AIMC Topic: Probability

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Pan-Logical Probabilistic Algorithms Based on Convolutional Neural Networks.

Computational intelligence and neuroscience Aug 10, 2022
A brand-new kind of flexible logic system called universal logic aims to address a variety of uncertain problems. In this study, the role of convolutional neural networks in assessing probabilistic pan-logic algorithms is investigated. A generic logi...
Neural Networks, Computer Algorithms Learning Probability Logic
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Spiking Neural Network Regularization With Fixed and Adaptive Drop-Keep Probabilities.

IEEE transactions on neural networks and learning systems Aug 3, 2022
Dropout and DropConnect are two techniques to facilitate the regularization of neural network models, having achieved the state-of-the-art results in several benchmarks. In this paper, to improve the generalization capability of spiking neural networ...
Neural Networks, Computer Algorithms Neurons Probability Generalization, Psychological
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Dynamically Weighted Balanced Loss: Class Imbalanced Learning and Confidence Calibration of Deep Neural Networks.

IEEE transactions on neural networks and learning systems Jul 6, 2022
Imbalanced class distribution is an inherent problem in many real-world classification tasks where the minority class is the class of interest. Many conventional statistical and machine learning classification algorithms are subject to frequency bias...
Neural Networks, Computer Machine Learning Algorithms Probability Calibration
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An Intelligent Fusion Model with Portfolio Selection and Machine Learning for Stock Market Prediction.

Computational intelligence and neuroscience Jun 23, 2022
Developing reliable equity market models allows investors to make more informed decisions. A trading model can reduce the risks associated with investment and allow traders to choose the best-paying stocks. However, stock market analysis is complicat...
Neural Networks, Computer Machine Learning Algorithms Intelligence Probability
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Remaining Useful Life Prediction Method for Bearings Based on LSTM with Uncertainty Quantification.

Sensors (Basel, Switzerland) Jun 16, 2022
To reduce the economic losses caused by bearing failures and prevent safety accidents, it is necessary to develop an effective method to predict the remaining useful life (RUL) of the rolling bearing. However, the degradation inside the bearing is di...
Neural Networks, Computer Probability Uncertainty
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Maximum entropy models provide functional connectivity estimates in neural networks.

Scientific reports Jun 10, 2022
Tools to estimate brain connectivity offer the potential to enhance our understanding of brain functioning. The behavior of neuronal networks, including functional connectivity and induced connectivity changes by external stimuli, can be studied usin...
Neural Networks, Computer Brain Models, Neurological Neurons Probability Nerve Net Entropy
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Security Evaluation of Financial and Insurance and Ruin Probability Analysis Integrating Deep Learning Models.

Computational intelligence and neuroscience Jun 8, 2022
To ensure safe development of the financial and insurance industry and promote the continuous growth of the social economy, the theory and its role of deep learning are firstly analyzed. Secondly, the security of financial and insurance and bankruptc...
Deep Learning Insurance Probability Bankruptcy
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Approximation properties of Gaussian-binary restricted Boltzmann machines and Gaussian-binary deep belief networks.

Neural networks : the official journal of the International Neural Network Society Jun 2, 2022
Despite the successful use of Gaussian-binary restricted Boltzmann machines (GB-RBMs) and Gaussian-binary deep belief networks (GB-DBNs), little is known about their theoretical approximation capabilities to represent distributions of continuous rand...
Neural Networks, Computer Computer Simulation Probability Normal Distribution
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Nonparametric estimation of the causal effect of a stochastic threshold-based intervention.

Biometrics May 26, 2022
Identifying a biomarker or treatment-dose threshold that marks a specified level of risk is an important problem, especially in clinical trials. In view of this goal, we consider a covariate-adjusted threshold-based interventional estimand, which hap...
Humans Machine Learning Causality Models, Statistical Bias Probability
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Analysis and Prevention and Control System of Domino Accident Risk Data in Chemical Parks Based on Topological Neural Network.

Computational intelligence and neuroscience May 26, 2022
A topologically based neural network algorithm is used to conduct an in-depth study and analysis of domino accident risk data in chemical parks, and this is used to construct a prevention and control system applied to the safety prediction of chemica...
Neural Networks, Computer Algorithms Reproducibility of Results Accidents Probability
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