Regularization, early-stopping and dreaming: A Hopfield-like setup to address generalization and overfitting

Neural Networks, Journal Year: 2024, Volume and Issue: 177, P. 106389 - 106389

Published: May 15, 2024

In this work we approach attractor neural networks from a machine learning perspective: look for optimal network parameters by applying gradient descent over regularized loss function. Within framework, the neuron-interaction matrices turn out to be class of which correspond Hebbian kernels revised reiterated unlearning protocol. Remarkably, extent such is proved related regularization hyperparameter function and training time. Thus, can design strategies avoid overfitting that are formulated in terms early-stopping tuning. The generalization capabilities these also investigated: analytical results obtained random synthetic datasets, next, emerging picture corroborated numerical experiments highlight existence several regimes (i.e., overfitting, failure success) as dataset varied.

Language: Английский

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Dual optimization approach in discrete Hopfield neural network

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 164, P. 111929 - 111929

Published: July 7, 2024

Language: Английский

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Integrated deep learning paradigm for document-based sentiment analysis

Journal of King Saud University - Computer and Information Sciences, Journal Year: 2023, Volume and Issue: 35(7), P. 101578 - 101578

Published: May 25, 2023

An integrated deep learning paradigm for the analysis of document-based sentiments is presented in this article. Generally, sentiment has enormous applications real world, particularly e-commerce and/or cloud computing-oriented businesses. Integrated paradigms seek to efficiently categorize polarity contextual into positive, negative, and neutral aid organizations making informed decisions. Nonetheless, sparsity text disambiguation natural languages make it relatively difficult existing methods provide precise identification extraction when subjected data. As a result, study introduces BERT-MultiLayered Convolutional Neural Network (B-MLCNN) as computationally viable paradigm. The B-MLCNN considers overall textual review single document classifies available sentiments. First, BERT pre-trained language model handles feature vector representation captures any global features. Further, multi-layered convolutional neural network (MLCNN) with different kernel dimensions extraction. A softmax function produces classification results. experimental setup based on IMDB movie reviews, 2002 2004 Amazon datasets achieved accuracies 95%, 88%, 95% respectively, which promises be efficient deploy practical applications.

Language: Английский

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Parameter identification of fuel cell using Repairable Grey Wolf Optimization algorithm

Applied Soft Computing, Journal Year: 2023, Volume and Issue: 147, P. 110791 - 110791

Published: Sept. 9, 2023

Language: Английский

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Interpretable machine learning assessment

Neurocomputing, Journal Year: 2023, Volume and Issue: 561, P. 126891 - 126891

Published: Oct. 5, 2023

Language: Английский

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Integrating heterogeneous structures and community semantics for unsupervised community detection in heterogeneous networks

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 238, P. 121821 - 121821

Published: Oct. 6, 2023

Language: Английский

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Fractional order-induced bifurcations in a delayed neural network with three neurons

Chaos An Interdisciplinary Journal of Nonlinear Science, Journal Year: 2023, Volume and Issue: 33(3)

Published: March 1, 2023

This paper reports the novel results on fractional order-induced bifurcation of a tri-neuron fractional-order neural network (FONN) with delays and instantaneous self-connections by intersection implicit function curves to solve critical point. Firstly, it considers distribution root characteristic equation in depth. Subsequently, views order as parameter establishes transversal condition stability interval. The main novelties this are systematically analyze concretely establish value through an array, which is idea value. derived exhibit that once greater than value, system will be smashed Hopf emerge. Ultimately, validity developed key fruits elucidated via two numerical experiments.

Language: Английский

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Breast cancer classification based on convolutional neural network and image fusion approaches using ultrasound images

Heliyon, Journal Year: 2023, Volume and Issue: 9(11), P. e22406 - e22406

Published: Nov. 1, 2023

Deep learning and image processing are used to classify segment breast tumor images, specifically in ultrasound (US) modalities, support clinical decisions improve healthcare quality. However, directly using US images can be challenging due noise diverse imaging modalities. In this study, we developed a three-step scheme involving speckle filtering block-matching three-dimensional technique, region of interest highlighting, RGB fusion. This method enhances the generalization deep-learning models achieves better performance. We deep model (VGG19) perform transfer on three datasets: BUSI (780 images), Dataset B (162 KAIMRC (5693 images). When tested datasets fivefold cross-validation mechanism, with proposed preprocessing step performed than without for each dataset. The approach improves performance cancer classification model. Multiple (private public) were generalize application.

Language: Английский

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MTS-PRO2SAT: Hybrid Mutation Tabu Search Algorithm in Optimizing Probabilistic 2 Satisfiability in Discrete Hopfield Neural Network

Mathematics, Journal Year: 2024, Volume and Issue: 12(5), P. 721 - 721

Published: Feb. 29, 2024

The primary objective of introducing metaheuristic algorithms into traditional systematic logic is to minimize the cost function. However, there a lack research on impact function under different proportions positive literals. In order fill in this gap and improve efficiency algorithm logic, we proposed based mutation tabu search embedded it probabilistic satisfiability discrete Hopfield neural networks. Based algorithm, operators genetic were combined its global ability during learning phase ensure that converged zero at Additionally, further optimization was carried out retrieval enhance diversity solutions. Compared with nine other exhaustive algorithms, superior terms time complexity convergence, showed higher solutions binary space, consolidated phase, significantly improved solution logic.

Language: Английский

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Particle swarm optimization tuned multi-headed long short-term memory networks approach for fuel prices forecasting

Journal of Network and Computer Applications, Journal Year: 2024, Volume and Issue: 233, P. 104048 - 104048

Published: Nov. 7, 2024

Language: Английский

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