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Optimization-inspired manual architecture design and neural architecture search
Neural architecture has been a research focus in recent years due to its importance in deciding the performance of deep networks. Representative ones...
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DMF-TONN: Direct Mesh-free Topology Optimization using Neural Networks
We propose a direct mesh-free method for performing topology optimization by integrating a density field approximation neural network with a...
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COOT optimization algorithm on training artificial neural networks
In recent years, significant advancements have been made in artificial neural network models and they have been applied to a variety of real-world...
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Graph neural networks for deep portfolio optimization
There is extensive literature dating back to the Markowitz model on portfolio optimization. Recently, with the introduction of deep models in...
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English letter recognition based on adaptive optimization spiking neural P systems
The novel dynamic guider algorithm within the adaptive optimization spiking neural P system (AOSNPS) framework is employed to create an innovative...
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Hierarchical multi-scale parametric optimization of deep neural networks
Traditionally, sensitivity analysis has been utilized to determine the importance of input variables to a deep neural network (DNN). However, the...
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Neural networks as an approximator for a family of optimization algorithm solutions for online applications
In this paper, we propose a sufficient condition at which a neural network can approximate a set of optimization algorithm solutions; we establish...
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Air Pollutants Classification Using Optimized Neural Network Based on War Strategy Optimization Algorithm
AbstractAir quality prediction is considered one of complex problems. This is due to volatility, dynamic nature, and high variability in space and...
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Image neural style transfer combining global and local optimization
In order to avoid the shortcomings of a single optimization method, improve the effect of style transfer, and control the occurrence of artifacts,...
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Optimized Convolutional Neural Network Using Hierarchical Particle Swarm Optimization for Sensor Based Human Activity Recognition
Hyperparameter optimization poses a significant challenge when develo** deep neural networks. Building a convolutional neural network (CNN) for...
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Hyperparameter optimization of pre-trained convolutional neural networks using adolescent identity search algorithm
Convolutional neural networks (CNNs) are widely used deep learning (DL) models for image classification. The selected hyperparameters for training...
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Neural coordination through spider monkey optimization-guided weight synchronization
This paper proposes a Spider Monkey-based neural weight optimization approach for quicker neural synchronization. To exchange the session key across...
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Differentiable Discrete Optimization Using Dataless Neural Networks
The area of combinatorial optimization is characterized by the search for optimal combinations of discrete variables that satisfy some set of...
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Ebola optimization based spiking neural network for automatic hate speech recognition
In this paper, efficient machine learning technique is introduced to develop efficient machine learning model for hate speech recognition from the...
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Cancer gene selection with adaptive optimization spiking neural P systems and hybrid classifiers
The selection of disease-causing genes from gene expression and methylation data is a great benefit for cancer diagnosis and treatment, but it also...
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DWSR: an architecture optimization framework for adaptive super-resolution neural networks based on meta-heuristics
Despite recent advancements in super-resolution neural network optimization, a fundamental challenge remains unresolved: as the number of parameters...
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Multi-learning rate optimization spiking neural P systems for solving the discrete optimization problems
To further improve the performance of optimization spiking neural P system (OSNPS), a multi-learning rate optimization spiking neural P system...
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Combination of Optimization Methods in a Multistage Approach for a Deep Neural Network Model
This paper on gradient descent (GD) lies at the heart and soul of neural networks. The development of GD optimization algorithms significantly sped...
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Hyperparameter optimization of neural networks based on Q-learning
Machine learning algorithms are sensitive to hyperparameters, and hyperparameter optimization techniques are often computationally expensive,...
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Optimization of Artificial Neural Networks using Wavelet Transforms
AbstractThe article presents the artificial neural networks performance optimization using wavelet transform. The existing approaches of wavelet...
