of Five Distant Transient Gamma-Ray AGNs with z > 1

The multiwavelength emissions, especially gamma-rays, of active galactic nuclei (AGNs) are essential for studying the physical properties of jets emanating from supermassive black holes at galaxy centers. However, for high-redshift AGNs, it...

String with deformed dispersion relation

Abstract We study string theory with momentum living on de Sitter space. We argue that consistency of the theory implies that the global momentum can be defined on de Sitter space. Then we perform careful canonical analysis and we we also s...