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CA-NN: a cellular automata neural network for handwritten pattern recognition
Convolutional neural networks (CNNs) are best suited for image data. The most important layer in CNNs is the convolution layer. In this paper,...
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Multi-objective Evolutionary Neural Architecture Search for Recurrent Neural Networks
Artificial neural network (NN) architecture design is a nontrivial and time-consuming task that often requires a high level of human expertise....
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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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Estimation of extreme quantiles from heavy-tailed distributions with neural networks
We propose new parametrizations for neural networks in order to estimate extreme quantiles in both non-conditional and conditional heavy-tailed...
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Towards Network Implementation of CBR: Case Study of a Neural Network K-NN Algorithm
Recent research brings the strengths of neural networks to bear on CBR tasks such as similarity assessment and case adaptation. This paper further...
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Quantization of Neural Networks
Quantization has emerged as a highly successful strategy for both training and inference of neural networks (NN). While the challenges of numerical...
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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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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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Predicting triplanar and bidirectional movements for a transtibial prosthesis for rehabilitation using intelligent neural networks
In this study, artificial neural networks (NN) are applied to the design of a transtibial prosthesis to adapt triplanar and bidirectional movements...
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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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EEF1-NN: Efficient and EF1 Allocations Through Neural Networks
Our goal is to allocate items to maximize efficiency while ensuring fairness. Since Envy-freeness may not always exist, we consider the relaxed...
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State estimation in coupled neural networks with delays via changeable pinning control
This work investigates the state estimation problem in a network of diffusively coupled neural networks (NNs), where each NN has delayed dynamics,...
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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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Neural Networks
Logistic regression is an effective, but elementary technique. This chapter describes how we can extend it by stacking more layers and functions, and...
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A Sound Abstraction Method Towards Efficient Neural Networks Verification
With the increasing application of neural networks (NN) in safety-critical systems, the (formal) verification of NN is becoming more than essential....
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Supervised Learning Neural Networks
In this chapter, we describe the basic concepts, notation, and basic learning algorithms for supervised neural networks that will be of great use for...
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MultiPINN: multi-head enriched physics-informed neural networks for differential equations solving
Recently, the physics-informed neural network (PINN) has attracted much attention in solving partial differential equations (PDEs). The success is...
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Regenerating Networked Systems’ Monitoring Traces Using Neural Networks
Monitoring main entities in distributed systems is important for research, development, and innovation activities involving those systems (offline...
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Towards robust neural networks via a global and monotonically decreasing robustness training strategy
Robustness of deep neural networks (DNNs) has caused great concerns in the academic and industrial communities, especially in safety-critical...
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Neural networks for scalar input and functional output
The regression of a functional response on a set of scalar predictors can be a challenging task, especially if there is a large number of predictors,...
