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Neural lasso: a unifying approach of lasso and neural networks
In recent years, there has been a growing interest in establishing bridges between statistics and neural networks. This article focuses on the...
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Explainable generalized additive neural networks with independent neural network training
Neural Networks are one of the most popular methods nowadays given their high performance on diverse tasks, such as computer vision, anomaly...
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Use of artificial neural networks in architecture: determining the architectural style of a building with a convolutional neural networks
The discussion of "can machines think?" which started with the invention of the modern computer, brought along the question of "can machines design?"...
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Generating adaptation rule-specific neural networks
There have been a number of approaches to employ neural networks in self-adaptive systems; in many cases, generic neural networks and deep learning...
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Riesz Networks: Scale-Invariant Neural Networks in a Single Forward Pass
Scale invariance of an algorithm refers to its ability to treat objects equally independently of their size. For neural networks, scale invariance is...
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Modular Neural Networks
We describe in this chapter the basic concepts, theory and algorithms of modular and ensemble neural networks. We will also give particular attention...
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Merging of Neural Networks
We propose a simple scheme for merging two neural networks trained with different starting initialization into a single one with the same size as the...
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Neural Networks with Dependent Inputs
Neural networks and decision tree algorithms are essential tools in machine learning and data science. They deal with patterns among inputs and...
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Pseudo datasets explain artificial neural networks
Machine learning enhances predictive ability in various research compared to conventional statistical approaches. However, the advantage of the...
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A survey of uncertainty in deep neural networks
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...
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Siamese neural networks in recommendation
Recommender systems are widely adopted as an increasing research and development area, since they provide users with diverse and useful information...
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Learning to rank influential nodes in complex networks via convolutional neural networks
AbstractIdentifying influential nodes is crucial for enhancing information diffusion in complex networks. Several approaches have been proposed to...
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Neural Networks
Though neural networks have been around for many years, because of technological advancement and computational power, they have gained popularity...
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DyPipe: A Holistic Approach to Accelerating Dynamic Neural Networks with Dynamic Pipelining
Dynamic neural network (NN) techniques are increasingly important because they facilitate deep learning techniques with more complex network...
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Contradiction neutralization for interpreting multi-layered neural networks
The present paper aims to propose a new method for neutralizing contradictions in neural networks. Neural networks exhibit numerous contradictions in...
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Unsupervised Learning Neural Networks
This chapter introduces the basic concepts and notation of unsupervised learning neural networks. Unsupervised networks are useful for analyzing data...
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Shallow quantum neural networks (SQNNs) with application to crack identification
Quantum neural networks have been explored in a number of tasks including image recognition. Most of the approaches involve using quantum gates in...
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An Intrusion Detection System Using Extended Kalman Filter and Neural Networks for IoT Networks
The unparalleled growth of the Internet of Things (IoT) is introducing a new paradigm shift in networking technology. By connecting everyday devices...
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Boosting deep neural networks with geometrical prior knowledge: a survey
Deep neural networks achieve state-of-the-art results in many different problem settings by exploiting vast amounts of training data. However,...
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Evolutionary spiking neural networks: a survey
Spiking neural networks (SNNs) are gaining increasing attention as potential computationally efficient alternatives to traditional artificial neural...
