Academic Articles & Research Papers

A survey of uncertainty in deep neural networks

Abstract Over the last decade, neural networks have reached almost every field of science and become a crucial part of various real world applications. Due to the increasing spread, confidence in neural network predictions has become more a...

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...

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.,...

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...

Emerging Principles Governing The Operation Of Neural Networks

ical, and computational techniques have provided the tools to begin uncovering concepts underlying neural network function. This review is a summary of insights into the organization and operation of neural circuits acquired through applica...

Neural networks for classification: a survey

Classification is one of the most active research and application areas of neural networks. The literature is vast and growing. This paper summarizes some of the most important developments in neural network classification research. Specifi...

Photonic Neural Networks: A Survey

Photonic solutions are today a mature industrial reality concerning high speed, high throughput data communication and switching infrastructures. It is still a matter of investigation to what extent photonics will play a role in next-genera...

Approximation theory of the MLP model in neural networks

In this survey we discuss various approximation-theoretic problems that arise in the multilayer feedforward perceptron (MLP) model in neural networks. The MLP model is one of the more popular and practical of the many neural network models....

Theory of the backpropagation neural network

The author presents a survey of the basic theory of the backpropagation neural network architecture covering architectural design, performance measurement, function approximation capability, and learning. The survey includes previously know...

On Interpretability of Artificial Neural Networks: A Survey

Deep learning as represented by the artificial deep neural networks (DNNs) has achieved great success recently in many important areas that deal with text, images, videos, graphs, and so on. However, the black-box nature of DNNs has become ...

Deep Learning for Instance Retrieval: A Survey

In recent years a vast amount of visual content has been generated and shared from many fields, such as social media platforms, medical imaging, and robotics. This abundance of content creation and sharing has introduced new challenges, par...

Federated Machine Learning

Today’s artificial intelligence still faces two major challenges. One is that, in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security. We propose a possible solution to t...

On Formalizing Fairness in Prediction with Machine Learning

Machine learning algorithms for prediction are increasingly being used in critical decisions affecting human lives. Various fairness formalizations, with no firm consensus yet, are employed to prevent such algorithms from systematically dis...

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...

Search-based software engineering

In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. ...

Perspectives in Software Engineering

Software engineering refers to the process of creating software systems.It applies loosely to techniques which reduce high software cost and complexity while increasing reliability and mochfiability.This paper outlines the procedures used i...

Multi-source environmental raster dataset of FAO desert locust survey records in Ethiopia (2015–2023) for machine-learning prediction of locust presence.

ECG interpretation in athletes by expert sports cardiologists and nonspecialist medical professionals across Europe: a survey from the European association of preventive cardiology.

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...