A Survey on Symmetrical Neural Network Architectures and Applications

Ilina, O. V.; Ziyadinov, Vadim; Klenov, N. V.; Терешонок, М. В.

doi:10.3390/sym14071391

Cited by 15 publications

(6 citation statements)

References 85 publications

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“…The use of symmetry in the morphology of a neural network is an effective way to improve its ability in order to recognise patterns and detect patterns in image databases [9,10].…”

Section: Related Workmentioning

confidence: 99%

Influence of the Symmetry Neural Network Morphology on the Mine Detection Metric

Peleshchak,

Lytvyn,

Nazarkevych

et al. 2024

Symmetry

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Presently, active detectors are widely used to detect mines, providing high accuracy. However, the principle of the operation of active detectors can lead to the explosion of hidden mines. The novelty of this work is the development of the morphology of a neural network for the classification of mines made of different materials (metallic, semi-metallic, plastic) with high accuracy (99.23%), based on a vector of input features with the following components: the value of the output voltage of the FLC-100 magnetic field sensor, which measures magnetic field anomalies in the vicinity of mines with an accuracy of 10−10–10−4 Tesla; six different soil types, depending on the humidity; and the height at which the magnetic field sensor is located above the mine. Due to the fact that mines, when made of different materials (metallic, semi-metallic, plastic), have different magnetic properties, the neural network method of mine classification, based on the sensor data regarding anomalies of the magnetic field in the vicinity of mines, allows the classification of mines made of different materials. The accuracy of mine classification was assessed with two-layer and three-layer neural networks on various metrics (confusion matrix, ROC curves, accuracy–loss curves), using ADAM, RMSprop, and SGD optimisers, and analyses and comparisons were then carried out. The impact of asymmetry in the neuron number and the types of activation functions in the first and second hidden layers on the values of the accuracy and loss metrics was studied. In particular, it was established that the asymmetry of the number of neurons in the first and second hidden layers relative to the plane of symmetry between the hidden layers has a significant effect on the accuracy of the model (decrease in accuracy by 25%), while the loss function, when the symmetry of the neurons number in the hidden layers is violated, increases to a maximum of 50%.

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“…The use of symmetry in the morphology of a neural network is an effective way to improve its ability in order to recognise patterns and detect patterns in image databases [9,10].…”

Section: Related Workmentioning

confidence: 99%

Influence of the Symmetry Neural Network Morphology on the Mine Detection Metric

Peleshchak,

Lytvyn,

Nazarkevych

et al. 2024

Symmetry

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“…Deep learning is a subfield of machine learning that deals with the construction and training of neural networks composed of multiple layers, enabling the learning of hierarchical representations [33][34][35]. Given an input x ∈ X , where X denotes the input space, a deep learning model N with parameters Θ maps the input to an output ŷ ∈ Y, where Y denotes the output space.…”

Section: Deep Learningmentioning

confidence: 99%

Multi-Task Deep Learning Games: Investigating Nash Equilibria and Convergence Properties

Lee

2023

Axioms

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This paper conducts a rigorous game-theoretic analysis on multi-task deep learning, providing mathematical insights into the dynamics and interactions of tasks within these models. Multi-task deep learning has attracted significant attention in recent years due to its ability to leverage shared representations across multiple correlated tasks, leading to improved generalization and reduced training time. However, understanding and examining the interactions between tasks within a multi-task deep learning system poses a considerable challenge. In this paper, we present a game-theoretic investigation of multi-task deep learning, focusing on the existence and convergence of Nash equilibria. Game theory provides a suitable framework for modeling the interactions among various tasks in a multi-task deep learning system, as it captures the strategic behavior of learning agents sharing a common set of parameters. Our primary contributions include: casting the multi-task deep learning problem as a game where each task acts as a player aiming to minimize its task-specific loss function; introducing the notion of a Nash equilibrium for the multi-task deep learning game; demonstrating the existence of at least one Nash equilibrium under specific convexity and Lipschitz continuity assumptions for the loss functions; examining the convergence characteristics of the Nash equilibrium; and providing a comprehensive analysis of the implications and limitations of our theoretical findings. We also discuss potential extensions and directions for future research in the multi-task deep learning landscape.

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“…A distinctive feature in the current era of information technology evolution is the widespread development and implementation of artificial intelligence (AI) [1][2][3][4][5][6]. In order to effectively solve a number of tasks, specialised hardware implementation of AI systems is required [7,8].…”

Section: Introductionmentioning

confidence: 99%

Optimisation Challenge for a Superconducting Adiabatic Neural Network That Implements XOR and OR Boolean Functions

Pashin,

Bastrakova,

Rybin

et al. 2024

Nanomaterials

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In this article, we consider designs of simple analog artificial neural networks based on adiabatic Josephson cells with a sigmoid activation function. A new approach based on the gradient descent method is developed to adjust the circuit parameters, allowing efficient signal transmission between the network layers. The proposed solution is demonstrated on the example of a system that implements XOR and OR logical operations.

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A Survey on Symmetrical Neural Network Architectures and Applications

Cited by 15 publications

References 85 publications

Influence of the Symmetry Neural Network Morphology on the Mine Detection Metric

Influence of the Symmetry Neural Network Morphology on the Mine Detection Metric

Multi-Task Deep Learning Games: Investigating Nash Equilibria and Convergence Properties

Optimisation Challenge for a Superconducting Adiabatic Neural Network That Implements XOR and OR Boolean Functions

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